mmiu.jsonl

{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_0_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_1_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_2_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_3_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_4_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_5_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_6_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_7_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_8_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_9_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_10_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_11_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_12_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_13_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_14_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_15_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_16_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_17_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_18_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_19_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_20_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_21_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_22_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_23_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_24_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_25_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_26_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_27_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_28_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_29_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_30_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_31_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_32_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_33_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_34_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_35_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_36_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_37_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_38_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_39_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_40_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_41_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_42_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_43_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_44_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_45_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_46_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_47_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_48_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_49_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_50_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_51_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_52_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_53_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_54_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_55_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_56_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_57_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_58_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_59_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_60_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_61_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_62_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_63_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_64_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_65_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_66_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_67_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_68_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_69_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_70_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_71_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_72_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_73_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_74_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_75_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_76_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_77_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_78_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_79_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_80_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_81_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_82_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_83_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_84_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_85_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_86_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_87_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_88_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_89_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_90_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_91_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_92_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_93_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_94_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_95_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_96_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_97_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_98_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_99_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_100_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_101_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_102_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_103_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_104_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_105_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_106_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_107_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_108_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_109_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_110_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_111_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_112_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_113_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_114_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_115_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_116_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_117_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_118_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_119_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_120_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_121_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_122_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_123_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_124_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_125_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_126_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_127_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_128_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_129_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_130_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_131_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_132_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_133_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_134_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_135_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_136_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_137_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_138_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_139_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_140_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_141_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_142_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_143_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_144_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_145_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_146_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_147_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_148_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_149_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_150_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_151_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_152_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_153_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_154_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_155_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_156_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_157_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_158_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_159_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_160_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_161_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_162_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_163_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_164_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_165_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_166_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_167_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_168_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_169_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_170_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_171_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_172_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_173_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_174_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_175_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_176_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_177_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_178_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_179_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_180_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_181_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_182_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_183_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_184_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_185_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_186_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_187_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_188_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_189_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_190_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_191_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "D"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_192_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_193_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "H"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_194_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "G"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_195_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "F"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_196_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "C"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_197_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "B"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_198_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "E"}, "task": "ravens_progressive_matrices"}
{"source": "RAVEN_10000", "options": "A: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image", "visual_input_component": ["synthetic image"], "input": {"input_image_path": ["2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_0.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_1.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_2.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_3.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_4.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_5.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_6.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_7.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_8.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_9.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_10.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_11.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_12.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_13.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_14.png", "2D-spatial/ravens_progressive_matrices/ravens_progressive_matrices_199_15.png"], "question": "Following the structural and analogical relations, which image best completes the problem matrix?", "context": "In the input images, the first 8 are the images from the question, and the last 8 are the images for the choices.Select from the following choices.\nA: The 9th image\nB: The 10th image\nC: The 11th image\nD: The 12th image\nE: The 13th image\nF: The 14th image\nG: The 15th image\nH: The 16th image"}, "output": {"output_text": "A"}, "task": "ravens_progressive_matrices"}
{"source": "Hpatches", "options": "A: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nB: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n\nC: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nD: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_0_0.png", "2D-spatial/Homography_estimation/Homography_estimation_0_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nB: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n\nC: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nD: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.91628 -0.19782 70.502\n0.072414 0.68419 -33.187\n5.7127e-06 -0.00025258 0.99947\n\nB: 1.2895 0.43518 -118.46\n-0.025956 1.4233 161.89\n-3.0413e-05 0.00069874 1.0013\n\nC: 4.3722 0.14407 -818.24\n-0.25209 3.9595 -549.15\n0.001718 0.0010825 0.97985\n\nD: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_1_0.png", "2D-spatial/Homography_estimation/Homography_estimation_1_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.91628 -0.19782 70.502\n0.072414 0.68419 -33.187\n5.7127e-06 -0.00025258 0.99947\n\nB: 1.2895 0.43518 -118.46\n-0.025956 1.4233 161.89\n-3.0413e-05 0.00069874 1.0013\n\nC: 4.3722 0.14407 -818.24\n-0.25209 3.9595 -549.15\n0.001718 0.0010825 0.97985\n\nD: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n\nB: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nC: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nD: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_2_0.png", "2D-spatial/Homography_estimation/Homography_estimation_2_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n\nB: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nC: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nD: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.9861 0.031586 27.893\n0.62141 1.9607 -531.99\n0.0011993 -1.9815e-05 0.99978\n\nB: -0.21679 -0.12572 585.55\n0.12463 -0.21699 355.1\n-1.085e-06 -1.8818e-06 1.0002\n\nC: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nD: 5.1051 0.34986 -885.86\n1.0306 5.9768 -2733.1\n0.0033649 0.00099216 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_3_0.png", "2D-spatial/Homography_estimation/Homography_estimation_3_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.9861 0.031586 27.893\n0.62141 1.9607 -531.99\n0.0011993 -1.9815e-05 0.99978\n\nB: -0.21679 -0.12572 585.55\n0.12463 -0.21699 355.1\n-1.085e-06 -1.8818e-06 1.0002\n\nC: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nD: 5.1051 0.34986 -885.86\n1.0306 5.9768 -2733.1\n0.0033649 0.00099216 1\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n\nB: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n\nC: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nD: 2.9599 0.00703 244.64\n0.78405 1.8789 -438.29\n0.0018411 4.4095e-05 0.99694\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_4_0.png", "2D-spatial/Homography_estimation/Homography_estimation_4_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n\nB: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n\nC: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nD: 2.9599 0.00703 244.64\n0.78405 1.8789 -438.29\n0.0018411 4.4095e-05 0.99694\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nB: 1.547 0.11677 155.75\n0.40373 1.373 -170.1\n0.00090791 8.8782e-05 1.0012\n\nC: 1.3951 0.13641 136.74\n0.31704 1.2758 -219.28\n0.00053511 0.00013896 0.99675\n\nD: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_5_0.png", "2D-spatial/Homography_estimation/Homography_estimation_5_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nB: 1.547 0.11677 155.75\n0.40373 1.373 -170.1\n0.00090791 8.8782e-05 1.0012\n\nC: 1.3951 0.13641 136.74\n0.31704 1.2758 -219.28\n0.00053511 0.00013896 0.99675\n\nD: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 0.012717 0.014394 193.52\n-0.12386 0.60301 126.7\n-0.00063953 7.9665e-05 1.0012\n\nC: 0.27317 0.041297 84.951\n-0.22859 0.68736 124.47\n-0.00041264 5.2763e-05 1.0003\n\nD: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_6_0.png", "2D-spatial/Homography_estimation/Homography_estimation_6_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 0.012717 0.014394 193.52\n-0.12386 0.60301 126.7\n-0.00063953 7.9665e-05 1.0012\n\nC: 0.27317 0.041297 84.951\n-0.22859 0.68736 124.47\n-0.00041264 5.2763e-05 1.0003\n\nD: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.60879 -0.35761 289.93\n0.34822 0.61653 -30.949\n-2.0912e-05 1.3527e-06 1.014\n\nB: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n\nC: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nD: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_7_0.png", "2D-spatial/Homography_estimation/Homography_estimation_7_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.60879 -0.35761 289.93\n0.34822 0.61653 -30.949\n-2.0912e-05 1.3527e-06 1.014\n\nB: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n\nC: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nD: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n\nB: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n\nC: 0.98278 -0.0048237 22.209\n-0.012055 0.97088 45.658\n-7.6753e-06 -2.3467e-05 1.0001\n\nD: 1.0499 0.025643 108.77\n0.19467 1.0054 -7.8895\n0.0011218 -3.184e-05 1.0021\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_8_0.png", "2D-spatial/Homography_estimation/Homography_estimation_8_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n\nB: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n\nC: 0.98278 -0.0048237 22.209\n-0.012055 0.97088 45.658\n-7.6753e-06 -2.3467e-05 1.0001\n\nD: 1.0499 0.025643 108.77\n0.19467 1.0054 -7.8895\n0.0011218 -3.184e-05 1.0021\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.3184 0.1614 32.607\n0.092973 1.2239 -454.36\n-0.00072537 0.00028453 0.99713\n\nB: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n\nC: 0.87235 0.023622 101.75\n0.12982 0.76075 59.456\n0.0005519 9.0915e-05 1.0016\n\nD: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_9_0.png", "2D-spatial/Homography_estimation/Homography_estimation_9_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.3184 0.1614 32.607\n0.092973 1.2239 -454.36\n-0.00072537 0.00028453 0.99713\n\nB: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n\nC: 0.87235 0.023622 101.75\n0.12982 0.76075 59.456\n0.0005519 9.0915e-05 1.0016\n\nD: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nB: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n\nC: 0.15114 -0.00089399 241.66\n-0.078633 0.45918 14.453\n-0.00033245 3.1152e-05 0.99996\n\nD: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_10_0.png", "2D-spatial/Homography_estimation/Homography_estimation_10_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nB: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n\nC: 0.15114 -0.00089399 241.66\n-0.078633 0.45918 14.453\n-0.00033245 3.1152e-05 0.99996\n\nD: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nC: 1.1198 0.031669 158.94\n0.13747 0.986 -24.458\n0.00036259 4.1267e-05 0.99658\n\nD: 0.52949 -0.028655 46.849\n-0.2451 0.79991 158.44\n-0.00032499 -1.8164e-05 0.99959\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_11_0.png", "2D-spatial/Homography_estimation/Homography_estimation_11_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nC: 1.1198 0.031669 158.94\n0.13747 0.986 -24.458\n0.00036259 4.1267e-05 0.99658\n\nD: 0.52949 -0.028655 46.849\n-0.2451 0.79991 158.44\n-0.00032499 -1.8164e-05 0.99959\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nB: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nC: 0.57079 0.0076829 -45.295\n-0.15447 0.93183 62.276\n-0.00028402 -5.8827e-06 0.99996\n\nD: 0.77105 -0.097833 -3.6994\n-0.092675 0.81167 92.799\n-0.0001392 -0.00012806 0.99964\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_12_0.png", "2D-spatial/Homography_estimation/Homography_estimation_12_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nB: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nC: 0.57079 0.0076829 -45.295\n-0.15447 0.93183 62.276\n-0.00028402 -5.8827e-06 0.99996\n\nD: 0.77105 -0.097833 -3.6994\n-0.092675 0.81167 92.799\n-0.0001392 -0.00012806 0.99964\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.6481 0.070248 -423.11\n0.5002 2.6605 -906.39\n0.0012014 0.00025943 0.99533\n\nB: 0.58099 -0.029382 -20.47\n-0.29479 0.73128 188.62\n-0.00043803 -4.3076e-05 1.0007\n\nC: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n\nD: 0.38854 -0.073106 92.576\n-0.1986 0.7319 139.21\n-0.00040811 -1.555e-05 0.99988\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_13_0.png", "2D-spatial/Homography_estimation/Homography_estimation_13_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.6481 0.070248 -423.11\n0.5002 2.6605 -906.39\n0.0012014 0.00025943 0.99533\n\nB: 0.58099 -0.029382 -20.47\n-0.29479 0.73128 188.62\n-0.00043803 -4.3076e-05 1.0007\n\nC: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n\nD: 0.38854 -0.073106 92.576\n-0.1986 0.7319 139.21\n-0.00040811 -1.555e-05 0.99988\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n\nB: 1.141 -0.024147 186.42\n0.29573 0.97376 -60.872\n0.00082251 -1.0843e-05 0.99973\n\nC: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n\nD: 1.3308 -0.060097 223.54\n0.17906 0.94189 -10.999\n0.00034146 -4.4675e-05 0.99983\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_14_0.png", "2D-spatial/Homography_estimation/Homography_estimation_14_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n\nB: 1.141 -0.024147 186.42\n0.29573 0.97376 -60.872\n0.00082251 -1.0843e-05 0.99973\n\nC: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n\nD: 1.3308 -0.060097 223.54\n0.17906 0.94189 -10.999\n0.00034146 -4.4675e-05 0.99983\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n\nB: 0.3184 0.1614 32.607\n0.092973 1.2239 -454.36\n-0.00072537 0.00028453 0.99713\n\nC: 0.47208 0.021042 63.836\n-0.16332 0.73028 126.94\n-0.00030371 2.4606e-05 0.99981\n\nD: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_15_0.png", "2D-spatial/Homography_estimation/Homography_estimation_15_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n\nB: 0.3184 0.1614 32.607\n0.092973 1.2239 -454.36\n-0.00072537 0.00028453 0.99713\n\nC: 0.47208 0.021042 63.836\n-0.16332 0.73028 126.94\n-0.00030371 2.4606e-05 0.99981\n\nD: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nB: 0.7088 -0.010965 -26.07\n-0.13602 0.83489 103.19\n-0.00023352 -1.5615e-05 1.0004\n\nC: 0.54304 0.026384 236.48\n-0.041921 0.64806 87.13\n-5.8662e-05 1.5685e-05 1\n\nD: 0.37107 -0.09213 318.73\n0.086334 0.37505 188.02\n-1.0814e-05 -3.6548e-06 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_16_0.png", "2D-spatial/Homography_estimation/Homography_estimation_16_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nB: 0.7088 -0.010965 -26.07\n-0.13602 0.83489 103.19\n-0.00023352 -1.5615e-05 1.0004\n\nC: 0.54304 0.026384 236.48\n-0.041921 0.64806 87.13\n-5.8662e-05 1.5685e-05 1\n\nD: 0.37107 -0.09213 318.73\n0.086334 0.37505 188.02\n-1.0814e-05 -3.6548e-06 1\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.77105 -0.097833 -3.6994\n-0.092675 0.81167 92.799\n-0.0001392 -0.00012806 0.99964\n\nB: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nC: 0.13896 0.020204 194.37\n-0.25201 0.63798 118.99\n-0.00052359 2.2762e-05 0.9996\n\nD: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_17_0.png", "2D-spatial/Homography_estimation/Homography_estimation_17_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.77105 -0.097833 -3.6994\n-0.092675 0.81167 92.799\n-0.0001392 -0.00012806 0.99964\n\nB: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nC: 0.13896 0.020204 194.37\n-0.25201 0.63798 118.99\n-0.00052359 2.2762e-05 0.9996\n\nD: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n\nB: 0.45287 0.0061881 100.32\n-0.053734 0.66556 61.961\n-0.00023168 -5.8559e-06 1.0005\n\nC: 1.0478 0.035143 64.843\n0.063507 1.0349 21.701\n0.00023044 -6.878e-06 0.99998\n\nD: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_18_0.png", "2D-spatial/Homography_estimation/Homography_estimation_18_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n\nB: 0.45287 0.0061881 100.32\n-0.053734 0.66556 61.961\n-0.00023168 -5.8559e-06 1.0005\n\nC: 1.0478 0.035143 64.843\n0.063507 1.0349 21.701\n0.00023044 -6.878e-06 0.99998\n\nD: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.55616 0.0088234 83.342\n-0.19782 0.70845 195.76\n-0.00029305 -3.175e-05 0.99884\n\nB: 0.85799 0.21669 9.4839\n-0.21177 0.85855 130.48\n1.5015e-06 9.2033e-07 1\n\nC: 5.1051 0.34986 -885.86\n1.0306 5.9768 -2733.1\n0.0033649 0.00099216 1\n\nD: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_19_0.png", "2D-spatial/Homography_estimation/Homography_estimation_19_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.55616 0.0088234 83.342\n-0.19782 0.70845 195.76\n-0.00029305 -3.175e-05 0.99884\n\nB: 0.85799 0.21669 9.4839\n-0.21177 0.85855 130.48\n1.5015e-06 9.2033e-07 1\n\nC: 5.1051 0.34986 -885.86\n1.0306 5.9768 -2733.1\n0.0033649 0.00099216 1\n\nD: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.032608 0.010774 198.34\n-0.16134 0.44659 114.31\n-0.00057725 -5.1566e-07 1.0017\n\nB: 0.18178 0.033268 82.883\n-0.24959 0.68306 123.62\n-0.0004688 5.3047e-05 1.0005\n\nC: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nD: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_20_0.png", "2D-spatial/Homography_estimation/Homography_estimation_20_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.032608 0.010774 198.34\n-0.16134 0.44659 114.31\n-0.00057725 -5.1566e-07 1.0017\n\nB: 0.18178 0.033268 82.883\n-0.24959 0.68306 123.62\n-0.0004688 5.3047e-05 1.0005\n\nC: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nD: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nB: 0.20876 0.015221 174.06\n-0.13382 0.55012 11.64\n-0.00044084 3.575e-05 1.0177\n\nC: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n\nD: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_21_0.png", "2D-spatial/Homography_estimation/Homography_estimation_21_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nB: 0.20876 0.015221 174.06\n-0.13382 0.55012 11.64\n-0.00044084 3.575e-05 1.0177\n\nC: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n\nD: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n\nB: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nC: 0.012717 0.014394 193.52\n-0.12386 0.60301 126.7\n-0.00063953 7.9665e-05 1.0012\n\nD: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_22_0.png", "2D-spatial/Homography_estimation/Homography_estimation_22_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n\nB: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nC: 0.012717 0.014394 193.52\n-0.12386 0.60301 126.7\n-0.00063953 7.9665e-05 1.0012\n\nD: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.8851 0.028166 274.85\n0.48185 1.6951 -326.97\n0.0011778 8.455e-05 0.99801\n\nB: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nC: 0.98278 -0.0048237 22.209\n-0.012055 0.97088 45.658\n-7.6753e-06 -2.3467e-05 1.0001\n\nD: 1.6284 1.0346 -954.33\n-0.096789 2.5434 -782.98\n-0.00078653 0.0011044 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_23_0.png", "2D-spatial/Homography_estimation/Homography_estimation_23_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.8851 0.028166 274.85\n0.48185 1.6951 -326.97\n0.0011778 8.455e-05 0.99801\n\nB: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nC: 0.98278 -0.0048237 22.209\n-0.012055 0.97088 45.658\n-7.6753e-06 -2.3467e-05 1.0001\n\nD: 1.6284 1.0346 -954.33\n-0.096789 2.5434 -782.98\n-0.00078653 0.0011044 1\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.9207 0.17258 153.68\n0.62581 1.7293 -542.33\n0.0010509 0.0001244 0.99848\n\nB: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nC: 0.91628 -0.19782 70.502\n0.072414 0.68419 -33.187\n5.7127e-06 -0.00025258 0.99947\n\nD: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_24_0.png", "2D-spatial/Homography_estimation/Homography_estimation_24_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.9207 0.17258 153.68\n0.62581 1.7293 -542.33\n0.0010509 0.0001244 0.99848\n\nB: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nC: 0.91628 -0.19782 70.502\n0.072414 0.68419 -33.187\n5.7127e-06 -0.00025258 0.99947\n\nD: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nB: 0.18178 0.033268 82.883\n-0.24959 0.68306 123.62\n-0.0004688 5.3047e-05 1.0005\n\nC: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nD: 1.0499 0.025643 108.77\n0.19467 1.0054 -7.8895\n0.0011218 -3.184e-05 1.0021\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_25_0.png", "2D-spatial/Homography_estimation/Homography_estimation_25_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nB: 0.18178 0.033268 82.883\n-0.24959 0.68306 123.62\n-0.0004688 5.3047e-05 1.0005\n\nC: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nD: 1.0499 0.025643 108.77\n0.19467 1.0054 -7.8895\n0.0011218 -3.184e-05 1.0021\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.38914 0.285 169.51\n-0.28531 0.39347 340.1\n-6.4617e-06 5.0341e-06 1\n\nB: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nC: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nD: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_26_0.png", "2D-spatial/Homography_estimation/Homography_estimation_26_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.38914 0.285 169.51\n-0.28531 0.39347 340.1\n-6.4617e-06 5.0341e-06 1\n\nB: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nC: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nD: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nB: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n\nC: 0.57125 -0.095863 127.19\n0.050302 0.75099 -13.911\n-0.00020485 1.2421e-06 0.9999\n\nD: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_27_0.png", "2D-spatial/Homography_estimation/Homography_estimation_27_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nB: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n\nC: 0.57125 -0.095863 127.19\n0.050302 0.75099 -13.911\n-0.00020485 1.2421e-06 0.9999\n\nD: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.6996 0.02142 200.09\n0.31149 1.4251 -246.25\n0.00053609 -6.8541e-05 0.99889\n\nB: 0.4591 -0.47767 436.55\n0.46479 0.46941 -27.514\n-2.7182e-05 -1.2668e-06 1.0191\n\nC: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nD: 1.0499 0.025643 108.77\n0.19467 1.0054 -7.8895\n0.0011218 -3.184e-05 1.0021\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_28_0.png", "2D-spatial/Homography_estimation/Homography_estimation_28_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.6996 0.02142 200.09\n0.31149 1.4251 -246.25\n0.00053609 -6.8541e-05 0.99889\n\nB: 0.4591 -0.47767 436.55\n0.46479 0.46941 -27.514\n-2.7182e-05 -1.2668e-06 1.0191\n\nC: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nD: 1.0499 0.025643 108.77\n0.19467 1.0054 -7.8895\n0.0011218 -3.184e-05 1.0021\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.13896 0.020204 194.37\n-0.25201 0.63798 118.99\n-0.00052359 2.2762e-05 0.9996\n\nB: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nC: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nD: 0.30367 0.12862 200.05\n-0.12888 0.30356 134.47\n2.6855e-07 -3.4026e-07 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_29_0.png", "2D-spatial/Homography_estimation/Homography_estimation_29_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.13896 0.020204 194.37\n-0.25201 0.63798 118.99\n-0.00052359 2.2762e-05 0.9996\n\nB: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nC: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nD: 0.30367 0.12862 200.05\n-0.12888 0.30356 134.47\n2.6855e-07 -3.4026e-07 1\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nB: 0.10472 0.069057 99.841\n-0.17731 0.5329 107.18\n-0.00051255 -1.3734e-05 0.98616\n\nC: 0.76922 -0.28498 222.68\n0.33855 1.0341 -81.069\n0.00035349 1.2014e-05 0.99834\n\nD: -0.47246 -0.28359 869.57\n0.29041 -0.47016 396.67\n5.0949e-06 1.2499e-05 0.99998\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_30_0.png", "2D-spatial/Homography_estimation/Homography_estimation_30_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nB: 0.10472 0.069057 99.841\n-0.17731 0.5329 107.18\n-0.00051255 -1.3734e-05 0.98616\n\nC: 0.76922 -0.28498 222.68\n0.33855 1.0341 -81.069\n0.00035349 1.2014e-05 0.99834\n\nD: -0.47246 -0.28359 869.57\n0.29041 -0.47016 396.67\n5.0949e-06 1.2499e-05 0.99998\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.1198 0.031669 158.94\n0.13747 0.986 -24.458\n0.00036259 4.1267e-05 0.99658\n\nB: 1.3522 0.025037 96.693\n0.20588 1.5085 -279.44\n0.000418 4.2466e-05 1.0103\n\nC: 0.040904 -0.0023332 234.76\n-0.10713 0.35038 218.5\n-0.00028907 6.311e-06 1.0035\n\nD: 0.38266 -0.33125 122.6\n-0.21363 0.61581 225.35\n-0.00034121 -7.7515e-06 0.99865\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_31_0.png", "2D-spatial/Homography_estimation/Homography_estimation_31_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.1198 0.031669 158.94\n0.13747 0.986 -24.458\n0.00036259 4.1267e-05 0.99658\n\nB: 1.3522 0.025037 96.693\n0.20588 1.5085 -279.44\n0.000418 4.2466e-05 1.0103\n\nC: 0.040904 -0.0023332 234.76\n-0.10713 0.35038 218.5\n-0.00028907 6.311e-06 1.0035\n\nD: 0.38266 -0.33125 122.6\n-0.21363 0.61581 225.35\n-0.00034121 -7.7515e-06 0.99865\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.15114 -0.00089399 241.66\n-0.078633 0.45918 14.453\n-0.00033245 3.1152e-05 0.99996\n\nB: 0.87235 0.023622 101.75\n0.12982 0.76075 59.456\n0.0005519 9.0915e-05 1.0016\n\nC: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nD: 0.084461 -0.022036 252.3\n-0.21 0.51325 245.38\n-0.000447 -2.621e-05 1.0009\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_32_0.png", "2D-spatial/Homography_estimation/Homography_estimation_32_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.15114 -0.00089399 241.66\n-0.078633 0.45918 14.453\n-0.00033245 3.1152e-05 0.99996\n\nB: 0.87235 0.023622 101.75\n0.12982 0.76075 59.456\n0.0005519 9.0915e-05 1.0016\n\nC: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nD: 0.084461 -0.022036 252.3\n-0.21 0.51325 245.38\n-0.000447 -2.621e-05 1.0009\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n\nB: 0.62091 -0.030805 57.622\n-0.22703 0.84222 -13.023\n-0.00037179 -4.2767e-05 0.99852\n\nC: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n\nD: 0.056448 -0.012851 135.19\n-0.38625 0.54689 255.61\n-0.00066718 5.392e-05 1.0012\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_33_0.png", "2D-spatial/Homography_estimation/Homography_estimation_33_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n\nB: 0.62091 -0.030805 57.622\n-0.22703 0.84222 -13.023\n-0.00037179 -4.2767e-05 0.99852\n\nC: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n\nD: 0.056448 -0.012851 135.19\n-0.38625 0.54689 255.61\n-0.00066718 5.392e-05 1.0012\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n\nB: 1.4862 -0.061679 54.577\n0.4606 1.2816 -147.5\n0.0007321 -7.3842e-05 0.99895\n\nC: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nD: 1.3522 0.025037 96.693\n0.20588 1.5085 -279.44\n0.000418 4.2466e-05 1.0103\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_34_0.png", "2D-spatial/Homography_estimation/Homography_estimation_34_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n\nB: 1.4862 -0.061679 54.577\n0.4606 1.2816 -147.5\n0.0007321 -7.3842e-05 0.99895\n\nC: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nD: 1.3522 0.025037 96.693\n0.20588 1.5085 -279.44\n0.000418 4.2466e-05 1.0103\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.3794 0.089822 49.168\n-0.27745 0.88349 -5.6379\n-0.00046319 5.6849e-05 0.99886\n\nB: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nC: 1.0478 0.035143 64.843\n0.063507 1.0349 21.701\n0.00023044 -6.878e-06 0.99998\n\nD: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_35_0.png", "2D-spatial/Homography_estimation/Homography_estimation_35_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.3794 0.089822 49.168\n-0.27745 0.88349 -5.6379\n-0.00046319 5.6849e-05 0.99886\n\nB: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nC: 1.0478 0.035143 64.843\n0.063507 1.0349 21.701\n0.00023044 -6.878e-06 0.99998\n\nD: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nB: 0.4591 -0.47767 436.55\n0.46479 0.46941 -27.514\n-2.7182e-05 -1.2668e-06 1.0191\n\nC: 0.83129 0.00294 81.765\n-0.011403 0.83158 63.28\n-7.0021e-06 -1.5701e-05 1\n\nD: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_36_0.png", "2D-spatial/Homography_estimation/Homography_estimation_36_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nB: 0.4591 -0.47767 436.55\n0.46479 0.46941 -27.514\n-2.7182e-05 -1.2668e-06 1.0191\n\nC: 0.83129 0.00294 81.765\n-0.011403 0.83158 63.28\n-7.0021e-06 -1.5701e-05 1\n\nD: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nC: 0.17608 -0.024321 273.19\n-0.19809 0.7405 74.826\n-0.00053318 1.2457e-05 1.0069\n\nD: 1.0063 -0.0054085 288.55\n0.23295 0.84053 7.8206\n0.0005941 1.4583e-05 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_37_0.png", "2D-spatial/Homography_estimation/Homography_estimation_37_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nC: 0.17608 -0.024321 273.19\n-0.19809 0.7405 74.826\n-0.00053318 1.2457e-05 1.0069\n\nD: 1.0063 -0.0054085 288.55\n0.23295 0.84053 7.8206\n0.0005941 1.4583e-05 1.0001\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n\nB: 0.87235 0.023622 101.75\n0.12982 0.76075 59.456\n0.0005519 9.0915e-05 1.0016\n\nC: 1.1198 0.031669 158.94\n0.13747 0.986 -24.458\n0.00036259 4.1267e-05 0.99658\n\nD: 0.29534 0.035751 -56.21\n-0.35718 0.5432 233.53\n-0.00064211 -1.1093e-05 0.97783\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_38_0.png", "2D-spatial/Homography_estimation/Homography_estimation_38_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n\nB: 0.87235 0.023622 101.75\n0.12982 0.76075 59.456\n0.0005519 9.0915e-05 1.0016\n\nC: 1.1198 0.031669 158.94\n0.13747 0.986 -24.458\n0.00036259 4.1267e-05 0.99658\n\nD: 0.29534 0.035751 -56.21\n-0.35718 0.5432 233.53\n-0.00064211 -1.1093e-05 0.97783\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.056448 -0.012851 135.19\n-0.38625 0.54689 255.61\n-0.00066718 5.392e-05 1.0012\n\nB: 1.6996 0.02142 200.09\n0.31149 1.4251 -246.25\n0.00053609 -6.8541e-05 0.99889\n\nC: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n\nD: 0.1268 -0.03963 330.5\n-0.1892 0.46973 254.2\n-0.00039857 -3.9641e-05 0.99971\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_39_0.png", "2D-spatial/Homography_estimation/Homography_estimation_39_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.056448 -0.012851 135.19\n-0.38625 0.54689 255.61\n-0.00066718 5.392e-05 1.0012\n\nB: 1.6996 0.02142 200.09\n0.31149 1.4251 -246.25\n0.00053609 -6.8541e-05 0.99889\n\nC: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n\nD: 0.1268 -0.03963 330.5\n-0.1892 0.46973 254.2\n-0.00039857 -3.9641e-05 0.99971\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.441 -0.037212 269.33\n0.73295 1.6438 -380.65\n0.0014226 4.1601e-05 1.0102\n\nB: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nC: 0.30367 0.12862 200.05\n-0.12888 0.30356 134.47\n2.6855e-07 -3.4026e-07 1\n\nD: 0.28973 0.014397 100.07\n-0.29955 0.64174 168.27\n-0.00067332 7.239e-06 1.0017\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_40_0.png", "2D-spatial/Homography_estimation/Homography_estimation_40_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.441 -0.037212 269.33\n0.73295 1.6438 -380.65\n0.0014226 4.1601e-05 1.0102\n\nB: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nC: 0.30367 0.12862 200.05\n-0.12888 0.30356 134.47\n2.6855e-07 -3.4026e-07 1\n\nD: 0.28973 0.014397 100.07\n-0.29955 0.64174 168.27\n-0.00067332 7.239e-06 1.0017\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.012717 0.014394 193.52\n-0.12386 0.60301 126.7\n-0.00063953 7.9665e-05 1.0012\n\nB: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nC: 1.5134 -0.0029581 20.934\n0.2678 1.4062 -232.68\n0.00048583 -4.0311e-06 1.0006\n\nD: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_41_0.png", "2D-spatial/Homography_estimation/Homography_estimation_41_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.012717 0.014394 193.52\n-0.12386 0.60301 126.7\n-0.00063953 7.9665e-05 1.0012\n\nB: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nC: 1.5134 -0.0029581 20.934\n0.2678 1.4062 -232.68\n0.00048583 -4.0311e-06 1.0006\n\nD: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.18178 0.033268 82.883\n-0.24959 0.68306 123.62\n-0.0004688 5.3047e-05 1.0005\n\nB: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nC: 0.62147 0.055609 221.79\n0.21978 1.1561 -23.942\n0.00048557 -4.4311e-05 0.99866\n\nD: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_42_0.png", "2D-spatial/Homography_estimation/Homography_estimation_42_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.18178 0.033268 82.883\n-0.24959 0.68306 123.62\n-0.0004688 5.3047e-05 1.0005\n\nB: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nC: 0.62147 0.055609 221.79\n0.21978 1.1561 -23.942\n0.00048557 -4.4311e-05 0.99866\n\nD: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nB: 0.81883 -0.28544 161.88\n0.010536 0.53499 62.327\n1.3163e-05 -0.00056443 1.0014\n\nC: 0.54304 0.026384 236.48\n-0.041921 0.64806 87.13\n-5.8662e-05 1.5685e-05 1\n\nD: 0.14705 0.061323 72.893\n-0.27582 0.69094 109.44\n-0.00056993 1.3825e-06 0.9981\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_43_0.png", "2D-spatial/Homography_estimation/Homography_estimation_43_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nB: 0.81883 -0.28544 161.88\n0.010536 0.53499 62.327\n1.3163e-05 -0.00056443 1.0014\n\nC: 0.54304 0.026384 236.48\n-0.041921 0.64806 87.13\n-5.8662e-05 1.5685e-05 1\n\nD: 0.14705 0.061323 72.893\n-0.27582 0.69094 109.44\n-0.00056993 1.3825e-06 0.9981\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.41873 -0.043533 -18.562\n-0.27021 0.88041 53.791\n-0.00050299 -2.2546e-05 0.99941\n\nB: 0.63669 0.0018872 137.9\n-0.00033285 0.63926 95.922\n-2.0441e-06 4.1104e-06 1\n\nC: 1.1884 0.015274 95.776\n0.23282 1.0681 -20.551\n0.00097623 0.00015903 1.0014\n\nD: 0.3794 0.089822 49.168\n-0.27745 0.88349 -5.6379\n-0.00046319 5.6849e-05 0.99886\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_44_0.png", "2D-spatial/Homography_estimation/Homography_estimation_44_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.41873 -0.043533 -18.562\n-0.27021 0.88041 53.791\n-0.00050299 -2.2546e-05 0.99941\n\nB: 0.63669 0.0018872 137.9\n-0.00033285 0.63926 95.922\n-2.0441e-06 4.1104e-06 1\n\nC: 1.1884 0.015274 95.776\n0.23282 1.0681 -20.551\n0.00097623 0.00015903 1.0014\n\nD: 0.3794 0.089822 49.168\n-0.27745 0.88349 -5.6379\n-0.00046319 5.6849e-05 0.99886\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.1268 -0.03963 330.5\n-0.1892 0.46973 254.2\n-0.00039857 -3.9641e-05 0.99971\n\nB: 4.3722 0.14407 -818.24\n-0.25209 3.9595 -549.15\n0.001718 0.0010825 0.97985\n\nC: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nD: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_45_0.png", "2D-spatial/Homography_estimation/Homography_estimation_45_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.1268 -0.03963 330.5\n-0.1892 0.46973 254.2\n-0.00039857 -3.9641e-05 0.99971\n\nB: 4.3722 0.14407 -818.24\n-0.25209 3.9595 -549.15\n0.001718 0.0010825 0.97985\n\nC: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nD: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nB: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n\nC: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nD: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_46_0.png", "2D-spatial/Homography_estimation/Homography_estimation_46_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nB: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n\nC: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nD: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nB: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nC: 1.0505 -0.0053825 276.45\n0.20631 0.92888 48.832\n0.00048841 -1.9251e-05 0.99878\n\nD: 0.62147 0.055609 221.79\n0.21978 1.1561 -23.942\n0.00048557 -4.4311e-05 0.99866\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_47_0.png", "2D-spatial/Homography_estimation/Homography_estimation_47_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nB: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nC: 1.0505 -0.0053825 276.45\n0.20631 0.92888 48.832\n0.00048841 -1.9251e-05 0.99878\n\nD: 0.62147 0.055609 221.79\n0.21978 1.1561 -23.942\n0.00048557 -4.4311e-05 0.99866\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nB: 2.5614 0.083075 163.07\n0.94137 2.2586 -732.08\n0.0017783 2.1603e-05 0.99316\n\nC: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nD: 0.15114 -0.00089399 241.66\n-0.078633 0.45918 14.453\n-0.00033245 3.1152e-05 0.99996\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_48_0.png", "2D-spatial/Homography_estimation/Homography_estimation_48_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nB: 2.5614 0.083075 163.07\n0.94137 2.2586 -732.08\n0.0017783 2.1603e-05 0.99316\n\nC: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nD: 0.15114 -0.00089399 241.66\n-0.078633 0.45918 14.453\n-0.00033245 3.1152e-05 0.99996\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.7088 -0.010965 -26.07\n-0.13602 0.83489 103.19\n-0.00023352 -1.5615e-05 1.0004\n\nB: 3.1418 0.21701 -576.91\n0.129 3.5039 -1062.5\n0.0014143 0.00082533 0.98844\n\nC: 0.70161 0.023304 -1.9207\n-0.10366 0.81239 71.251\n-0.00023167 -1.5062e-05 0.99976\n\nD: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_49_0.png", "2D-spatial/Homography_estimation/Homography_estimation_49_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.7088 -0.010965 -26.07\n-0.13602 0.83489 103.19\n-0.00023352 -1.5615e-05 1.0004\n\nB: 3.1418 0.21701 -576.91\n0.129 3.5039 -1062.5\n0.0014143 0.00082533 0.98844\n\nC: 0.70161 0.023304 -1.9207\n-0.10366 0.81239 71.251\n-0.00023167 -1.5062e-05 0.99976\n\nD: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.35568 0.079611 -21.49\n-0.17793 0.7199 62.24\n-0.00050458 1.9913e-05 0.9982\n\nB: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nC: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nD: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_50_0.png", "2D-spatial/Homography_estimation/Homography_estimation_50_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.35568 0.079611 -21.49\n-0.17793 0.7199 62.24\n-0.00050458 1.9913e-05 0.9982\n\nB: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nC: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nD: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nB: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n\nC: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nD: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_51_0.png", "2D-spatial/Homography_estimation/Homography_estimation_51_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nB: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n\nC: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nD: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nB: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nC: 0.42945 0.0071566 96.266\n-0.019537 0.48377 43.049\n-7.8698e-05 1.6013e-05 1.0001\n\nD: 0.70161 0.023304 -1.9207\n-0.10366 0.81239 71.251\n-0.00023167 -1.5062e-05 0.99976\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_52_0.png", "2D-spatial/Homography_estimation/Homography_estimation_52_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nB: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nC: 0.42945 0.0071566 96.266\n-0.019537 0.48377 43.049\n-7.8698e-05 1.6013e-05 1.0001\n\nD: 0.70161 0.023304 -1.9207\n-0.10366 0.81239 71.251\n-0.00023167 -1.5062e-05 0.99976\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nB: 0.73597 -0.0032436 13.11\n0.017092 0.71039 36.002\n5.8878e-05 -9.3828e-06 0.99995\n\nC: 1.0478 0.035143 64.843\n0.063507 1.0349 21.701\n0.00023044 -6.878e-06 0.99998\n\nD: 0.52949 -0.028655 46.849\n-0.2451 0.79991 158.44\n-0.00032499 -1.8164e-05 0.99959\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_53_0.png", "2D-spatial/Homography_estimation/Homography_estimation_53_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nB: 0.73597 -0.0032436 13.11\n0.017092 0.71039 36.002\n5.8878e-05 -9.3828e-06 0.99995\n\nC: 1.0478 0.035143 64.843\n0.063507 1.0349 21.701\n0.00023044 -6.878e-06 0.99998\n\nD: 0.52949 -0.028655 46.849\n-0.2451 0.79991 158.44\n-0.00032499 -1.8164e-05 0.99959\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.32788 -0.00026656 168.52\n-0.087696 0.49289 72.043\n-0.00025798 4.6006e-06 0.9984\n\nB: 0.84581 -0.039469 34.117\n-0.067529 0.81703 142.37\n-0.00011408 -0.00014793 1.0014\n\nC: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n\nD: 0.38914 0.285 169.51\n-0.28531 0.39347 340.1\n-6.4617e-06 5.0341e-06 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_54_0.png", "2D-spatial/Homography_estimation/Homography_estimation_54_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.32788 -0.00026656 168.52\n-0.087696 0.49289 72.043\n-0.00025798 4.6006e-06 0.9984\n\nB: 0.84581 -0.039469 34.117\n-0.067529 0.81703 142.37\n-0.00011408 -0.00014793 1.0014\n\nC: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n\nD: 0.38914 0.285 169.51\n-0.28531 0.39347 340.1\n-6.4617e-06 5.0341e-06 1\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.441 -0.037212 269.33\n0.73295 1.6438 -380.65\n0.0014226 4.1601e-05 1.0102\n\nB: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n\nC: 2.6177 0.042575 -65.797\n0.74359 2.3954 -903.27\n0.0018892 8.2816e-05 0.98996\n\nD: 0.27317 0.041297 84.951\n-0.22859 0.68736 124.47\n-0.00041264 5.2763e-05 1.0003\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_55_0.png", "2D-spatial/Homography_estimation/Homography_estimation_55_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.441 -0.037212 269.33\n0.73295 1.6438 -380.65\n0.0014226 4.1601e-05 1.0102\n\nB: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n\nC: 2.6177 0.042575 -65.797\n0.74359 2.3954 -903.27\n0.0018892 8.2816e-05 0.98996\n\nD: 0.27317 0.041297 84.951\n-0.22859 0.68736 124.47\n-0.00041264 5.2763e-05 1.0003\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.85799 0.21669 9.4839\n-0.21177 0.85855 130.48\n1.5015e-06 9.2033e-07 1\n\nB: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nC: 0.17608 -0.024321 273.19\n-0.19809 0.7405 74.826\n-0.00053318 1.2457e-05 1.0069\n\nD: 2.3594 0.0026252 -116.05\n0.5085 2.302 -550.96\n0.0013826 0.0001837 1.0004\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_56_0.png", "2D-spatial/Homography_estimation/Homography_estimation_56_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.85799 0.21669 9.4839\n-0.21177 0.85855 130.48\n1.5015e-06 9.2033e-07 1\n\nB: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nC: 0.17608 -0.024321 273.19\n-0.19809 0.7405 74.826\n-0.00053318 1.2457e-05 1.0069\n\nD: 2.3594 0.0026252 -116.05\n0.5085 2.302 -550.96\n0.0013826 0.0001837 1.0004\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nB: 2.1479 0.036813 206.94\n0.67819 1.8174 -485.8\n0.0012074 -6.8043e-06 0.99599\n\nC: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nD: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_57_0.png", "2D-spatial/Homography_estimation/Homography_estimation_57_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nB: 2.1479 0.036813 206.94\n0.67819 1.8174 -485.8\n0.0012074 -6.8043e-06 0.99599\n\nC: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nD: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.2564 0.092521 94.187\n-0.28031 0.83589 -0.15652\n-0.00048968 6.0866e-05 1.0015\n\nB: 2.6481 0.070248 -423.11\n0.5002 2.6605 -906.39\n0.0012014 0.00025943 0.99533\n\nC: 1.0035 -0.00055314 2.5255\n-0.0028717 1.0087 -9.7285\n-3.8783e-06 3.4244e-06 1\n\nD: 2.5614 0.083075 163.07\n0.94137 2.2586 -732.08\n0.0017783 2.1603e-05 0.99316\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_58_0.png", "2D-spatial/Homography_estimation/Homography_estimation_58_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.2564 0.092521 94.187\n-0.28031 0.83589 -0.15652\n-0.00048968 6.0866e-05 1.0015\n\nB: 2.6481 0.070248 -423.11\n0.5002 2.6605 -906.39\n0.0012014 0.00025943 0.99533\n\nC: 1.0035 -0.00055314 2.5255\n-0.0028717 1.0087 -9.7285\n-3.8783e-06 3.4244e-06 1\n\nD: 2.5614 0.083075 163.07\n0.94137 2.2586 -732.08\n0.0017783 2.1603e-05 0.99316\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n\nB: 1.4219 0.01866 342.44\n0.36005 1.3261 -141.73\n0.00090969 2.3838e-05 1.0002\n\nC: 1.9861 0.031586 27.893\n0.62141 1.9607 -531.99\n0.0011993 -1.9815e-05 0.99978\n\nD: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_59_0.png", "2D-spatial/Homography_estimation/Homography_estimation_59_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n\nB: 1.4219 0.01866 342.44\n0.36005 1.3261 -141.73\n0.00090969 2.3838e-05 1.0002\n\nC: 1.9861 0.031586 27.893\n0.62141 1.9607 -531.99\n0.0011993 -1.9815e-05 0.99978\n\nD: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nB: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nC: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nD: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_60_0.png", "2D-spatial/Homography_estimation/Homography_estimation_60_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nB: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nC: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nD: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n\nB: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nC: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nD: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_61_0.png", "2D-spatial/Homography_estimation/Homography_estimation_61_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n\nB: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nC: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nD: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nB: 0.2024 0.0033266 96.15\n-0.28093 0.65512 201.73\n-0.00049784 1.8106e-06 1.0048\n\nC: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n\nD: 0.48531 0.10549 -95.005\n-0.11843 0.77202 44.217\n-0.00029301 2.8434e-05 0.99773\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_62_0.png", "2D-spatial/Homography_estimation/Homography_estimation_62_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nB: 0.2024 0.0033266 96.15\n-0.28093 0.65512 201.73\n-0.00049784 1.8106e-06 1.0048\n\nC: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n\nD: 0.48531 0.10549 -95.005\n-0.11843 0.77202 44.217\n-0.00029301 2.8434e-05 0.99773\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.8771 0.00026849 -1.1131\n-0.035484 0.88589 36.525\n-7.7192e-05 -1.833e-05 1\n\nB: 0.4591 -0.47767 436.55\n0.46479 0.46941 -27.514\n-2.7182e-05 -1.2668e-06 1.0191\n\nC: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n\nD: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_63_0.png", "2D-spatial/Homography_estimation/Homography_estimation_63_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.8771 0.00026849 -1.1131\n-0.035484 0.88589 36.525\n-7.7192e-05 -1.833e-05 1\n\nB: 0.4591 -0.47767 436.55\n0.46479 0.46941 -27.514\n-2.7182e-05 -1.2668e-06 1.0191\n\nC: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n\nD: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.5134 -0.0029581 20.934\n0.2678 1.4062 -232.68\n0.00048583 -4.0311e-06 1.0006\n\nB: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nC: 0.49838 -0.015725 33.278\n-0.18045 0.77392 59.799\n-0.00064863 -4.2793e-05 0.99978\n\nD: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_64_0.png", "2D-spatial/Homography_estimation/Homography_estimation_64_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.5134 -0.0029581 20.934\n0.2678 1.4062 -232.68\n0.00048583 -4.0311e-06 1.0006\n\nB: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nC: 0.49838 -0.015725 33.278\n-0.18045 0.77392 59.799\n-0.00064863 -4.2793e-05 0.99978\n\nD: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.3522 0.025037 96.693\n0.20588 1.5085 -279.44\n0.000418 4.2466e-05 1.0103\n\nB: 1.8954 -0.043603 197.83\n0.50589 1.509 -236.95\n0.0010644 -1.6279e-05 1.0115\n\nC: 0.48882 0.0079397 13.575\n-0.24956 0.69593 149.6\n-0.00053246 -7.8574e-06 1.0026\n\nD: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_65_0.png", "2D-spatial/Homography_estimation/Homography_estimation_65_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.3522 0.025037 96.693\n0.20588 1.5085 -279.44\n0.000418 4.2466e-05 1.0103\n\nB: 1.8954 -0.043603 197.83\n0.50589 1.509 -236.95\n0.0010644 -1.6279e-05 1.0115\n\nC: 0.48882 0.0079397 13.575\n-0.24956 0.69593 149.6\n-0.00053246 -7.8574e-06 1.0026\n\nD: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0019 0.045013 144.39\n0.13277 0.95284 -14.111\n0.0002066 5.2875e-05 1\n\nB: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n\nC: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nD: 0.37618 -0.0026073 58.013\n-0.13988 0.81886 117.4\n-0.00032276 -1.1378e-05 0.99983\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_66_0.png", "2D-spatial/Homography_estimation/Homography_estimation_66_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0019 0.045013 144.39\n0.13277 0.95284 -14.111\n0.0002066 5.2875e-05 1\n\nB: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n\nC: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nD: 0.37618 -0.0026073 58.013\n-0.13988 0.81886 117.4\n-0.00032276 -1.1378e-05 0.99983\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n\nB: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n\nC: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n\nD: 1.141 -0.024147 186.42\n0.29573 0.97376 -60.872\n0.00082251 -1.0843e-05 0.99973\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_67_0.png", "2D-spatial/Homography_estimation/Homography_estimation_67_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n\nB: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n\nC: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n\nD: 1.141 -0.024147 186.42\n0.29573 0.97376 -60.872\n0.00082251 -1.0843e-05 0.99973\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nB: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nC: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nD: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_68_0.png", "2D-spatial/Homography_estimation/Homography_estimation_68_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nB: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nC: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nD: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0983 -0.030393 111.31\n0.31879 0.9789 58.516\n0.00050073 -5.3943e-05 1.0005\n\nB: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n\nC: 0.91628 -0.19782 70.502\n0.072414 0.68419 -33.187\n5.7127e-06 -0.00025258 0.99947\n\nD: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_69_0.png", "2D-spatial/Homography_estimation/Homography_estimation_69_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0983 -0.030393 111.31\n0.31879 0.9789 58.516\n0.00050073 -5.3943e-05 1.0005\n\nB: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n\nC: 0.91628 -0.19782 70.502\n0.072414 0.68419 -33.187\n5.7127e-06 -0.00025258 0.99947\n\nD: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n\nB: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nC: 0.62091 -0.030805 57.622\n-0.22703 0.84222 -13.023\n-0.00037179 -4.2767e-05 0.99852\n\nD: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_70_0.png", "2D-spatial/Homography_estimation/Homography_estimation_70_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n\nB: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nC: 0.62091 -0.030805 57.622\n-0.22703 0.84222 -13.023\n-0.00037179 -4.2767e-05 0.99852\n\nD: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.49838 -0.015725 33.278\n-0.18045 0.77392 59.799\n-0.00064863 -4.2793e-05 0.99978\n\nB: 2.9599 0.00703 244.64\n0.78405 1.8789 -438.29\n0.0018411 4.4095e-05 0.99694\n\nC: 0.58099 -0.029382 -20.47\n-0.29479 0.73128 188.62\n-0.00043803 -4.3076e-05 1.0007\n\nD: 2.6177 0.042575 -65.797\n0.74359 2.3954 -903.27\n0.0018892 8.2816e-05 0.98996\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_71_0.png", "2D-spatial/Homography_estimation/Homography_estimation_71_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.49838 -0.015725 33.278\n-0.18045 0.77392 59.799\n-0.00064863 -4.2793e-05 0.99978\n\nB: 2.9599 0.00703 244.64\n0.78405 1.8789 -438.29\n0.0018411 4.4095e-05 0.99694\n\nC: 0.58099 -0.029382 -20.47\n-0.29479 0.73128 188.62\n-0.00043803 -4.3076e-05 1.0007\n\nD: 2.6177 0.042575 -65.797\n0.74359 2.3954 -903.27\n0.0018892 8.2816e-05 0.98996\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nB: 0.73597 -0.0032436 13.11\n0.017092 0.71039 36.002\n5.8878e-05 -9.3828e-06 0.99995\n\nC: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nD: 1.9834 -0.0016422 376.55\n0.84 1.4832 -241.61\n0.0019136 -3.8955e-05 1.0014\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_72_0.png", "2D-spatial/Homography_estimation/Homography_estimation_72_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nB: 0.73597 -0.0032436 13.11\n0.017092 0.71039 36.002\n5.8878e-05 -9.3828e-06 0.99995\n\nC: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nD: 1.9834 -0.0016422 376.55\n0.84 1.4832 -241.61\n0.0019136 -3.8955e-05 1.0014\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n\nB: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n\nC: 0.67444 0.023361 37.089\n-0.047926 0.90094 60.932\n-0.00018688 1.1402e-05 1.0007\n\nD: 1.0063 -0.0054085 288.55\n0.23295 0.84053 7.8206\n0.0005941 1.4583e-05 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_73_0.png", "2D-spatial/Homography_estimation/Homography_estimation_73_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n\nB: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n\nC: 0.67444 0.023361 37.089\n-0.047926 0.90094 60.932\n-0.00018688 1.1402e-05 1.0007\n\nD: 1.0063 -0.0054085 288.55\n0.23295 0.84053 7.8206\n0.0005941 1.4583e-05 1.0001\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nC: 0.4605 0.0019073 42.778\n0.003918 0.45748 107.3\n1.6895e-05 4.8733e-06 1.0001\n\nD: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_74_0.png", "2D-spatial/Homography_estimation/Homography_estimation_74_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nC: 0.4605 0.0019073 42.778\n0.003918 0.45748 107.3\n1.6895e-05 4.8733e-06 1.0001\n\nD: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.43124 0.047668 -66.525\n-0.34772 0.62068 209.27\n-0.00060194 -2.1104e-07 0.98648\n\nB: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n\nC: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n\nD: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_75_0.png", "2D-spatial/Homography_estimation/Homography_estimation_75_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.43124 0.047668 -66.525\n-0.34772 0.62068 209.27\n-0.00060194 -2.1104e-07 0.98648\n\nB: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n\nC: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n\nD: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.547 0.11677 155.75\n0.40373 1.373 -170.1\n0.00090791 8.8782e-05 1.0012\n\nB: 0.056448 -0.012851 135.19\n-0.38625 0.54689 255.61\n-0.00066718 5.392e-05 1.0012\n\nC: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nD: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_76_0.png", "2D-spatial/Homography_estimation/Homography_estimation_76_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.547 0.11677 155.75\n0.40373 1.373 -170.1\n0.00090791 8.8782e-05 1.0012\n\nB: 0.056448 -0.012851 135.19\n-0.38625 0.54689 255.61\n-0.00066718 5.392e-05 1.0012\n\nC: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nD: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.0033111 0.031282 184.63\n-0.15843 0.75999 4.5609\n-0.00083562 0.00011238 0.99927\n\nB: 0.88632 -0.012492 -136.92\n-0.047209 1.0157 42.178\n-0.0001423 1.8595e-05 1.0005\n\nC: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nD: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_77_0.png", "2D-spatial/Homography_estimation/Homography_estimation_77_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.0033111 0.031282 184.63\n-0.15843 0.75999 4.5609\n-0.00083562 0.00011238 0.99927\n\nB: 0.88632 -0.012492 -136.92\n-0.047209 1.0157 42.178\n-0.0001423 1.8595e-05 1.0005\n\nC: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nD: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nB: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nC: -0.47246 -0.28359 869.57\n0.29041 -0.47016 396.67\n5.0949e-06 1.2499e-05 0.99998\n\nD: -0.47246 -0.28359 869.57\n0.29041 -0.47016 396.67\n5.0949e-06 1.2499e-05 0.99998\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_78_0.png", "2D-spatial/Homography_estimation/Homography_estimation_78_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nB: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nC: -0.47246 -0.28359 869.57\n0.29041 -0.47016 396.67\n5.0949e-06 1.2499e-05 0.99998\n\nD: -0.47246 -0.28359 869.57\n0.29041 -0.47016 396.67\n5.0949e-06 1.2499e-05 0.99998\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.55616 0.0088234 83.342\n-0.19782 0.70845 195.76\n-0.00029305 -3.175e-05 0.99884\n\nB: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nC: 0.7088 -0.010965 -26.07\n-0.13602 0.83489 103.19\n-0.00023352 -1.5615e-05 1.0004\n\nD: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_79_0.png", "2D-spatial/Homography_estimation/Homography_estimation_79_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.55616 0.0088234 83.342\n-0.19782 0.70845 195.76\n-0.00029305 -3.175e-05 0.99884\n\nB: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nC: 0.7088 -0.010965 -26.07\n-0.13602 0.83489 103.19\n-0.00023352 -1.5615e-05 1.0004\n\nD: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n\nB: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nC: 0.27317 0.041297 84.951\n-0.22859 0.68736 124.47\n-0.00041264 5.2763e-05 1.0003\n\nD: 1.9861 0.031586 27.893\n0.62141 1.9607 -531.99\n0.0011993 -1.9815e-05 0.99978\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_80_0.png", "2D-spatial/Homography_estimation/Homography_estimation_80_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n\nB: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nC: 0.27317 0.041297 84.951\n-0.22859 0.68736 124.47\n-0.00041264 5.2763e-05 1.0003\n\nD: 1.9861 0.031586 27.893\n0.62141 1.9607 -531.99\n0.0011993 -1.9815e-05 0.99978\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.37107 -0.09213 318.73\n0.086334 0.37505 188.02\n-1.0814e-05 -3.6548e-06 1\n\nB: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nC: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nD: 0.49202 0.0057754 242.06\n0.058005 0.43541 166.02\n0.00018017 1.0746e-05 0.99974\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_81_0.png", "2D-spatial/Homography_estimation/Homography_estimation_81_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.37107 -0.09213 318.73\n0.086334 0.37505 188.02\n-1.0814e-05 -3.6548e-06 1\n\nB: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nC: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nD: 0.49202 0.0057754 242.06\n0.058005 0.43541 166.02\n0.00018017 1.0746e-05 0.99974\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.52064 0.019326 41.006\n-0.1476 0.75468 101.84\n-0.00026848 4.5639e-05 1.0094\n\nB: 1.6284 1.0346 -954.33\n-0.096789 2.5434 -782.98\n-0.00078653 0.0011044 1\n\nC: 0.1176 -0.0075311 194.61\n-0.10067 0.3391 257.1\n-0.00023555 -9.6091e-06 0.99858\n\nD: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_82_0.png", "2D-spatial/Homography_estimation/Homography_estimation_82_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.52064 0.019326 41.006\n-0.1476 0.75468 101.84\n-0.00026848 4.5639e-05 1.0094\n\nB: 1.6284 1.0346 -954.33\n-0.096789 2.5434 -782.98\n-0.00078653 0.0011044 1\n\nC: 0.1176 -0.0075311 194.61\n-0.10067 0.3391 257.1\n-0.00023555 -9.6091e-06 0.99858\n\nD: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nB: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nC: 0.35568 0.079611 -21.49\n-0.17793 0.7199 62.24\n-0.00050458 1.9913e-05 0.9982\n\nD: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_83_0.png", "2D-spatial/Homography_estimation/Homography_estimation_83_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nB: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nC: 0.35568 0.079611 -21.49\n-0.17793 0.7199 62.24\n-0.00050458 1.9913e-05 0.9982\n\nD: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.4605 0.0019073 42.778\n0.003918 0.45748 107.3\n1.6895e-05 4.8733e-06 1.0001\n\nB: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nC: 0.57079 0.0076829 -45.295\n-0.15447 0.93183 62.276\n-0.00028402 -5.8827e-06 0.99996\n\nD: 0.47208 0.021042 63.836\n-0.16332 0.73028 126.94\n-0.00030371 2.4606e-05 0.99981\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_84_0.png", "2D-spatial/Homography_estimation/Homography_estimation_84_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.4605 0.0019073 42.778\n0.003918 0.45748 107.3\n1.6895e-05 4.8733e-06 1.0001\n\nB: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nC: 0.57079 0.0076829 -45.295\n-0.15447 0.93183 62.276\n-0.00028402 -5.8827e-06 0.99996\n\nD: 0.47208 0.021042 63.836\n-0.16332 0.73028 126.94\n-0.00030371 2.4606e-05 0.99981\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nB: 1.5534 0.017684 158.94\n0.56083 1.4841 -343.65\n0.0010107 3.8363e-05 0.99895\n\nC: 1.4272 0.064496 -40.82\n0.15764 1.3161 -94.847\n0.00037033 4.6015e-05 0.99258\n\nD: 1.3951 0.13641 136.74\n0.31704 1.2758 -219.28\n0.00053511 0.00013896 0.99675\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_85_0.png", "2D-spatial/Homography_estimation/Homography_estimation_85_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nB: 1.5534 0.017684 158.94\n0.56083 1.4841 -343.65\n0.0010107 3.8363e-05 0.99895\n\nC: 1.4272 0.064496 -40.82\n0.15764 1.3161 -94.847\n0.00037033 4.6015e-05 0.99258\n\nD: 1.3951 0.13641 136.74\n0.31704 1.2758 -219.28\n0.00053511 0.00013896 0.99675\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.308 -0.067061 201.09\n0.71494 1.8702 -412.16\n0.0015273 -1.6972e-05 1.0162\n\nB: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n\nC: 0.46288 -0.016626 22.437\n-0.26713 0.81047 151.27\n-0.00036789 7.646e-06 0.99855\n\nD: 1.3522 0.025037 96.693\n0.20588 1.5085 -279.44\n0.000418 4.2466e-05 1.0103\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_86_0.png", "2D-spatial/Homography_estimation/Homography_estimation_86_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.308 -0.067061 201.09\n0.71494 1.8702 -412.16\n0.0015273 -1.6972e-05 1.0162\n\nB: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n\nC: 0.46288 -0.016626 22.437\n-0.26713 0.81047 151.27\n-0.00036789 7.646e-06 0.99855\n\nD: 1.3522 0.025037 96.693\n0.20588 1.5085 -279.44\n0.000418 4.2466e-05 1.0103\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nB: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nC: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n\nD: 1.0063 -0.0054085 288.55\n0.23295 0.84053 7.8206\n0.0005941 1.4583e-05 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_87_0.png", "2D-spatial/Homography_estimation/Homography_estimation_87_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nB: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nC: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n\nD: 1.0063 -0.0054085 288.55\n0.23295 0.84053 7.8206\n0.0005941 1.4583e-05 1.0001\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.3951 0.13641 136.74\n0.31704 1.2758 -219.28\n0.00053511 0.00013896 0.99675\n\nB: 0.31269 -0.011782 51.842\n-0.22276 0.71181 65.24\n-0.00081452 -4.173e-05 0.99309\n\nC: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n\nD: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_88_0.png", "2D-spatial/Homography_estimation/Homography_estimation_88_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.3951 0.13641 136.74\n0.31704 1.2758 -219.28\n0.00053511 0.00013896 0.99675\n\nB: 0.31269 -0.011782 51.842\n-0.22276 0.71181 65.24\n-0.00081452 -4.173e-05 0.99309\n\nC: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n\nD: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n\nB: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nC: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nD: 1.1346 -0.16977 -78.128\n-0.0017173 0.8512 -82.973\n8.0333e-07 -0.00031449 0.99917\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_89_0.png", "2D-spatial/Homography_estimation/Homography_estimation_89_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n\nB: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nC: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nD: 1.1346 -0.16977 -78.128\n-0.0017173 0.8512 -82.973\n8.0333e-07 -0.00031449 0.99917\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: -0.21679 -0.12572 585.55\n0.12463 -0.21699 355.1\n-1.085e-06 -1.8818e-06 1.0002\n\nB: 0.31237 0.099342 8.5389\n-0.29392 0.92363 14.629\n-0.00074642 6.3257e-05 0.99168\n\nC: 0.51123 -0.013639 59.603\n-0.16055 0.85238 103.24\n-0.0003334 -4.0403e-05 1.0009\n\nD: 1.1202 -0.0055862 43.04\n0.17566 1.0194 -5.6786\n0.00085767 -4.4625e-05 0.99922\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_90_0.png", "2D-spatial/Homography_estimation/Homography_estimation_90_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: -0.21679 -0.12572 585.55\n0.12463 -0.21699 355.1\n-1.085e-06 -1.8818e-06 1.0002\n\nB: 0.31237 0.099342 8.5389\n-0.29392 0.92363 14.629\n-0.00074642 6.3257e-05 0.99168\n\nC: 0.51123 -0.013639 59.603\n-0.16055 0.85238 103.24\n-0.0003334 -4.0403e-05 1.0009\n\nD: 1.1202 -0.0055862 43.04\n0.17566 1.0194 -5.6786\n0.00085767 -4.4625e-05 0.99922\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n\nB: 0.10472 0.069057 99.841\n-0.17731 0.5329 107.18\n-0.00051255 -1.3734e-05 0.98616\n\nC: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n\nD: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_91_0.png", "2D-spatial/Homography_estimation/Homography_estimation_91_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n\nB: 0.10472 0.069057 99.841\n-0.17731 0.5329 107.18\n-0.00051255 -1.3734e-05 0.98616\n\nC: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n\nD: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.3594 0.0026252 -116.05\n0.5085 2.302 -550.96\n0.0013826 0.0001837 1.0004\n\nB: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nC: 0.25611 0.0594 88.294\n-0.24702 0.7663 71.53\n-0.00048162 6.7687e-05 1.0008\n\nD: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_92_0.png", "2D-spatial/Homography_estimation/Homography_estimation_92_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.3594 0.0026252 -116.05\n0.5085 2.302 -550.96\n0.0013826 0.0001837 1.0004\n\nB: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nC: 0.25611 0.0594 88.294\n-0.24702 0.7663 71.53\n-0.00048162 6.7687e-05 1.0008\n\nD: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.63669 0.0018872 137.9\n-0.00033285 0.63926 95.922\n-2.0441e-06 4.1104e-06 1\n\nB: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n\nC: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nD: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_93_0.png", "2D-spatial/Homography_estimation/Homography_estimation_93_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.63669 0.0018872 137.9\n-0.00033285 0.63926 95.922\n-2.0441e-06 4.1104e-06 1\n\nB: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n\nC: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nD: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.45287 0.0061881 100.32\n-0.053734 0.66556 61.961\n-0.00023168 -5.8559e-06 1.0005\n\nB: 0.62091 -0.030805 57.622\n-0.22703 0.84222 -13.023\n-0.00037179 -4.2767e-05 0.99852\n\nC: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nD: 0.46461 0.085196 589.33\n0.19659 0.76327 25.833\n0.00026763 8.9486e-05 1.0006\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_94_0.png", "2D-spatial/Homography_estimation/Homography_estimation_94_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.45287 0.0061881 100.32\n-0.053734 0.66556 61.961\n-0.00023168 -5.8559e-06 1.0005\n\nB: 0.62091 -0.030805 57.622\n-0.22703 0.84222 -13.023\n-0.00037179 -4.2767e-05 0.99852\n\nC: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nD: 0.46461 0.085196 589.33\n0.19659 0.76327 25.833\n0.00026763 8.9486e-05 1.0006\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.31237 0.099342 8.5389\n-0.29392 0.92363 14.629\n-0.00074642 6.3257e-05 0.99168\n\nB: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nC: 0.62147 0.055609 221.79\n0.21978 1.1561 -23.942\n0.00048557 -4.4311e-05 0.99866\n\nD: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_95_0.png", "2D-spatial/Homography_estimation/Homography_estimation_95_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.31237 0.099342 8.5389\n-0.29392 0.92363 14.629\n-0.00074642 6.3257e-05 0.99168\n\nB: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nC: 0.62147 0.055609 221.79\n0.21978 1.1561 -23.942\n0.00048557 -4.4311e-05 0.99866\n\nD: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.62091 -0.030805 57.622\n-0.22703 0.84222 -13.023\n-0.00037179 -4.2767e-05 0.99852\n\nB: 1.4219 0.01866 342.44\n0.36005 1.3261 -141.73\n0.00090969 2.3838e-05 1.0002\n\nC: 0.38854 -0.073106 92.576\n-0.1986 0.7319 139.21\n-0.00040811 -1.555e-05 0.99988\n\nD: 0.1268 -0.03963 330.5\n-0.1892 0.46973 254.2\n-0.00039857 -3.9641e-05 0.99971\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_96_0.png", "2D-spatial/Homography_estimation/Homography_estimation_96_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.62091 -0.030805 57.622\n-0.22703 0.84222 -13.023\n-0.00037179 -4.2767e-05 0.99852\n\nB: 1.4219 0.01866 342.44\n0.36005 1.3261 -141.73\n0.00090969 2.3838e-05 1.0002\n\nC: 0.38854 -0.073106 92.576\n-0.1986 0.7319 139.21\n-0.00040811 -1.555e-05 0.99988\n\nD: 0.1268 -0.03963 330.5\n-0.1892 0.46973 254.2\n-0.00039857 -3.9641e-05 0.99971\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n\nB: 3.1418 0.21701 -576.91\n0.129 3.5039 -1062.5\n0.0014143 0.00082533 0.98844\n\nC: 0.67783 0.002447 123\n-0.00051063 0.68091 83.563\n-2.5166e-06 5.6486e-06 1\n\nD: 0.4605 0.0019073 42.778\n0.003918 0.45748 107.3\n1.6895e-05 4.8733e-06 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_97_0.png", "2D-spatial/Homography_estimation/Homography_estimation_97_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n\nB: 3.1418 0.21701 -576.91\n0.129 3.5039 -1062.5\n0.0014143 0.00082533 0.98844\n\nC: 0.67783 0.002447 123\n-0.00051063 0.68091 83.563\n-2.5166e-06 5.6486e-06 1\n\nD: 0.4605 0.0019073 42.778\n0.003918 0.45748 107.3\n1.6895e-05 4.8733e-06 1.0001\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n\nB: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nC: 1.5534 0.017684 158.94\n0.56083 1.4841 -343.65\n0.0010107 3.8363e-05 0.99895\n\nD: 1.9861 0.031586 27.893\n0.62141 1.9607 -531.99\n0.0011993 -1.9815e-05 0.99978\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_98_0.png", "2D-spatial/Homography_estimation/Homography_estimation_98_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n\nB: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nC: 1.5534 0.017684 158.94\n0.56083 1.4841 -343.65\n0.0010107 3.8363e-05 0.99895\n\nD: 1.9861 0.031586 27.893\n0.62141 1.9607 -531.99\n0.0011993 -1.9815e-05 0.99978\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.45287 0.0061881 100.32\n-0.053734 0.66556 61.961\n-0.00023168 -5.8559e-06 1.0005\n\nB: 1.4862 -0.061679 54.577\n0.4606 1.2816 -147.5\n0.0007321 -7.3842e-05 0.99895\n\nC: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nD: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_99_0.png", "2D-spatial/Homography_estimation/Homography_estimation_99_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.45287 0.0061881 100.32\n-0.053734 0.66556 61.961\n-0.00023168 -5.8559e-06 1.0005\n\nB: 1.4862 -0.061679 54.577\n0.4606 1.2816 -147.5\n0.0007321 -7.3842e-05 0.99895\n\nC: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nD: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nB: 0.1176 -0.0075311 194.61\n-0.10067 0.3391 257.1\n-0.00023555 -9.6091e-06 0.99858\n\nC: 0.77105 -0.097833 -3.6994\n-0.092675 0.81167 92.799\n-0.0001392 -0.00012806 0.99964\n\nD: 0.48531 0.10549 -95.005\n-0.11843 0.77202 44.217\n-0.00029301 2.8434e-05 0.99773\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_100_0.png", "2D-spatial/Homography_estimation/Homography_estimation_100_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nB: 0.1176 -0.0075311 194.61\n-0.10067 0.3391 257.1\n-0.00023555 -9.6091e-06 0.99858\n\nC: 0.77105 -0.097833 -3.6994\n-0.092675 0.81167 92.799\n-0.0001392 -0.00012806 0.99964\n\nD: 0.48531 0.10549 -95.005\n-0.11843 0.77202 44.217\n-0.00029301 2.8434e-05 0.99773\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n\nB: 0.57125 -0.095863 127.19\n0.050302 0.75099 -13.911\n-0.00020485 1.2421e-06 0.9999\n\nC: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nD: 2.6177 0.042575 -65.797\n0.74359 2.3954 -903.27\n0.0018892 8.2816e-05 0.98996\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_101_0.png", "2D-spatial/Homography_estimation/Homography_estimation_101_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n\nB: 0.57125 -0.095863 127.19\n0.050302 0.75099 -13.911\n-0.00020485 1.2421e-06 0.9999\n\nC: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nD: 2.6177 0.042575 -65.797\n0.74359 2.3954 -903.27\n0.0018892 8.2816e-05 0.98996\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.18178 0.033268 82.883\n-0.24959 0.68306 123.62\n-0.0004688 5.3047e-05 1.0005\n\nB: 0.49202 0.0057754 242.06\n0.058005 0.43541 166.02\n0.00018017 1.0746e-05 0.99974\n\nC: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nD: 0.58099 -0.029382 -20.47\n-0.29479 0.73128 188.62\n-0.00043803 -4.3076e-05 1.0007\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_102_0.png", "2D-spatial/Homography_estimation/Homography_estimation_102_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.18178 0.033268 82.883\n-0.24959 0.68306 123.62\n-0.0004688 5.3047e-05 1.0005\n\nB: 0.49202 0.0057754 242.06\n0.058005 0.43541 166.02\n0.00018017 1.0746e-05 0.99974\n\nC: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nD: 0.58099 -0.029382 -20.47\n-0.29479 0.73128 188.62\n-0.00043803 -4.3076e-05 1.0007\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n\nB: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n\nC: 0.48531 0.10549 -95.005\n-0.11843 0.77202 44.217\n-0.00029301 2.8434e-05 0.99773\n\nD: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_103_0.png", "2D-spatial/Homography_estimation/Homography_estimation_103_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n\nB: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n\nC: 0.48531 0.10549 -95.005\n-0.11843 0.77202 44.217\n-0.00029301 2.8434e-05 0.99773\n\nD: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 5.1051 0.34986 -885.86\n1.0306 5.9768 -2733.1\n0.0033649 0.00099216 1\n\nB: 1.5134 -0.0029581 20.934\n0.2678 1.4062 -232.68\n0.00048583 -4.0311e-06 1.0006\n\nC: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nD: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_104_0.png", "2D-spatial/Homography_estimation/Homography_estimation_104_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 5.1051 0.34986 -885.86\n1.0306 5.9768 -2733.1\n0.0033649 0.00099216 1\n\nB: 1.5134 -0.0029581 20.934\n0.2678 1.4062 -232.68\n0.00048583 -4.0311e-06 1.0006\n\nC: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nD: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nB: 0.60879 -0.35761 289.93\n0.34822 0.61653 -30.949\n-2.0912e-05 1.3527e-06 1.014\n\nC: 0.63669 0.0018872 137.9\n-0.00033285 0.63926 95.922\n-2.0441e-06 4.1104e-06 1\n\nD: 1.8851 0.028166 274.85\n0.48185 1.6951 -326.97\n0.0011778 8.455e-05 0.99801\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_105_0.png", "2D-spatial/Homography_estimation/Homography_estimation_105_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.79208 0.010314 26.019\n-0.023778 0.92337 43.513\n-0.00011513 1.2161e-05 1.0003\n\nB: 0.60879 -0.35761 289.93\n0.34822 0.61653 -30.949\n-2.0912e-05 1.3527e-06 1.014\n\nC: 0.63669 0.0018872 137.9\n-0.00033285 0.63926 95.922\n-2.0441e-06 4.1104e-06 1\n\nD: 1.8851 0.028166 274.85\n0.48185 1.6951 -326.97\n0.0011778 8.455e-05 0.99801\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4733 -0.014435 76.772\n0.25007 1.2556 -120.81\n0.00088206 8.1414e-05 1.002\n\nB: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nC: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nD: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_106_0.png", "2D-spatial/Homography_estimation/Homography_estimation_106_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4733 -0.014435 76.772\n0.25007 1.2556 -120.81\n0.00088206 8.1414e-05 1.002\n\nB: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nC: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nD: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n\nB: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n\nC: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nD: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_107_0.png", "2D-spatial/Homography_estimation/Homography_estimation_107_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n\nB: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n\nC: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nD: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0505 -0.0053825 276.45\n0.20631 0.92888 48.832\n0.00048841 -1.9251e-05 0.99878\n\nB: 0.056448 -0.012851 135.19\n-0.38625 0.54689 255.61\n-0.00066718 5.392e-05 1.0012\n\nC: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nD: 0.20876 0.015221 174.06\n-0.13382 0.55012 11.64\n-0.00044084 3.575e-05 1.0177\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_108_0.png", "2D-spatial/Homography_estimation/Homography_estimation_108_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0505 -0.0053825 276.45\n0.20631 0.92888 48.832\n0.00048841 -1.9251e-05 0.99878\n\nB: 0.056448 -0.012851 135.19\n-0.38625 0.54689 255.61\n-0.00066718 5.392e-05 1.0012\n\nC: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nD: 0.20876 0.015221 174.06\n-0.13382 0.55012 11.64\n-0.00044084 3.575e-05 1.0177\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.141 -0.024147 186.42\n0.29573 0.97376 -60.872\n0.00082251 -1.0843e-05 0.99973\n\nB: 0.42945 0.0071566 96.266\n-0.019537 0.48377 43.049\n-7.8698e-05 1.6013e-05 1.0001\n\nC: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n\nD: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_109_0.png", "2D-spatial/Homography_estimation/Homography_estimation_109_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.141 -0.024147 186.42\n0.29573 0.97376 -60.872\n0.00082251 -1.0843e-05 0.99973\n\nB: 0.42945 0.0071566 96.266\n-0.019537 0.48377 43.049\n-7.8698e-05 1.6013e-05 1.0001\n\nC: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n\nD: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.040904 -0.0023332 234.76\n-0.10713 0.35038 218.5\n-0.00028907 6.311e-06 1.0035\n\nB: 0.38914 0.285 169.51\n-0.28531 0.39347 340.1\n-6.4617e-06 5.0341e-06 1\n\nC: 0.0033111 0.031282 184.63\n-0.15843 0.75999 4.5609\n-0.00083562 0.00011238 0.99927\n\nD: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_110_0.png", "2D-spatial/Homography_estimation/Homography_estimation_110_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.040904 -0.0023332 234.76\n-0.10713 0.35038 218.5\n-0.00028907 6.311e-06 1.0035\n\nB: 0.38914 0.285 169.51\n-0.28531 0.39347 340.1\n-6.4617e-06 5.0341e-06 1\n\nC: 0.0033111 0.031282 184.63\n-0.15843 0.75999 4.5609\n-0.00083562 0.00011238 0.99927\n\nD: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nB: 0.42186 0.031568 60.169\n-0.084563 0.88575 93.738\n-0.00032749 1.4457e-05 1.0012\n\nC: -0.47246 -0.28359 869.57\n0.29041 -0.47016 396.67\n5.0949e-06 1.2499e-05 0.99998\n\nD: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_111_0.png", "2D-spatial/Homography_estimation/Homography_estimation_111_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.1857 -0.0018512 147.73\n-0.094288 0.35154 277.67\n-0.00019671 -1.563e-05 0.9996\n\nB: 0.42186 0.031568 60.169\n-0.084563 0.88575 93.738\n-0.00032749 1.4457e-05 1.0012\n\nC: -0.47246 -0.28359 869.57\n0.29041 -0.47016 396.67\n5.0949e-06 1.2499e-05 0.99998\n\nD: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: -0.21679 -0.12572 585.55\n0.12463 -0.21699 355.1\n-1.085e-06 -1.8818e-06 1.0002\n\nB: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n\nC: 0.13896 0.020204 194.37\n-0.25201 0.63798 118.99\n-0.00052359 2.2762e-05 0.9996\n\nD: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_112_0.png", "2D-spatial/Homography_estimation/Homography_estimation_112_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: -0.21679 -0.12572 585.55\n0.12463 -0.21699 355.1\n-1.085e-06 -1.8818e-06 1.0002\n\nB: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n\nC: 0.13896 0.020204 194.37\n-0.25201 0.63798 118.99\n-0.00052359 2.2762e-05 0.9996\n\nD: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n\nC: 0.46288 -0.016626 22.437\n-0.26713 0.81047 151.27\n-0.00036789 7.646e-06 0.99855\n\nD: 0.14586 0.056449 119.48\n-0.21737 0.71439 95.786\n-0.00051182 3.3282e-05 1.0008\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_113_0.png", "2D-spatial/Homography_estimation/Homography_estimation_113_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n\nC: 0.46288 -0.016626 22.437\n-0.26713 0.81047 151.27\n-0.00036789 7.646e-06 0.99855\n\nD: 0.14586 0.056449 119.48\n-0.21737 0.71439 95.786\n-0.00051182 3.3282e-05 1.0008\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nB: 0.4605 0.0019073 42.778\n0.003918 0.45748 107.3\n1.6895e-05 4.8733e-06 1.0001\n\nC: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n\nD: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_114_0.png", "2D-spatial/Homography_estimation/Homography_estimation_114_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nB: 0.4605 0.0019073 42.778\n0.003918 0.45748 107.3\n1.6895e-05 4.8733e-06 1.0001\n\nC: 1.6408 -0.0013389 -221.64\n0.1704 1.44 -155.56\n0.00036369 -3.22e-05 1.0003\n\nD: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.67444 0.023361 37.089\n-0.047926 0.90094 60.932\n-0.00018688 1.1402e-05 1.0007\n\nB: 0.2024 0.0033266 96.15\n-0.28093 0.65512 201.73\n-0.00049784 1.8106e-06 1.0048\n\nC: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n\nD: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_115_0.png", "2D-spatial/Homography_estimation/Homography_estimation_115_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.67444 0.023361 37.089\n-0.047926 0.90094 60.932\n-0.00018688 1.1402e-05 1.0007\n\nB: 0.2024 0.0033266 96.15\n-0.28093 0.65512 201.73\n-0.00049784 1.8106e-06 1.0048\n\nC: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n\nD: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n\nB: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n\nC: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nD: 0.14705 0.061323 72.893\n-0.27582 0.69094 109.44\n-0.00056993 1.3825e-06 0.9981\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_116_0.png", "2D-spatial/Homography_estimation/Homography_estimation_116_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n\nB: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n\nC: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nD: 0.14705 0.061323 72.893\n-0.27582 0.69094 109.44\n-0.00056993 1.3825e-06 0.9981\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.37107 -0.09213 318.73\n0.086334 0.37505 188.02\n-1.0814e-05 -3.6548e-06 1\n\nB: 0.29534 0.035751 -56.21\n-0.35718 0.5432 233.53\n-0.00064211 -1.1093e-05 0.97783\n\nC: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nD: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_117_0.png", "2D-spatial/Homography_estimation/Homography_estimation_117_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.37107 -0.09213 318.73\n0.086334 0.37505 188.02\n-1.0814e-05 -3.6548e-06 1\n\nB: 0.29534 0.035751 -56.21\n-0.35718 0.5432 233.53\n-0.00064211 -1.1093e-05 0.97783\n\nC: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nD: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nB: 0.20876 0.015221 174.06\n-0.13382 0.55012 11.64\n-0.00044084 3.575e-05 1.0177\n\nC: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nD: 0.51123 -0.013639 59.603\n-0.16055 0.85238 103.24\n-0.0003334 -4.0403e-05 1.0009\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_118_0.png", "2D-spatial/Homography_estimation/Homography_estimation_118_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nB: 0.20876 0.015221 174.06\n-0.13382 0.55012 11.64\n-0.00044084 3.575e-05 1.0177\n\nC: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nD: 0.51123 -0.013639 59.603\n-0.16055 0.85238 103.24\n-0.0003334 -4.0403e-05 1.0009\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.31237 0.099342 8.5389\n-0.29392 0.92363 14.629\n-0.00074642 6.3257e-05 0.99168\n\nB: 1.4272 0.064496 -40.82\n0.15764 1.3161 -94.847\n0.00037033 4.6015e-05 0.99258\n\nC: 14.984 -1.5209 -1987.5\n0.59203 13.878 -3896.8\n0.0072047 0.0038814 0.92614\n\nD: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_119_0.png", "2D-spatial/Homography_estimation/Homography_estimation_119_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.31237 0.099342 8.5389\n-0.29392 0.92363 14.629\n-0.00074642 6.3257e-05 0.99168\n\nB: 1.4272 0.064496 -40.82\n0.15764 1.3161 -94.847\n0.00037033 4.6015e-05 0.99258\n\nC: 14.984 -1.5209 -1987.5\n0.59203 13.878 -3896.8\n0.0072047 0.0038814 0.92614\n\nD: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4733 -0.014435 76.772\n0.25007 1.2556 -120.81\n0.00088206 8.1414e-05 1.002\n\nB: 0.10472 0.069057 99.841\n-0.17731 0.5329 107.18\n-0.00051255 -1.3734e-05 0.98616\n\nC: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n\nD: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_120_0.png", "2D-spatial/Homography_estimation/Homography_estimation_120_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4733 -0.014435 76.772\n0.25007 1.2556 -120.81\n0.00088206 8.1414e-05 1.002\n\nB: 0.10472 0.069057 99.841\n-0.17731 0.5329 107.18\n-0.00051255 -1.3734e-05 0.98616\n\nC: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n\nD: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.43124 0.047668 -66.525\n-0.34772 0.62068 209.27\n-0.00060194 -2.1104e-07 0.98648\n\nB: 1.1202 -0.0055862 43.04\n0.17566 1.0194 -5.6786\n0.00085767 -4.4625e-05 0.99922\n\nC: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nD: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_121_0.png", "2D-spatial/Homography_estimation/Homography_estimation_121_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.43124 0.047668 -66.525\n-0.34772 0.62068 209.27\n-0.00060194 -2.1104e-07 0.98648\n\nB: 1.1202 -0.0055862 43.04\n0.17566 1.0194 -5.6786\n0.00085767 -4.4625e-05 0.99922\n\nC: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nD: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n\nB: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nC: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n\nD: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_122_0.png", "2D-spatial/Homography_estimation/Homography_estimation_122_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n\nB: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nC: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n\nD: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 2.9721 0.034514 6.1536\n0.86739 2.9829 -532.95\n0.0035453 0.00017204 0.95976\n\nC: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nD: 2.1479 0.036813 206.94\n0.67819 1.8174 -485.8\n0.0012074 -6.8043e-06 0.99599\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_123_0.png", "2D-spatial/Homography_estimation/Homography_estimation_123_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 2.9721 0.034514 6.1536\n0.86739 2.9829 -532.95\n0.0035453 0.00017204 0.95976\n\nC: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nD: 2.1479 0.036813 206.94\n0.67819 1.8174 -485.8\n0.0012074 -6.8043e-06 0.99599\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nB: 1.547 0.11677 155.75\n0.40373 1.373 -170.1\n0.00090791 8.8782e-05 1.0012\n\nC: 0.012717 0.014394 193.52\n-0.12386 0.60301 126.7\n-0.00063953 7.9665e-05 1.0012\n\nD: 0.54304 0.026384 236.48\n-0.041921 0.64806 87.13\n-5.8662e-05 1.5685e-05 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_124_0.png", "2D-spatial/Homography_estimation/Homography_estimation_124_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nB: 1.547 0.11677 155.75\n0.40373 1.373 -170.1\n0.00090791 8.8782e-05 1.0012\n\nC: 0.012717 0.014394 193.52\n-0.12386 0.60301 126.7\n-0.00063953 7.9665e-05 1.0012\n\nD: 0.54304 0.026384 236.48\n-0.041921 0.64806 87.13\n-5.8662e-05 1.5685e-05 1\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nB: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n\nC: 0.52949 -0.028655 46.849\n-0.2451 0.79991 158.44\n-0.00032499 -1.8164e-05 0.99959\n\nD: 0.52064 0.019326 41.006\n-0.1476 0.75468 101.84\n-0.00026848 4.5639e-05 1.0094\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_125_0.png", "2D-spatial/Homography_estimation/Homography_estimation_125_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nB: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n\nC: 0.52949 -0.028655 46.849\n-0.2451 0.79991 158.44\n-0.00032499 -1.8164e-05 0.99959\n\nD: 0.52064 0.019326 41.006\n-0.1476 0.75468 101.84\n-0.00026848 4.5639e-05 1.0094\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.9834 -0.0016422 376.55\n0.84 1.4832 -241.61\n0.0019136 -3.8955e-05 1.0014\n\nB: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nC: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nD: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_126_0.png", "2D-spatial/Homography_estimation/Homography_estimation_126_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.9834 -0.0016422 376.55\n0.84 1.4832 -241.61\n0.0019136 -3.8955e-05 1.0014\n\nB: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nC: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nD: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0983 -0.030393 111.31\n0.31879 0.9789 58.516\n0.00050073 -5.3943e-05 1.0005\n\nB: 0.76922 -0.28498 222.68\n0.33855 1.0341 -81.069\n0.00035349 1.2014e-05 0.99834\n\nC: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n\nD: 0.39176 -0.48622 421.69\n0.48543 0.39488 -0.097812\n2.3979e-06 -3.3236e-06 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_127_0.png", "2D-spatial/Homography_estimation/Homography_estimation_127_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0983 -0.030393 111.31\n0.31879 0.9789 58.516\n0.00050073 -5.3943e-05 1.0005\n\nB: 0.76922 -0.28498 222.68\n0.33855 1.0341 -81.069\n0.00035349 1.2014e-05 0.99834\n\nC: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n\nD: 0.39176 -0.48622 421.69\n0.48543 0.39488 -0.097812\n2.3979e-06 -3.3236e-06 1\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.6729 -0.01895 127.73\n-0.015916 0.67847 176.42\n-3.6225e-05 -3.2204e-05 1\n\nB: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nC: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n\nD: 0.14586 0.056449 119.48\n-0.21737 0.71439 95.786\n-0.00051182 3.3282e-05 1.0008\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_128_0.png", "2D-spatial/Homography_estimation/Homography_estimation_128_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.6729 -0.01895 127.73\n-0.015916 0.67847 176.42\n-3.6225e-05 -3.2204e-05 1\n\nB: 0.091252 0.0066749 132.72\n-0.14667 0.47258 88.51\n-0.00056772 8.3791e-06 1.0029\n\nC: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n\nD: 0.14586 0.056449 119.48\n-0.21737 0.71439 95.786\n-0.00051182 3.3282e-05 1.0008\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.28973 0.014397 100.07\n-0.29955 0.64174 168.27\n-0.00067332 7.239e-06 1.0017\n\nB: 0.37618 -0.0026073 58.013\n-0.13988 0.81886 117.4\n-0.00032276 -1.1378e-05 0.99983\n\nC: 0.39176 -0.48622 421.69\n0.48543 0.39488 -0.097812\n2.3979e-06 -3.3236e-06 1\n\nD: -0.19998 0.34647 247.36\n-0.34607 -0.19989 467.21\n2.0354e-07 -5.1701e-08 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_129_0.png", "2D-spatial/Homography_estimation/Homography_estimation_129_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.28973 0.014397 100.07\n-0.29955 0.64174 168.27\n-0.00067332 7.239e-06 1.0017\n\nB: 0.37618 -0.0026073 58.013\n-0.13988 0.81886 117.4\n-0.00032276 -1.1378e-05 0.99983\n\nC: 0.39176 -0.48622 421.69\n0.48543 0.39488 -0.097812\n2.3979e-06 -3.3236e-06 1\n\nD: -0.19998 0.34647 247.36\n-0.34607 -0.19989 467.21\n2.0354e-07 -5.1701e-08 1\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.2024 0.0033266 96.15\n-0.28093 0.65512 201.73\n-0.00049784 1.8106e-06 1.0048\n\nB: 0.54304 0.026384 236.48\n-0.041921 0.64806 87.13\n-5.8662e-05 1.5685e-05 1\n\nC: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n\nD: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_130_0.png", "2D-spatial/Homography_estimation/Homography_estimation_130_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.2024 0.0033266 96.15\n-0.28093 0.65512 201.73\n-0.00049784 1.8106e-06 1.0048\n\nB: 0.54304 0.026384 236.48\n-0.041921 0.64806 87.13\n-5.8662e-05 1.5685e-05 1\n\nC: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n\nD: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n\nB: 1.8954 -0.043603 197.83\n0.50589 1.509 -236.95\n0.0010644 -1.6279e-05 1.0115\n\nC: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n\nD: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_131_0.png", "2D-spatial/Homography_estimation/Homography_estimation_131_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.48275 -0.12831 276.04\n-0.19138 0.40711 199.19\n-5.6548e-05 -0.00023367 0.99912\n\nB: 1.8954 -0.043603 197.83\n0.50589 1.509 -236.95\n0.0010644 -1.6279e-05 1.0115\n\nC: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n\nD: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 3.1627 -0.045434 -351.49\n0.7877 2.8197 -842.9\n0.0015033 -3.676e-05 1.0055\n\nB: 0.45287 0.0061881 100.32\n-0.053734 0.66556 61.961\n-0.00023168 -5.8559e-06 1.0005\n\nC: 1.8954 -0.043603 197.83\n0.50589 1.509 -236.95\n0.0010644 -1.6279e-05 1.0115\n\nD: 1.441 -0.037212 269.33\n0.73295 1.6438 -380.65\n0.0014226 4.1601e-05 1.0102\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_132_0.png", "2D-spatial/Homography_estimation/Homography_estimation_132_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 3.1627 -0.045434 -351.49\n0.7877 2.8197 -842.9\n0.0015033 -3.676e-05 1.0055\n\nB: 0.45287 0.0061881 100.32\n-0.053734 0.66556 61.961\n-0.00023168 -5.8559e-06 1.0005\n\nC: 1.8954 -0.043603 197.83\n0.50589 1.509 -236.95\n0.0010644 -1.6279e-05 1.0115\n\nD: 1.441 -0.037212 269.33\n0.73295 1.6438 -380.65\n0.0014226 4.1601e-05 1.0102\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.46461 0.085196 589.33\n0.19659 0.76327 25.833\n0.00026763 8.9486e-05 1.0006\n\nB: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n\nC: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n\nD: 0.35568 0.079611 -21.49\n-0.17793 0.7199 62.24\n-0.00050458 1.9913e-05 0.9982\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_133_0.png", "2D-spatial/Homography_estimation/Homography_estimation_133_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.46461 0.085196 589.33\n0.19659 0.76327 25.833\n0.00026763 8.9486e-05 1.0006\n\nB: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n\nC: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n\nD: 0.35568 0.079611 -21.49\n-0.17793 0.7199 62.24\n-0.00050458 1.9913e-05 0.9982\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n\nB: 0.32788 -0.00026656 168.52\n-0.087696 0.49289 72.043\n-0.00025798 4.6006e-06 0.9984\n\nC: 1.3526 0.026797 436.87\n0.31517 1.3826 -234.04\n0.00076901 0.00022984 1.0039\n\nD: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_134_0.png", "2D-spatial/Homography_estimation/Homography_estimation_134_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n\nB: 0.32788 -0.00026656 168.52\n-0.087696 0.49289 72.043\n-0.00025798 4.6006e-06 0.9984\n\nC: 1.3526 0.026797 436.87\n0.31517 1.3826 -234.04\n0.00076901 0.00022984 1.0039\n\nD: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nB: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nC: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n\nD: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_135_0.png", "2D-spatial/Homography_estimation/Homography_estimation_135_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n\nB: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nC: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n\nD: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.39176 -0.48622 421.69\n0.48543 0.39488 -0.097812\n2.3979e-06 -3.3236e-06 1\n\nB: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nC: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nD: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_136_0.png", "2D-spatial/Homography_estimation/Homography_estimation_136_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.39176 -0.48622 421.69\n0.48543 0.39488 -0.097812\n2.3979e-06 -3.3236e-06 1\n\nB: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nC: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nD: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nB: 0.2564 0.092521 94.187\n-0.28031 0.83589 -0.15652\n-0.00048968 6.0866e-05 1.0015\n\nC: 0.67444 0.023361 37.089\n-0.047926 0.90094 60.932\n-0.00018688 1.1402e-05 1.0007\n\nD: 1.6284 1.0346 -954.33\n-0.096789 2.5434 -782.98\n-0.00078653 0.0011044 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_137_0.png", "2D-spatial/Homography_estimation/Homography_estimation_137_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nB: 0.2564 0.092521 94.187\n-0.28031 0.83589 -0.15652\n-0.00048968 6.0866e-05 1.0015\n\nC: 0.67444 0.023361 37.089\n-0.047926 0.90094 60.932\n-0.00018688 1.1402e-05 1.0007\n\nD: 1.6284 1.0346 -954.33\n-0.096789 2.5434 -782.98\n-0.00078653 0.0011044 1\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.38266 -0.33125 122.6\n-0.21363 0.61581 225.35\n-0.00034121 -7.7515e-06 0.99865\n\nB: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n\nC: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nD: 0.4221 -0.055916 265.09\n0.060544 0.41967 174.7\n7.7273e-06 -2.0972e-06 0.99999\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_138_0.png", "2D-spatial/Homography_estimation/Homography_estimation_138_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.38266 -0.33125 122.6\n-0.21363 0.61581 225.35\n-0.00034121 -7.7515e-06 0.99865\n\nB: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n\nC: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nD: 0.4221 -0.055916 265.09\n0.060544 0.41967 174.7\n7.7273e-06 -2.0972e-06 0.99999\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.5614 0.083075 163.07\n0.94137 2.2586 -732.08\n0.0017783 2.1603e-05 0.99316\n\nB: 0.37618 -0.0026073 58.013\n-0.13988 0.81886 117.4\n-0.00032276 -1.1378e-05 0.99983\n\nC: 0.63669 0.0018872 137.9\n-0.00033285 0.63926 95.922\n-2.0441e-06 4.1104e-06 1\n\nD: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_139_0.png", "2D-spatial/Homography_estimation/Homography_estimation_139_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.5614 0.083075 163.07\n0.94137 2.2586 -732.08\n0.0017783 2.1603e-05 0.99316\n\nB: 0.37618 -0.0026073 58.013\n-0.13988 0.81886 117.4\n-0.00032276 -1.1378e-05 0.99983\n\nC: 0.63669 0.0018872 137.9\n-0.00033285 0.63926 95.922\n-2.0441e-06 4.1104e-06 1\n\nD: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: -0.21679 -0.12572 585.55\n0.12463 -0.21699 355.1\n-1.085e-06 -1.8818e-06 1.0002\n\nB: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nC: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n\nD: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_140_0.png", "2D-spatial/Homography_estimation/Homography_estimation_140_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: -0.21679 -0.12572 585.55\n0.12463 -0.21699 355.1\n-1.085e-06 -1.8818e-06 1.0002\n\nB: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nC: 2.3515 0.16969 142.03\n1.0602 2.1465 -778.33\n0.0016806 -4.8949e-05 0.99537\n\nD: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 3.1627 -0.045434 -351.49\n0.7877 2.8197 -842.9\n0.0015033 -3.676e-05 1.0055\n\nB: 0.91628 -0.19782 70.502\n0.072414 0.68419 -33.187\n5.7127e-06 -0.00025258 0.99947\n\nC: 0.27317 0.041297 84.951\n-0.22859 0.68736 124.47\n-0.00041264 5.2763e-05 1.0003\n\nD: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_141_0.png", "2D-spatial/Homography_estimation/Homography_estimation_141_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 3.1627 -0.045434 -351.49\n0.7877 2.8197 -842.9\n0.0015033 -3.676e-05 1.0055\n\nB: 0.91628 -0.19782 70.502\n0.072414 0.68419 -33.187\n5.7127e-06 -0.00025258 0.99947\n\nC: 0.27317 0.041297 84.951\n-0.22859 0.68736 124.47\n-0.00041264 5.2763e-05 1.0003\n\nD: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.38266 -0.33125 122.6\n-0.21363 0.61581 225.35\n-0.00034121 -7.7515e-06 0.99865\n\nB: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nC: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n\nD: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_142_0.png", "2D-spatial/Homography_estimation/Homography_estimation_142_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.38266 -0.33125 122.6\n-0.21363 0.61581 225.35\n-0.00034121 -7.7515e-06 0.99865\n\nB: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nC: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n\nD: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.67783 0.002447 123\n-0.00051063 0.68091 83.563\n-2.5166e-06 5.6486e-06 1\n\nB: 0.17608 -0.024321 273.19\n-0.19809 0.7405 74.826\n-0.00053318 1.2457e-05 1.0069\n\nC: 0.2564 0.092521 94.187\n-0.28031 0.83589 -0.15652\n-0.00048968 6.0866e-05 1.0015\n\nD: 1.5134 -0.0029581 20.934\n0.2678 1.4062 -232.68\n0.00048583 -4.0311e-06 1.0006\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_143_0.png", "2D-spatial/Homography_estimation/Homography_estimation_143_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.67783 0.002447 123\n-0.00051063 0.68091 83.563\n-2.5166e-06 5.6486e-06 1\n\nB: 0.17608 -0.024321 273.19\n-0.19809 0.7405 74.826\n-0.00053318 1.2457e-05 1.0069\n\nC: 0.2564 0.092521 94.187\n-0.28031 0.83589 -0.15652\n-0.00048968 6.0866e-05 1.0015\n\nD: 1.5134 -0.0029581 20.934\n0.2678 1.4062 -232.68\n0.00048583 -4.0311e-06 1.0006\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n\nC: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n\nD: 0.17608 -0.024321 273.19\n-0.19809 0.7405 74.826\n-0.00053318 1.2457e-05 1.0069\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_144_0.png", "2D-spatial/Homography_estimation/Homography_estimation_144_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n\nC: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n\nD: 0.17608 -0.024321 273.19\n-0.19809 0.7405 74.826\n-0.00053318 1.2457e-05 1.0069\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n\nB: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n\nC: 0.42945 0.0071566 96.266\n-0.019537 0.48377 43.049\n-7.8698e-05 1.6013e-05 1.0001\n\nD: 0.81883 -0.28544 161.88\n0.010536 0.53499 62.327\n1.3163e-05 -0.00056443 1.0014\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_145_0.png", "2D-spatial/Homography_estimation/Homography_estimation_145_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.33414 0.069646 90.22\n-0.25229 0.73446 157.67\n-0.00038885 2.2582e-06 1.0024\n\nB: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n\nC: 0.42945 0.0071566 96.266\n-0.019537 0.48377 43.049\n-7.8698e-05 1.6013e-05 1.0001\n\nD: 0.81883 -0.28544 161.88\n0.010536 0.53499 62.327\n1.3163e-05 -0.00056443 1.0014\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.31269 -0.011782 51.842\n-0.22276 0.71181 65.24\n-0.00081452 -4.173e-05 0.99309\n\nB: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n\nC: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nD: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_146_0.png", "2D-spatial/Homography_estimation/Homography_estimation_146_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.31269 -0.011782 51.842\n-0.22276 0.71181 65.24\n-0.00081452 -4.173e-05 0.99309\n\nB: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n\nC: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nD: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nB: 0.0033111 0.031282 184.63\n-0.15843 0.75999 4.5609\n-0.00083562 0.00011238 0.99927\n\nC: 0.42186 0.031568 60.169\n-0.084563 0.88575 93.738\n-0.00032749 1.4457e-05 1.0012\n\nD: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_147_0.png", "2D-spatial/Homography_estimation/Homography_estimation_147_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n\nB: 0.0033111 0.031282 184.63\n-0.15843 0.75999 4.5609\n-0.00083562 0.00011238 0.99927\n\nC: 0.42186 0.031568 60.169\n-0.084563 0.88575 93.738\n-0.00032749 1.4457e-05 1.0012\n\nD: 0.54693 0.20925 -108.35\n-0.082341 1.1176 -236.48\n-0.0006026 0.0001769 1.0001\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n\nB: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nC: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n\nD: 0.86273 0.030727 -257.65\n-0.081274 1.0175 -48.986\n-0.00016043 4.4449e-05 1.0008\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_148_0.png", "2D-spatial/Homography_estimation/Homography_estimation_148_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.3903 -0.069797 29.319\n0.18963 1.0284 22.049\n0.00052989 -9.8197e-05 1.0021\n\nB: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nC: 1.8454 -0.0093839 117.6\n0.8533 1.9335 -566.11\n0.0016091 6.8147e-05 1.0105\n\nD: 0.86273 0.030727 -257.65\n-0.081274 1.0175 -48.986\n-0.00016043 4.4449e-05 1.0008\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nB: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n\nC: 0.81883 -0.28544 161.88\n0.010536 0.53499 62.327\n1.3163e-05 -0.00056443 1.0014\n\nD: 0.48882 0.0079397 13.575\n-0.24956 0.69593 149.6\n-0.00053246 -7.8574e-06 1.0026\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_149_0.png", "2D-spatial/Homography_estimation/Homography_estimation_149_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nB: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n\nC: 0.81883 -0.28544 161.88\n0.010536 0.53499 62.327\n1.3163e-05 -0.00056443 1.0014\n\nD: 0.48882 0.0079397 13.575\n-0.24956 0.69593 149.6\n-0.00053246 -7.8574e-06 1.0026\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nB: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n\nC: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nD: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_150_0.png", "2D-spatial/Homography_estimation/Homography_estimation_150_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nB: 0.54864 -0.010797 -6.1494\n-0.11876 0.86651 111.28\n-0.00026448 -1.8961e-05 1\n\nC: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nD: 0.85555 -0.17378 91.59\n0.17068 0.85755 -31.264\n-5.1182e-06 2.0966e-06 1.0023\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.29534 0.035751 -56.21\n-0.35718 0.5432 233.53\n-0.00064211 -1.1093e-05 0.97783\n\nB: 14.984 -1.5209 -1987.5\n0.59203 13.878 -3896.8\n0.0072047 0.0038814 0.92614\n\nC: 0.67783 0.002447 123\n-0.00051063 0.68091 83.563\n-2.5166e-06 5.6486e-06 1\n\nD: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_151_0.png", "2D-spatial/Homography_estimation/Homography_estimation_151_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.29534 0.035751 -56.21\n-0.35718 0.5432 233.53\n-0.00064211 -1.1093e-05 0.97783\n\nB: 14.984 -1.5209 -1987.5\n0.59203 13.878 -3896.8\n0.0072047 0.0038814 0.92614\n\nC: 0.67783 0.002447 123\n-0.00051063 0.68091 83.563\n-2.5166e-06 5.6486e-06 1\n\nD: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.47208 0.021042 63.836\n-0.16332 0.73028 126.94\n-0.00030371 2.4606e-05 0.99981\n\nB: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nC: 1.3308 -0.060097 223.54\n0.17906 0.94189 -10.999\n0.00034146 -4.4675e-05 0.99983\n\nD: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_152_0.png", "2D-spatial/Homography_estimation/Homography_estimation_152_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.47208 0.021042 63.836\n-0.16332 0.73028 126.94\n-0.00030371 2.4606e-05 0.99981\n\nB: 0.13416 0.073075 56.977\n-0.21333 0.70433 84.528\n-0.00055481 6.1106e-05 1\n\nC: 1.3308 -0.060097 223.54\n0.17906 0.94189 -10.999\n0.00034146 -4.4675e-05 0.99983\n\nD: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n\nB: 0.55347 0.01345 110.12\n-0.085938 0.64894 151.2\n-0.00016395 1.1079e-05 0.99926\n\nC: 0.28973 0.014397 100.07\n-0.29955 0.64174 168.27\n-0.00067332 7.239e-06 1.0017\n\nD: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_153_0.png", "2D-spatial/Homography_estimation/Homography_estimation_153_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.69134 -0.0063829 116.24\n0.0053381 0.71985 83.96\n-1.8171e-05 2.7124e-05 1\n\nB: 0.55347 0.01345 110.12\n-0.085938 0.64894 151.2\n-0.00016395 1.1079e-05 0.99926\n\nC: 0.28973 0.014397 100.07\n-0.29955 0.64174 168.27\n-0.00067332 7.239e-06 1.0017\n\nD: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.77105 -0.097833 -3.6994\n-0.092675 0.81167 92.799\n-0.0001392 -0.00012806 0.99964\n\nB: 0.32788 -0.00026656 168.52\n-0.087696 0.49289 72.043\n-0.00025798 4.6006e-06 0.9984\n\nC: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nD: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_154_0.png", "2D-spatial/Homography_estimation/Homography_estimation_154_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.77105 -0.097833 -3.6994\n-0.092675 0.81167 92.799\n-0.0001392 -0.00012806 0.99964\n\nB: 0.32788 -0.00026656 168.52\n-0.087696 0.49289 72.043\n-0.00025798 4.6006e-06 0.9984\n\nC: 0.88184 0.31397 -39.976\n-0.18167 0.93621 153.25\n0.00020118 -1.9028e-05 0.99997\n\nD: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.6413 0.074225 91.097\n0.77035 1.5061 -362.72\n0.0010583 -7.1897e-05 1.0011\n\nB: 1.0478 0.035143 64.843\n0.063507 1.0349 21.701\n0.00023044 -6.878e-06 0.99998\n\nC: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nD: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_155_0.png", "2D-spatial/Homography_estimation/Homography_estimation_155_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.6413 0.074225 91.097\n0.77035 1.5061 -362.72\n0.0010583 -7.1897e-05 1.0011\n\nB: 1.0478 0.035143 64.843\n0.063507 1.0349 21.701\n0.00023044 -6.878e-06 0.99998\n\nC: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nD: 1.3838 0.024181 -93.882\n0.093344 1.307 -232.76\n0.00015995 6.7546e-05 1.0008\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0035 -0.00055314 2.5255\n-0.0028717 1.0087 -9.7285\n-3.8783e-06 3.4244e-06 1\n\nB: 1.3951 0.13641 136.74\n0.31704 1.2758 -219.28\n0.00053511 0.00013896 0.99675\n\nC: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n\nD: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_156_0.png", "2D-spatial/Homography_estimation/Homography_estimation_156_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0035 -0.00055314 2.5255\n-0.0028717 1.0087 -9.7285\n-3.8783e-06 3.4244e-06 1\n\nB: 1.3951 0.13641 136.74\n0.31704 1.2758 -219.28\n0.00053511 0.00013896 0.99675\n\nC: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n\nD: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nB: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n\nC: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nD: 0.88632 -0.012492 -136.92\n-0.047209 1.0157 42.178\n-0.0001423 1.8595e-05 1.0005\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_157_0.png", "2D-spatial/Homography_estimation/Homography_estimation_157_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nB: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n\nC: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nD: 0.88632 -0.012492 -136.92\n-0.047209 1.0157 42.178\n-0.0001423 1.8595e-05 1.0005\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nB: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n\nC: 0.032608 0.010774 198.34\n-0.16134 0.44659 114.31\n-0.00057725 -5.1566e-07 1.0017\n\nD: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_158_0.png", "2D-spatial/Homography_estimation/Homography_estimation_158_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.1442 -0.037625 115.5\n0.22206 1.0286 -30.039\n0.00032815 -2.4116e-05 0.9999\n\nB: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n\nC: 0.032608 0.010774 198.34\n-0.16134 0.44659 114.31\n-0.00057725 -5.1566e-07 1.0017\n\nD: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n\nB: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n\nC: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nD: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_159_0.png", "2D-spatial/Homography_estimation/Homography_estimation_159_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n\nB: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n\nC: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nD: 0.22888 0.0058691 272.09\n-0.077153 0.3923 203.08\n-0.00024299 -4.5827e-06 1.0015\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.49202 0.0057754 242.06\n0.058005 0.43541 166.02\n0.00018017 1.0746e-05 0.99974\n\nB: 0.48882 0.0079397 13.575\n-0.24956 0.69593 149.6\n-0.00053246 -7.8574e-06 1.0026\n\nC: 0.86273 0.030727 -257.65\n-0.081274 1.0175 -48.986\n-0.00016043 4.4449e-05 1.0008\n\nD: 1.1198 0.031669 158.94\n0.13747 0.986 -24.458\n0.00036259 4.1267e-05 0.99658\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_160_0.png", "2D-spatial/Homography_estimation/Homography_estimation_160_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.49202 0.0057754 242.06\n0.058005 0.43541 166.02\n0.00018017 1.0746e-05 0.99974\n\nB: 0.48882 0.0079397 13.575\n-0.24956 0.69593 149.6\n-0.00053246 -7.8574e-06 1.0026\n\nC: 0.86273 0.030727 -257.65\n-0.081274 1.0175 -48.986\n-0.00016043 4.4449e-05 1.0008\n\nD: 1.1198 0.031669 158.94\n0.13747 0.986 -24.458\n0.00036259 4.1267e-05 0.99658\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.58099 -0.029382 -20.47\n-0.29479 0.73128 188.62\n-0.00043803 -4.3076e-05 1.0007\n\nB: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nC: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nD: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_161_0.png", "2D-spatial/Homography_estimation/Homography_estimation_161_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.58099 -0.029382 -20.47\n-0.29479 0.73128 188.62\n-0.00043803 -4.3076e-05 1.0007\n\nB: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nC: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nD: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.57125 -0.095863 127.19\n0.050302 0.75099 -13.911\n-0.00020485 1.2421e-06 0.9999\n\nB: 0.57079 0.0076829 -45.295\n-0.15447 0.93183 62.276\n-0.00028402 -5.8827e-06 0.99996\n\nC: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nD: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_162_0.png", "2D-spatial/Homography_estimation/Homography_estimation_162_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.57125 -0.095863 127.19\n0.050302 0.75099 -13.911\n-0.00020485 1.2421e-06 0.9999\n\nB: 0.57079 0.0076829 -45.295\n-0.15447 0.93183 62.276\n-0.00028402 -5.8827e-06 0.99996\n\nC: 0.24117 0.068506 48.185\n-0.23318 0.79398 68.106\n-0.0005259 5.079e-05 0.99834\n\nD: 0.75268 -0.0092452 -71.273\n-0.17607 0.97566 6.3105\n-0.00029582 -1.5187e-05 0.99957\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n\nB: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nC: 0.7088 -0.010965 -26.07\n-0.13602 0.83489 103.19\n-0.00023352 -1.5615e-05 1.0004\n\nD: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_163_0.png", "2D-spatial/Homography_estimation/Homography_estimation_163_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n\nB: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nC: 0.7088 -0.010965 -26.07\n-0.13602 0.83489 103.19\n-0.00023352 -1.5615e-05 1.0004\n\nD: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.55347 0.01345 110.12\n-0.085938 0.64894 151.2\n-0.00016395 1.1079e-05 0.99926\n\nB: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n\nC: 2.1479 0.036813 206.94\n0.67819 1.8174 -485.8\n0.0012074 -6.8043e-06 0.99599\n\nD: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_164_0.png", "2D-spatial/Homography_estimation/Homography_estimation_164_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.55347 0.01345 110.12\n-0.085938 0.64894 151.2\n-0.00016395 1.1079e-05 0.99926\n\nB: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n\nC: 2.1479 0.036813 206.94\n0.67819 1.8174 -485.8\n0.0012074 -6.8043e-06 0.99599\n\nD: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n\nC: 2.6481 0.070248 -423.11\n0.5002 2.6605 -906.39\n0.0012014 0.00025943 0.99533\n\nD: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_165_0.png", "2D-spatial/Homography_estimation/Homography_estimation_165_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4932 0.01661 231.74\n0.45676 1.4341 -212.29\n0.0013256 9.9938e-05 0.99686\n\nB: 0.40245 -0.33938 102.29\n-0.2125 0.62381 216.78\n-0.00033866 -1.5855e-05 1.0018\n\nC: 2.6481 0.070248 -423.11\n0.5002 2.6605 -906.39\n0.0012014 0.00025943 0.99533\n\nD: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n\nB: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n\nC: 1.8851 0.028166 274.85\n0.48185 1.6951 -326.97\n0.0011778 8.455e-05 0.99801\n\nD: -0.19998 0.34647 247.36\n-0.34607 -0.19989 467.21\n2.0354e-07 -5.1701e-08 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_166_0.png", "2D-spatial/Homography_estimation/Homography_estimation_166_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n\nB: 1.1901 -0.048587 107.72\n0.14488 1.1926 -121.84\n0.00033622 1.1241e-05 1.0001\n\nC: 1.8851 0.028166 274.85\n0.48185 1.6951 -326.97\n0.0011778 8.455e-05 0.99801\n\nD: -0.19998 0.34647 247.36\n-0.34607 -0.19989 467.21\n2.0354e-07 -5.1701e-08 1\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n\nB: 3.1627 -0.045434 -351.49\n0.7877 2.8197 -842.9\n0.0015033 -3.676e-05 1.0055\n\nC: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nD: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_167_0.png", "2D-spatial/Homography_estimation/Homography_estimation_167_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n\nB: 3.1627 -0.045434 -351.49\n0.7877 2.8197 -842.9\n0.0015033 -3.676e-05 1.0055\n\nC: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nD: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.60879 -0.35761 289.93\n0.34822 0.61653 -30.949\n-2.0912e-05 1.3527e-06 1.014\n\nB: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nC: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n\nD: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_168_0.png", "2D-spatial/Homography_estimation/Homography_estimation_168_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.60879 -0.35761 289.93\n0.34822 0.61653 -30.949\n-2.0912e-05 1.3527e-06 1.014\n\nB: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nC: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n\nD: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.6413 0.074225 91.097\n0.77035 1.5061 -362.72\n0.0010583 -7.1897e-05 1.0011\n\nB: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nC: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n\nD: 0.85799 0.21669 9.4839\n-0.21177 0.85855 130.48\n1.5015e-06 9.2033e-07 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_169_0.png", "2D-spatial/Homography_estimation/Homography_estimation_169_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.6413 0.074225 91.097\n0.77035 1.5061 -362.72\n0.0010583 -7.1897e-05 1.0011\n\nB: 2.4665 0.083695 233.31\n0.87021 2.8235 -936.68\n0.0017821 0.0001592 0.98707\n\nC: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n\nD: 0.85799 0.21669 9.4839\n-0.21177 0.85855 130.48\n1.5015e-06 9.2033e-07 1\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n\nB: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n\nC: 0.55616 0.0088234 83.342\n-0.19782 0.70845 195.76\n-0.00029305 -3.175e-05 0.99884\n\nD: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_170_0.png", "2D-spatial/Homography_estimation/Homography_estimation_170_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n\nB: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n\nC: 0.55616 0.0088234 83.342\n-0.19782 0.70845 195.76\n-0.00029305 -3.175e-05 0.99884\n\nD: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.31237 0.099342 8.5389\n-0.29392 0.92363 14.629\n-0.00074642 6.3257e-05 0.99168\n\nB: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n\nC: 1.3526 0.026797 436.87\n0.31517 1.3826 -234.04\n0.00076901 0.00022984 1.0039\n\nD: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_171_0.png", "2D-spatial/Homography_estimation/Homography_estimation_171_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.31237 0.099342 8.5389\n-0.29392 0.92363 14.629\n-0.00074642 6.3257e-05 0.99168\n\nB: 2.2078 0.054458 63.617\n0.67654 2.2557 -637.98\n0.0013191 8.5079e-05 1.0033\n\nC: 1.3526 0.026797 436.87\n0.31517 1.3826 -234.04\n0.00076901 0.00022984 1.0039\n\nD: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.67444 0.023361 37.089\n-0.047926 0.90094 60.932\n-0.00018688 1.1402e-05 1.0007\n\nB: 0.57079 0.0076829 -45.295\n-0.15447 0.93183 62.276\n-0.00028402 -5.8827e-06 0.99996\n\nC: 4.3722 0.14407 -818.24\n-0.25209 3.9595 -549.15\n0.001718 0.0010825 0.97985\n\nD: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_172_0.png", "2D-spatial/Homography_estimation/Homography_estimation_172_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.67444 0.023361 37.089\n-0.047926 0.90094 60.932\n-0.00018688 1.1402e-05 1.0007\n\nB: 0.57079 0.0076829 -45.295\n-0.15447 0.93183 62.276\n-0.00028402 -5.8827e-06 0.99996\n\nC: 4.3722 0.14407 -818.24\n-0.25209 3.9595 -549.15\n0.001718 0.0010825 0.97985\n\nD: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.441 -0.037212 269.33\n0.73295 1.6438 -380.65\n0.0014226 4.1601e-05 1.0102\n\nB: 0.25611 0.0594 88.294\n-0.24702 0.7663 71.53\n-0.00048162 6.7687e-05 1.0008\n\nC: 0.37107 -0.09213 318.73\n0.086334 0.37505 188.02\n-1.0814e-05 -3.6548e-06 1\n\nD: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_173_0.png", "2D-spatial/Homography_estimation/Homography_estimation_173_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.441 -0.037212 269.33\n0.73295 1.6438 -380.65\n0.0014226 4.1601e-05 1.0102\n\nB: 0.25611 0.0594 88.294\n-0.24702 0.7663 71.53\n-0.00048162 6.7687e-05 1.0008\n\nC: 0.37107 -0.09213 318.73\n0.086334 0.37505 188.02\n-1.0814e-05 -3.6548e-06 1\n\nD: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n\nB: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nC: 0.33492 -0.0051126 63.132\n-0.19841 0.81318 98.482\n-0.00041298 -2.8119e-05 0.99833\n\nD: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_174_0.png", "2D-spatial/Homography_estimation/Homography_estimation_174_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n\nB: 1.2108 -0.031741 47.374\n0.20996 1.0345 -107.36\n0.00054926 -6.3631e-06 1.0004\n\nC: 0.33492 -0.0051126 63.132\n-0.19841 0.81318 98.482\n-0.00041298 -2.8119e-05 0.99833\n\nD: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.1268 -0.03963 330.5\n-0.1892 0.46973 254.2\n-0.00039857 -3.9641e-05 0.99971\n\nB: 2.9721 0.034514 6.1536\n0.86739 2.9829 -532.95\n0.0035453 0.00017204 0.95976\n\nC: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nD: 0.29534 0.035751 -56.21\n-0.35718 0.5432 233.53\n-0.00064211 -1.1093e-05 0.97783\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_175_0.png", "2D-spatial/Homography_estimation/Homography_estimation_175_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.1268 -0.03963 330.5\n-0.1892 0.46973 254.2\n-0.00039857 -3.9641e-05 0.99971\n\nB: 2.9721 0.034514 6.1536\n0.86739 2.9829 -532.95\n0.0035453 0.00017204 0.95976\n\nC: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nD: 0.29534 0.035751 -56.21\n-0.35718 0.5432 233.53\n-0.00064211 -1.1093e-05 0.97783\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nB: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n\nC: 1.0983 -0.030393 111.31\n0.31879 0.9789 58.516\n0.00050073 -5.3943e-05 1.0005\n\nD: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_176_0.png", "2D-spatial/Homography_estimation/Homography_estimation_176_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.31483 0.11583 690.51\n0.17546 0.70637 14.497\n0.00026712 0.00012691 1\n\nB: 0.77044 -0.014353 152.19\n0.007827 0.75172 76.397\n1.9039e-05 -2.1554e-05 1\n\nC: 1.0983 -0.030393 111.31\n0.31879 0.9789 58.516\n0.00050073 -5.3943e-05 1.0005\n\nD: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nB: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nC: 0.88632 -0.012492 -136.92\n-0.047209 1.0157 42.178\n-0.0001423 1.8595e-05 1.0005\n\nD: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_177_0.png", "2D-spatial/Homography_estimation/Homography_estimation_177_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.44469 -0.1629 197.72\n-0.090792 0.33606 37.55\n-0.00032851 -0.00028415 1.0004\n\nB: 2.4144 -0.0022023 -199.3\n0.52146 2.0547 -569.49\n0.0010423 8.4489e-05 1.0043\n\nC: 0.88632 -0.012492 -136.92\n-0.047209 1.0157 42.178\n-0.0001423 1.8595e-05 1.0005\n\nD: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n\nB: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nC: 0.49838 -0.015725 33.278\n-0.18045 0.77392 59.799\n-0.00064863 -4.2793e-05 0.99978\n\nD: 1.8954 -0.043603 197.83\n0.50589 1.509 -236.95\n0.0010644 -1.6279e-05 1.0115\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_178_0.png", "2D-spatial/Homography_estimation/Homography_estimation_178_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.4403 0.27154 10.734\n0.071471 1.5534 -44.533\n0.00030432 0.00049723 1.001\n\nB: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nC: 0.49838 -0.015725 33.278\n-0.18045 0.77392 59.799\n-0.00064863 -4.2793e-05 0.99978\n\nD: 1.8954 -0.043603 197.83\n0.50589 1.509 -236.95\n0.0010644 -1.6279e-05 1.0115\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.25611 0.0594 88.294\n-0.24702 0.7663 71.53\n-0.00048162 6.7687e-05 1.0008\n\nB: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nC: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nD: 0.48531 0.10549 -95.005\n-0.11843 0.77202 44.217\n-0.00029301 2.8434e-05 0.99773\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_179_0.png", "2D-spatial/Homography_estimation/Homography_estimation_179_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.25611 0.0594 88.294\n-0.24702 0.7663 71.53\n-0.00048162 6.7687e-05 1.0008\n\nB: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nC: 0.55202 0.096567 108.66\n-0.35774 1.4927 -276.32\n-0.00068886 0.0001065 0.98986\n\nD: 0.48531 0.10549 -95.005\n-0.11843 0.77202 44.217\n-0.00029301 2.8434e-05 0.99773\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nB: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n\nC: 1.0499 0.025643 108.77\n0.19467 1.0054 -7.8895\n0.0011218 -3.184e-05 1.0021\n\nD: 0.38854 -0.073106 92.576\n-0.1986 0.7319 139.21\n-0.00040811 -1.555e-05 0.99988\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_180_0.png", "2D-spatial/Homography_estimation/Homography_estimation_180_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.37694 0.049406 111.53\n-0.16444 0.72986 84.602\n-0.00037753 4.0247e-05 0.99869\n\nB: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n\nC: 1.0499 0.025643 108.77\n0.19467 1.0054 -7.8895\n0.0011218 -3.184e-05 1.0021\n\nD: 0.38854 -0.073106 92.576\n-0.1986 0.7319 139.21\n-0.00040811 -1.555e-05 0.99988\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n\nB: 1.4219 0.01866 342.44\n0.36005 1.3261 -141.73\n0.00090969 2.3838e-05 1.0002\n\nC: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nD: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_181_0.png", "2D-spatial/Homography_estimation/Homography_estimation_181_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.34904 -0.0038637 -43.899\n-0.22316 0.99346 45.579\n-0.00041195 -1.2246e-05 1\n\nB: 1.4219 0.01866 342.44\n0.36005 1.3261 -141.73\n0.00090969 2.3838e-05 1.0002\n\nC: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n\nD: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.81883 -0.28544 161.88\n0.010536 0.53499 62.327\n1.3163e-05 -0.00056443 1.0014\n\nB: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n\nC: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nD: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_182_0.png", "2D-spatial/Homography_estimation/Homography_estimation_182_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.81883 -0.28544 161.88\n0.010536 0.53499 62.327\n1.3163e-05 -0.00056443 1.0014\n\nB: 0.36677 -0.019493 213.68\n-0.082321 0.47708 180.81\n-0.00021125 -4.1441e-05 1.0123\n\nC: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nD: 0.94726 0.076953 177.36\n0.25112 1.0126 13.205\n0.00047269 2.7805e-05 0.99969\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n\nB: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n\nC: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n\nD: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_183_0.png", "2D-spatial/Homography_estimation/Homography_estimation_183_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.030125 -0.01797 299.5\n-0.19573 0.45869 167.74\n-0.00051291 -3.9704e-05 1.0019\n\nB: 0.74922 -0.0014388 -75.597\n-0.074158 0.94323 40.455\n-0.00018126 -6.2301e-06 1\n\nC: 1.3186 -0.0097277 -143.16\n0.094663 1.1956 -58.383\n0.00019153 -2.0281e-05 0.99989\n\nD: 0.53266 0.0019756 44.297\n-0.18137 0.85955 61.945\n-0.00038035 1.4705e-06 0.9999\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n\nB: 0.31269 -0.011782 51.842\n-0.22276 0.71181 65.24\n-0.00081452 -4.173e-05 0.99309\n\nC: 2.9599 0.00703 244.64\n0.78405 1.8789 -438.29\n0.0018411 4.4095e-05 0.99694\n\nD: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_184_0.png", "2D-spatial/Homography_estimation/Homography_estimation_184_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.7761 -0.053427 263.17\n0.41751 1.5987 -329.46\n0.00069677 3.1372e-05 1.0014\n\nB: 0.31269 -0.011782 51.842\n-0.22276 0.71181 65.24\n-0.00081452 -4.173e-05 0.99309\n\nC: 2.9599 0.00703 244.64\n0.78405 1.8789 -438.29\n0.0018411 4.4095e-05 0.99694\n\nD: 1.1529 0.012747 244.44\n0.41529 1.1943 -155.59\n0.00087156 5.6224e-05 1.0092\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nB: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nC: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n\nD: 0.084461 -0.022036 252.3\n-0.21 0.51325 245.38\n-0.000447 -2.621e-05 1.0009\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_185_0.png", "2D-spatial/Homography_estimation/Homography_estimation_185_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.54372 0.011697 65.787\n-0.06271 0.8727 105.67\n-0.00025117 2.4814e-06 0.99967\n\nB: 0.38922 0.015343 55.85\n-0.1763 0.84543 87.344\n-0.00049385 -2.1034e-05 1.0072\n\nC: 0.45841 0.038317 36.428\n-0.26806 0.75693 165.6\n-0.00037539 -1.4035e-05 1.0016\n\nD: 0.084461 -0.022036 252.3\n-0.21 0.51325 245.38\n-0.000447 -2.621e-05 1.0009\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nB: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nC: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nD: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_186_0.png", "2D-spatial/Homography_estimation/Homography_estimation_186_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 3.4851 0.086317 195.9\n1.1598 3.067 -1009.5\n0.0025647 -5.4567e-05 0.99349\n\nB: 0.70212 0.43231 -128.54\n-0.42351 0.70276 199.3\n6.3285e-06 1.2175e-05 0.99997\n\nC: 0.23209 -0.67097 528.16\n0.66389 0.2516 -30.266\n-3.168e-05 2.5631e-05 1.0087\n\nD: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 1.4862 -0.061679 54.577\n0.4606 1.2816 -147.5\n0.0007321 -7.3842e-05 0.99895\n\nC: 0.41873 -0.043533 -18.562\n-0.27021 0.88041 53.791\n-0.00050299 -2.2546e-05 0.99941\n\nD: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_187_0.png", "2D-spatial/Homography_estimation/Homography_estimation_187_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 1.4862 -0.061679 54.577\n0.4606 1.2816 -147.5\n0.0007321 -7.3842e-05 0.99895\n\nC: 0.41873 -0.043533 -18.562\n-0.27021 0.88041 53.791\n-0.00050299 -2.2546e-05 0.99941\n\nD: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.47208 0.021042 63.836\n-0.16332 0.73028 126.94\n-0.00030371 2.4606e-05 0.99981\n\nB: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nC: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nD: 2.6177 0.042575 -65.797\n0.74359 2.3954 -903.27\n0.0018892 8.2816e-05 0.98996\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_188_0.png", "2D-spatial/Homography_estimation/Homography_estimation_188_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.47208 0.021042 63.836\n-0.16332 0.73028 126.94\n-0.00030371 2.4606e-05 0.99981\n\nB: 1.6477 -0.037624 101.59\n0.49962 1.5725 -364.98\n0.00090272 4.6589e-05 1.0037\n\nC: 1.0669 0.31109 194.1\n-0.019953 0.9209 79.624\n0.000135 -7.6705e-05 0.99977\n\nD: 2.6177 0.042575 -65.797\n0.74359 2.3954 -903.27\n0.0018892 8.2816e-05 0.98996\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.43124 0.047668 -66.525\n-0.34772 0.62068 209.27\n-0.00060194 -2.1104e-07 0.98648\n\nB: 0.2564 0.092521 94.187\n-0.28031 0.83589 -0.15652\n-0.00048968 6.0866e-05 1.0015\n\nC: 1.1884 0.015274 95.776\n0.23282 1.0681 -20.551\n0.00097623 0.00015903 1.0014\n\nD: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_189_0.png", "2D-spatial/Homography_estimation/Homography_estimation_189_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.43124 0.047668 -66.525\n-0.34772 0.62068 209.27\n-0.00060194 -2.1104e-07 0.98648\n\nB: 0.2564 0.092521 94.187\n-0.28031 0.83589 -0.15652\n-0.00048968 6.0866e-05 1.0015\n\nC: 1.1884 0.015274 95.776\n0.23282 1.0681 -20.551\n0.00097623 0.00015903 1.0014\n\nD: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.8851 0.028166 274.85\n0.48185 1.6951 -326.97\n0.0011778 8.455e-05 0.99801\n\nB: 1.1943 0.010001 372.77\n0.22686 1.0937 -67.914\n0.00058802 5.2037e-05 0.99941\n\nC: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n\nD: 1.1884 0.015274 95.776\n0.23282 1.0681 -20.551\n0.00097623 0.00015903 1.0014\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_190_0.png", "2D-spatial/Homography_estimation/Homography_estimation_190_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.8851 0.028166 274.85\n0.48185 1.6951 -326.97\n0.0011778 8.455e-05 0.99801\n\nB: 1.1943 0.010001 372.77\n0.22686 1.0937 -67.914\n0.00058802 5.2037e-05 0.99941\n\nC: 0.66581 0.6777 -31.246\n-0.14346 0.96853 148.92\n0.00042869 -1.7355e-05 0.99928\n\nD: 1.1884 0.015274 95.776\n0.23282 1.0681 -20.551\n0.00097623 0.00015903 1.0014\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nB: 0.49202 0.0057754 242.06\n0.058005 0.43541 166.02\n0.00018017 1.0746e-05 0.99974\n\nC: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n\nD: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_191_0.png", "2D-spatial/Homography_estimation/Homography_estimation_191_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0582 -0.013384 562.45\n0.1807 0.93712 36.472\n0.00043718 5.9368e-06 0.99927\n\nB: 0.49202 0.0057754 242.06\n0.058005 0.43541 166.02\n0.00018017 1.0746e-05 0.99974\n\nC: 0.72201 0.13445 62.975\n0.059719 0.85126 46.305\n-1.7322e-05 0.00018166 1.0001\n\nD: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n"}, "output": {"output_text": "A"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nB: 1.2895 0.43518 -118.46\n-0.025956 1.4233 161.89\n-3.0413e-05 0.00069874 1.0013\n\nC: 2.9599 0.00703 244.64\n0.78405 1.8789 -438.29\n0.0018411 4.4095e-05 0.99694\n\nD: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_192_0.png", "2D-spatial/Homography_estimation/Homography_estimation_192_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.60665 -0.013034 217.78\n0.087451 0.52146 32.707\n0.00021516 2.9281e-07 1.0006\n\nB: 1.2895 0.43518 -118.46\n-0.025956 1.4233 161.89\n-3.0413e-05 0.00069874 1.0013\n\nC: 2.9599 0.00703 244.64\n0.78405 1.8789 -438.29\n0.0018411 4.4095e-05 0.99694\n\nD: 0.4849 -0.15095 280.72\n-0.18568 0.38797 170.57\n-4.9965e-05 -0.00024428 0.99985\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n\nB: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nC: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n\nD: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_193_0.png", "2D-spatial/Homography_estimation/Homography_estimation_193_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n\nB: 1.8278 -0.0075993 72.268\n0.68643 1.8832 -550.61\n0.0012853 4.1209e-05 1.006\n\nC: 1.2869 -0.0035671 90.117\n0.34981 1.1421 -290.48\n0.0010338 2.5575e-05 0.99928\n\nD: 1.7312 -0.086578 129.17\n0.3882 1.1026 -2.2164\n0.0010948 -0.00011788 1.0024\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n\nB: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nC: 14.984 -1.5209 -1987.5\n0.59203 13.878 -3896.8\n0.0072047 0.0038814 0.92614\n\nD: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_194_0.png", "2D-spatial/Homography_estimation/Homography_estimation_194_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n\nB: 2.2787 0.023843 -30.321\n0.58793 1.9158 -459.28\n0.0012782 -6.6868e-06 0.99971\n\nC: 14.984 -1.5209 -1987.5\n0.59203 13.878 -3896.8\n0.0072047 0.0038814 0.92614\n\nD: 0.7855 0.039826 119.05\n-0.25749 1.3451 -220.69\n-0.00047304 5.3677e-05 1.001\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nB: 1.0505 -0.0053825 276.45\n0.20631 0.92888 48.832\n0.00048841 -1.9251e-05 0.99878\n\nC: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nD: 1.3526 0.026797 436.87\n0.31517 1.3826 -234.04\n0.00076901 0.00022984 1.0039\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_195_0.png", "2D-spatial/Homography_estimation/Homography_estimation_195_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n\nB: 1.0505 -0.0053825 276.45\n0.20631 0.92888 48.832\n0.00048841 -1.9251e-05 0.99878\n\nC: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nD: 1.3526 0.026797 436.87\n0.31517 1.3826 -234.04\n0.00076901 0.00022984 1.0039\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n\nB: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n\nC: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n\nD: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_196_0.png", "2D-spatial/Homography_estimation/Homography_estimation_196_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 3.6199 0.1243 -2.4307\n0.35256 5.1536 -1935.2\n0.0029372 0.0011148 1\n\nB: 0.47589 0.042551 60.888\n-0.21388 0.80238 62.033\n-0.0003663 2.6901e-05 1.001\n\nC: 1.0819 0.012805 66.799\n0.075853 1.006 5.6909\n0.00034273 -2.4626e-05 1.0003\n\nD: 1.3231 -0.10518 226.69\n0.35118 1.4445 -217.52\n0.00076877 -2.4515e-05 0.99903\n"}, "output": {"output_text": "B"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n\nB: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nC: 0.57125 -0.095863 127.19\n0.050302 0.75099 -13.911\n-0.00020485 1.2421e-06 0.9999\n\nD: 0.14586 0.056449 119.48\n-0.21737 0.71439 95.786\n-0.00051182 3.3282e-05 1.0008\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_197_0.png", "2D-spatial/Homography_estimation/Homography_estimation_197_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n\nB: 1.4259 0.070724 58.865\n0.39243 1.3442 -170.04\n0.00084248 0.00011346 0.98851\n\nC: 0.57125 -0.095863 127.19\n0.050302 0.75099 -13.911\n-0.00020485 1.2421e-06 0.9999\n\nD: 0.14586 0.056449 119.48\n-0.21737 0.71439 95.786\n-0.00051182 3.3282e-05 1.0008\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n\nC: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n\nD: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_198_0.png", "2D-spatial/Homography_estimation/Homography_estimation_198_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 0.37083 -0.024499 139.16\n-0.094573 0.62749 65.353\n-0.00053805 -2.2225e-05 0.99885\n\nC: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n\nD: 0.60367 0.071352 -36.528\n-0.21232 0.96671 -45.299\n-0.00036835 6.7456e-05 0.99996\n"}, "output": {"output_text": "C"}, "task": "Homography_estimation"}
{"source": "Hpatches", "options": "A: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 0.67783 0.002447 123\n-0.00051063 0.68091 83.563\n-2.5166e-06 5.6486e-06 1\n\nC: 0.46461 0.085196 589.33\n0.19659 0.76327 25.833\n0.00026763 8.9486e-05 1.0006\n\nD: 0.040904 -0.0023332 234.76\n-0.10713 0.35038 218.5\n-0.00028907 6.311e-06 1.0035\n", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Homography_estimation/Homography_estimation_199_0.png", "2D-spatial/Homography_estimation/Homography_estimation_199_1.png"], "question": "Please compute the 3x3 homography matrix between these two images.", "context": "Your task is computing the 3x3 homography matrix that maps the coordinates of points in one image to their corresponding coordinates in another image. (Two images of the same planar.)\nSelect from the following choices.\nA: 0.29858 0.0403 -122.67\n-0.38113 0.61838 172.03\n-0.00071255 -1.0448e-06 0.97348\n\nB: 0.67783 0.002447 123\n-0.00051063 0.68091 83.563\n-2.5166e-06 5.6486e-06 1\n\nC: 0.46461 0.085196 589.33\n0.19659 0.76327 25.833\n0.00026763 8.9486e-05 1.0006\n\nD: 0.040904 -0.0023332 234.76\n-0.10713 0.35038 218.5\n-0.00028907 6.311e-06 1.0035\n"}, "output": {"output_text": "D"}, "task": "Homography_estimation"}
{"source": "ovis_sot", "options": "A: [0.105, 0.0, 0.539, 1.0]\nB: [0.231, 0.444, 0.698, 0.771]\nC: [0.204, 0.496, 0.49, 0.761]\nD: [0.105, 0.0, 0.624, 0.922]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_0_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_0_1.jpg"], "question": "Here is an object ([0.166, 0.0, 0.589, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.105, 0.0, 0.539, 1.0]\nB: [0.231, 0.444, 0.698, 0.771]\nC: [0.204, 0.496, 0.49, 0.761]\nD: [0.105, 0.0, 0.624, 0.922]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.37, 0.132, 0.788, 0.61]\nB: [0.457, 0.328, 0.655, 0.681]\nC: [0.457, 0.328, 0.673, 0.635]\nD: [0.457, 0.328, 0.656, 0.582]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_1_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_1_1.jpg"], "question": "Here is an object ([0.326, 0.224, 0.691, 0.644]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.37, 0.132, 0.788, 0.61]\nB: [0.457, 0.328, 0.655, 0.681]\nC: [0.457, 0.328, 0.673, 0.635]\nD: [0.457, 0.328, 0.656, 0.582]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.546, 0.242, 0.943, 1.0]\nB: [0.173, 0.0, 0.57, 0.758]\nC: [0.516, 0.2, 0.912, 0.958]\nD: [0.367, 0.242, 0.764, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_2_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_2_1.jpg"], "question": "Here is an object ([0.358, 0.26, 0.744, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.546, 0.242, 0.943, 1.0]\nB: [0.173, 0.0, 0.57, 0.758]\nC: [0.516, 0.2, 0.912, 0.958]\nD: [0.367, 0.242, 0.764, 1.0]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.302, 0.299, 0.571, 1.0]\nB: [0.802, 0.301, 0.919, 0.514]\nC: [0.302, 0.299, 0.607, 0.882]\nD: [0.255, 0.124, 0.525, 0.825]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_3_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_3_1.jpg"], "question": "Here is an object ([0.649, 0.335, 0.85, 0.992]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.302, 0.299, 0.571, 1.0]\nB: [0.802, 0.301, 0.919, 0.514]\nC: [0.302, 0.299, 0.607, 0.882]\nD: [0.255, 0.124, 0.525, 0.825]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.901, 0.0, 1.0, 0.304]\nB: [0.394, 0.317, 0.758, 0.617]\nC: [0.389, 0.294, 0.495, 0.551]\nD: [0.901, 0.0, 0.994, 0.3]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_4_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_4_1.jpg"], "question": "Here is an object ([0.832, 0.0, 0.977, 0.472]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.901, 0.0, 1.0, 0.304]\nB: [0.394, 0.317, 0.758, 0.617]\nC: [0.389, 0.294, 0.495, 0.551]\nD: [0.901, 0.0, 0.994, 0.3]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.987, 0.792, 0.998, 0.876]\nB: [0.987, 0.792, 0.998, 0.871]\nC: [0.987, 0.792, 1.0, 0.892]\nD: [0.987, 0.792, 1.002, 0.901]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_5_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_5_1.jpg"], "question": "Here is an object ([0.952, 0.703, 1.0, 0.904]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.987, 0.792, 0.998, 0.876]\nB: [0.987, 0.792, 0.998, 0.871]\nC: [0.987, 0.792, 1.0, 0.892]\nD: [0.987, 0.792, 1.002, 0.901]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.287, 0.453, 0.617, 0.774]\nB: [0.384, 0.432, 0.713, 0.753]\nC: [0.287, 0.453, 0.623, 0.828]\nD: [0.26, 0.356, 0.59, 0.676]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_6_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_6_1.jpg"], "question": "Here is an object ([0.284, 0.369, 0.636, 0.674]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.287, 0.453, 0.617, 0.774]\nB: [0.384, 0.432, 0.713, 0.753]\nC: [0.287, 0.453, 0.623, 0.828]\nD: [0.26, 0.356, 0.59, 0.676]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.282, 0.0, 1.0, 0.736]\nB: [0.282, 0.0, 1.047, 0.79]\nC: [0.248, 0.156, 0.966, 0.892]\nD: [0.186, 0.067, 0.904, 0.803]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_7_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_7_1.jpg"], "question": "Here is an object ([0.312, 0.0, 1.0, 0.736]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.282, 0.0, 1.0, 0.736]\nB: [0.282, 0.0, 1.047, 0.79]\nC: [0.248, 0.156, 0.966, 0.892]\nD: [0.186, 0.067, 0.904, 0.803]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.863, 0.157, 0.884, 0.624]\nB: [0.159, 0.19, 0.68, 1.0]\nC: [0.159, 0.19, 0.737, 1.156]\nD: [0.159, 0.19, 0.72, 1.014]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_8_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_8_1.jpg"], "question": "Here is an object ([0.174, 0.19, 0.691, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.863, 0.157, 0.884, 0.624]\nB: [0.159, 0.19, 0.68, 1.0]\nC: [0.159, 0.19, 0.737, 1.156]\nD: [0.159, 0.19, 0.72, 1.014]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.309, 0.435, 0.693, 1.0]\nB: [0.786, 0.633, 0.927, 0.776]\nC: [0.263, 0.193, 0.647, 0.758]\nD: [0.263, 0.193, 0.617, 0.678]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_9_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_9_1.jpg"], "question": "Here is an object ([0.227, 0.218, 0.607, 0.787]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.309, 0.435, 0.693, 1.0]\nB: [0.786, 0.633, 0.927, 0.776]\nC: [0.263, 0.193, 0.647, 0.758]\nD: [0.263, 0.193, 0.617, 0.678]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.013, 0.201, 0.494, 0.7]\nB: [0.177, 0.003, 0.868, 1.0]\nC: [0.129, 0.61, 0.238, 0.793]\nD: [0.243, 0.0, 0.934, 0.997]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_10_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_10_1.jpg"], "question": "Here is an object ([0.125, 0.0, 0.804, 0.988]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.013, 0.201, 0.494, 0.7]\nB: [0.177, 0.003, 0.868, 1.0]\nC: [0.129, 0.61, 0.238, 0.793]\nD: [0.243, 0.0, 0.934, 0.997]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.095, 0.672, 0.513, 0.838]\nB: [0.0, 0.257, 0.27, 1.056]\nC: [0.096, 0.265, 0.371, 1.0]\nD: [0.0, 0.257, 0.275, 0.992]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_11_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_11_1.jpg"], "question": "Here is an object ([0.0, 0.261, 0.28, 0.997]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.095, 0.672, 0.513, 0.838]\nB: [0.0, 0.257, 0.27, 1.056]\nC: [0.096, 0.265, 0.371, 1.0]\nD: [0.0, 0.257, 0.275, 0.992]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.173, 0.05, 0.833, 1.1]\nB: [0.173, 0.05, 0.754, 1.04]\nC: [0.173, 0.05, 0.789, 1.0]\nD: [0.173, 0.05, 0.712, 0.936]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_12_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_12_1.jpg"], "question": "Here is an object ([0.223, 0.032, 0.773, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.173, 0.05, 0.833, 1.1]\nB: [0.173, 0.05, 0.754, 1.04]\nC: [0.173, 0.05, 0.789, 1.0]\nD: [0.173, 0.05, 0.712, 0.936]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.042, 0.523, 1.0]\nB: [0.0, 0.042, 0.434, 0.851]\nC: [0.205, 0.0, 0.728, 0.958]\nD: [0.259, 0.042, 0.782, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_13_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_13_1.jpg"], "question": "Here is an object ([0.116, 0.024, 0.778, 0.988]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.042, 0.523, 1.0]\nB: [0.0, 0.042, 0.434, 0.851]\nC: [0.205, 0.0, 0.728, 0.958]\nD: [0.259, 0.042, 0.782, 1.0]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.177, 0.135, 0.627, 0.492]\nB: [0.442, 0.412, 0.921, 0.872]\nC: [0.249, 0.269, 0.701, 0.599]\nD: [0.241, 0.326, 0.693, 0.656]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_14_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_14_1.jpg"], "question": "Here is an object ([0.232, 0.326, 0.684, 0.656]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.177, 0.135, 0.627, 0.492]\nB: [0.442, 0.412, 0.921, 0.872]\nC: [0.249, 0.269, 0.701, 0.599]\nD: [0.241, 0.326, 0.693, 0.656]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.124, 0.142, 0.57, 0.851]\nB: [0.124, 0.142, 0.712, 1.079]\nC: [0.124, 0.142, 0.643, 0.931]\nD: [0.445, 0.485, 0.785, 0.751]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_15_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_15_1.jpg"], "question": "Here is an object ([0.123, 0.161, 0.635, 0.931]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.124, 0.142, 0.57, 0.851]\nB: [0.124, 0.142, 0.712, 1.079]\nC: [0.124, 0.142, 0.643, 0.931]\nD: [0.445, 0.485, 0.785, 0.751]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.337, 0.263, 0.374, 0.406]\nB: [0.325, 0.269, 0.362, 0.412]\nC: [0.325, 0.269, 0.362, 0.392]\nD: [0.265, 0.403, 0.642, 0.54]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_16_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_16_1.jpg"], "question": "Here is an object ([0.311, 0.271, 0.363, 0.412]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.337, 0.263, 0.374, 0.406]\nB: [0.325, 0.269, 0.362, 0.412]\nC: [0.325, 0.269, 0.362, 0.392]\nD: [0.265, 0.403, 0.642, 0.54]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.07, 0.04, 0.201, 0.126]\nB: [0.326, 0.0, 0.671, 0.593]\nC: [0.326, 0.0, 0.797, 0.729]\nD: [0.326, 0.0, 0.73, 0.738]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_17_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_17_1.jpg"], "question": "Here is an object ([0.37, 0.0, 0.701, 0.883]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.07, 0.04, 0.201, 0.126]\nB: [0.326, 0.0, 0.671, 0.593]\nC: [0.326, 0.0, 0.797, 0.729]\nD: [0.326, 0.0, 0.73, 0.738]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.467, 0.647, 0.685, 0.889]\nB: [0.494, 0.543, 0.712, 0.785]\nC: [0.649, 0.751, 0.892, 0.776]\nD: [0.494, 0.543, 0.677, 0.764]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_18_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_18_1.jpg"], "question": "Here is an object ([0.523, 0.457, 0.773, 0.708]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.467, 0.647, 0.685, 0.889]\nB: [0.494, 0.543, 0.712, 0.785]\nC: [0.649, 0.751, 0.892, 0.776]\nD: [0.494, 0.543, 0.677, 0.764]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.0, 0.0, 0.879, 0.878]\nB: [0.0, 0.0, 0.892, 0.821]\nC: [0.0, 0.0, 0.992, 0.739]\nD: [0.0, 0.0, 0.894, 1.05]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_19_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_19_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.883, 0.86]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.0, 0.879, 0.878]\nB: [0.0, 0.0, 0.892, 0.821]\nC: [0.0, 0.0, 0.992, 0.739]\nD: [0.0, 0.0, 0.894, 1.05]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.005, 0.846, 0.023, 0.993]\nB: [0.005, 0.846, 0.023, 0.994]\nC: [0.005, 0.846, 0.02, 0.965]\nD: [0.311, 0.061, 0.434, 0.287]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_20_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_20_1.jpg"], "question": "Here is an object ([0.0, 0.8, 0.043, 0.996]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.005, 0.846, 0.023, 0.993]\nB: [0.005, 0.846, 0.023, 0.994]\nC: [0.005, 0.846, 0.02, 0.965]\nD: [0.311, 0.061, 0.434, 0.287]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.323, 0.332, 0.442, 0.565]\nB: [0.323, 0.332, 0.47, 0.558]\nC: [0.323, 0.332, 0.495, 0.537]\nD: [0.323, 0.332, 0.477, 0.526]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_21_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_21_1.jpg"], "question": "Here is an object ([0.0, 0.05, 0.374, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.323, 0.332, 0.442, 0.565]\nB: [0.323, 0.332, 0.47, 0.558]\nC: [0.323, 0.332, 0.495, 0.537]\nD: [0.323, 0.332, 0.477, 0.526]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.292, 0.053, 0.432, 0.251]\nB: [0.27, 0.118, 0.41, 0.317]\nC: [0.27, 0.049, 0.409, 0.247]\nD: [0.689, 0.281, 0.863, 0.306]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_22_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_22_1.jpg"], "question": "Here is an object ([0.245, 0.086, 0.4, 0.275]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.292, 0.053, 0.432, 0.251]\nB: [0.27, 0.118, 0.41, 0.317]\nC: [0.27, 0.049, 0.409, 0.247]\nD: [0.689, 0.281, 0.863, 0.306]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.322, 0.804, 0.577, 0.947]\nB: [0.353, 0.024, 0.68, 0.975]\nC: [0.353, 0.024, 0.669, 0.874]\nD: [0.733, 0.014, 0.774, 0.058]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_23_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_23_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.524, 0.828]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.322, 0.804, 0.577, 0.947]\nB: [0.353, 0.024, 0.68, 0.975]\nC: [0.353, 0.024, 0.669, 0.874]\nD: [0.733, 0.014, 0.774, 0.058]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.184, 0.0, 0.909, 0.993]\nB: [0.184, 0.0, 1.027, 0.949]\nC: [0.199, 0.0, 0.924, 0.993]\nD: [0.184, 0.0, 1.048, 0.854]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_24_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_24_1.jpg"], "question": "Here is an object ([0.086, 0.0, 0.87, 0.919]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.184, 0.0, 0.909, 0.993]\nB: [0.184, 0.0, 1.027, 0.949]\nC: [0.199, 0.0, 0.924, 0.993]\nD: [0.184, 0.0, 1.048, 0.854]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.047, 0.869, 0.259, 0.904]\nB: [0.443, 0.679, 0.677, 1.011]\nC: [0.443, 0.679, 0.708, 0.976]\nD: [0.566, 0.703, 0.831, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_25_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_25_1.jpg"], "question": "Here is an object ([0.441, 0.71, 0.689, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.047, 0.869, 0.259, 0.904]\nB: [0.443, 0.679, 0.677, 1.011]\nC: [0.443, 0.679, 0.708, 0.976]\nD: [0.566, 0.703, 0.831, 1.0]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.001, 0.45, 1.0]\nB: [0.173, 0.001, 0.623, 1.0]\nC: [0.0, 0.001, 0.441, 1.01]\nD: [0.0, 0.001, 0.377, 1.032]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_26_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_26_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.402, 0.996]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.001, 0.45, 1.0]\nB: [0.173, 0.001, 0.623, 1.0]\nC: [0.0, 0.001, 0.441, 1.01]\nD: [0.0, 0.001, 0.377, 1.032]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.285, 0.181, 0.881, 0.531]\nB: [0.325, 0.09, 0.738, 0.329]\nC: [0.6, 0.492, 0.78, 0.658]\nD: [0.285, 0.181, 0.992, 0.608]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_27_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_27_1.jpg"], "question": "Here is an object ([0.277, 0.196, 0.994, 0.61]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.285, 0.181, 0.881, 0.531]\nB: [0.325, 0.09, 0.738, 0.329]\nC: [0.6, 0.492, 0.78, 0.658]\nD: [0.285, 0.181, 0.992, 0.608]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.596, 0.311, 0.855, 0.971]\nB: [0.656, 0.222, 0.916, 0.882]\nC: [0.181, 0.185, 0.651, 0.349]\nD: [0.57, 0.24, 0.83, 0.9]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_28_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_28_1.jpg"], "question": "Here is an object ([0.67, 0.219, 0.91, 0.886]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.596, 0.311, 0.855, 0.971]\nB: [0.656, 0.222, 0.916, 0.882]\nC: [0.181, 0.185, 0.651, 0.349]\nD: [0.57, 0.24, 0.83, 0.9]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.33, 0.006, 0.963, 0.889]\nB: [0.427, 0.299, 0.457, 0.435]\nC: [0.323, 0.642, 0.555, 0.656]\nD: [0.33, 0.006, 0.966, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_29_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_29_1.jpg"], "question": "Here is an object ([0.304, 0.001, 0.951, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.33, 0.006, 0.963, 0.889]\nB: [0.427, 0.299, 0.457, 0.435]\nC: [0.323, 0.642, 0.555, 0.656]\nD: [0.33, 0.006, 0.966, 1.0]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.298, 0.661, 0.788, 0.753]\nB: [0.0, 0.0, 0.955, 0.996]\nC: [0.502, 0.29, 0.769, 0.553]\nD: [0.0, 0.004, 0.955, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_30_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_30_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.893, 0.999]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.298, 0.661, 0.788, 0.753]\nB: [0.0, 0.0, 0.955, 0.996]\nC: [0.502, 0.29, 0.769, 0.553]\nD: [0.0, 0.004, 0.955, 1.0]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.378, 0.415, 0.458, 0.821]\nB: [0.378, 0.415, 0.467, 0.843]\nC: [0.378, 0.415, 0.462, 0.858]\nD: [0.378, 0.415, 0.473, 0.764]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_31_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_31_1.jpg"], "question": "Here is an object ([0.366, 0.428, 0.459, 0.826]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.378, 0.415, 0.458, 0.821]\nB: [0.378, 0.415, 0.467, 0.843]\nC: [0.378, 0.415, 0.462, 0.858]\nD: [0.378, 0.415, 0.473, 0.764]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.266, 0.356, 0.714, 0.89]\nB: [0.255, 0.425, 0.769, 0.932]\nC: [0.266, 0.356, 0.78, 0.863]\nD: [0.33, 0.275, 0.54, 0.724]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_32_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_32_1.jpg"], "question": "Here is an object ([0.268, 0.399, 0.774, 0.89]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.266, 0.356, 0.714, 0.89]\nB: [0.255, 0.425, 0.769, 0.932]\nC: [0.266, 0.356, 0.78, 0.863]\nD: [0.33, 0.275, 0.54, 0.724]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.878, 0.772, 0.995, 0.833]\nB: [0.134, 0.675, 0.455, 0.933]\nC: [0.397, 0.556, 0.869, 0.714]\nD: [0.134, 0.675, 0.518, 0.892]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_33_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_33_1.jpg"], "question": "Here is an object ([0.108, 0.626, 0.434, 0.892]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.878, 0.772, 0.995, 0.833]\nB: [0.134, 0.675, 0.455, 0.933]\nC: [0.397, 0.556, 0.869, 0.714]\nD: [0.134, 0.675, 0.518, 0.892]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.564, 0.332, 0.63, 0.44]\nB: [0.545, 0.307, 0.611, 0.415]\nC: [0.528, 0.263, 0.595, 0.371]\nD: [0.547, 0.319, 0.613, 0.428]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_34_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_34_1.jpg"], "question": "Here is an object ([0.593, 0.332, 0.659, 0.447]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.564, 0.332, 0.63, 0.44]\nB: [0.545, 0.307, 0.611, 0.415]\nC: [0.528, 0.263, 0.595, 0.371]\nD: [0.547, 0.319, 0.613, 0.428]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.217, 0.24, 0.541, 0.761]\nB: [0.119, 0.435, 0.442, 0.956]\nC: [0.217, 0.24, 0.478, 0.786]\nD: [0.138, 0.474, 0.461, 0.994]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_35_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_35_1.jpg"], "question": "Here is an object ([0.23, 0.247, 0.55, 0.715]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.217, 0.24, 0.541, 0.761]\nB: [0.119, 0.435, 0.442, 0.956]\nC: [0.217, 0.24, 0.478, 0.786]\nD: [0.138, 0.474, 0.461, 0.994]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.003, 0.11, 0.245, 0.188]\nB: [0.0, 0.0, 0.334, 0.435]\nC: [0.0, 0.0, 0.31, 0.529]\nD: [0.0, 0.0, 0.304, 0.487]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_36_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_36_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.263, 0.576]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.003, 0.11, 0.245, 0.188]\nB: [0.0, 0.0, 0.334, 0.435]\nC: [0.0, 0.0, 0.31, 0.529]\nD: [0.0, 0.0, 0.304, 0.487]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.317, 1.094, 0.986]\nB: [0.428, 0.043, 0.832, 0.14]\nC: [0.0, 0.368, 1.0, 0.989]\nD: [0.0, 0.317, 1.0, 0.938]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_37_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_37_1.jpg"], "question": "Here is an object ([0.609, 0.0, 0.853, 0.433]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.317, 1.094, 0.986]\nB: [0.428, 0.043, 0.832, 0.14]\nC: [0.0, 0.368, 1.0, 0.989]\nD: [0.0, 0.317, 1.0, 0.938]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.477, 0.165, 0.613, 0.461]\nB: [0.477, 0.083, 0.614, 0.379]\nC: [0.486, 0.0, 0.623, 0.296]\nD: [0.18, 0.044, 0.549, 0.249]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_38_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_38_1.jpg"], "question": "Here is an object ([0.469, 0.09, 0.59, 0.317]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.477, 0.165, 0.613, 0.461]\nB: [0.477, 0.083, 0.614, 0.379]\nC: [0.486, 0.0, 0.623, 0.296]\nD: [0.18, 0.044, 0.549, 0.249]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.204, 0.226, 0.796, 0.667]\nB: [0.85, 0.326, 0.93, 0.539]\nC: [0.317, 0.108, 0.652, 0.579]\nD: [0.204, 0.226, 0.846, 0.713]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_39_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_39_1.jpg"], "question": "Here is an object ([0.187, 0.107, 0.821, 0.719]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.204, 0.226, 0.796, 0.667]\nB: [0.85, 0.326, 0.93, 0.539]\nC: [0.317, 0.108, 0.652, 0.579]\nD: [0.204, 0.226, 0.846, 0.713]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.222, 0.174, 0.717, 1.0]\nB: [0.369, 0.033, 0.864, 0.86]\nC: [0.403, 0.0, 0.898, 0.826]\nD: [0.105, 0.729, 0.198, 0.839]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_40_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_40_1.jpg"], "question": "Here is an object ([0.263, 0.168, 0.714, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.222, 0.174, 0.717, 1.0]\nB: [0.369, 0.033, 0.864, 0.86]\nC: [0.403, 0.0, 0.898, 0.826]\nD: [0.105, 0.729, 0.198, 0.839]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.101, 0.694, 0.357, 0.807]\nB: [0.592, 0.454, 0.698, 0.651]\nC: [0.592, 0.454, 0.694, 0.631]\nD: [0.34, 0.282, 0.835, 0.693]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_41_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_41_1.jpg"], "question": "Here is an object ([0.541, 0.482, 0.603, 0.624]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.101, 0.694, 0.357, 0.807]\nB: [0.592, 0.454, 0.698, 0.651]\nC: [0.592, 0.454, 0.694, 0.631]\nD: [0.34, 0.282, 0.835, 0.693]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.398, 0.606, 0.528, 0.767]\nB: [0.398, 0.606, 0.509, 0.774]\nC: [0.398, 0.606, 0.507, 0.794]\nD: [0.384, 0.192, 0.498, 0.551]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_42_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_42_1.jpg"], "question": "Here is an object ([0.359, 0.608, 0.466, 0.8]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.398, 0.606, 0.528, 0.767]\nB: [0.398, 0.606, 0.509, 0.774]\nC: [0.398, 0.606, 0.507, 0.794]\nD: [0.384, 0.192, 0.498, 0.551]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.216, 0.225, 0.783, 0.904]\nB: [0.216, 0.225, 0.738, 1.065]\nC: [0.216, 0.225, 0.701, 1.0]\nD: [0.216, 0.225, 0.73, 1.015]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_43_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_43_1.jpg"], "question": "Here is an object ([0.226, 0.208, 0.703, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.216, 0.225, 0.783, 0.904]\nB: [0.216, 0.225, 0.738, 1.065]\nC: [0.216, 0.225, 0.701, 1.0]\nD: [0.216, 0.225, 0.73, 1.015]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.425, 0.474, 0.487, 0.668]\nB: [0.425, 0.474, 0.493, 0.706]\nC: [0.425, 0.474, 0.496, 0.647]\nD: [0.439, 0.428, 0.502, 0.622]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_44_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_44_1.jpg"], "question": "Here is an object ([0.417, 0.481, 0.48, 0.7]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.425, 0.474, 0.487, 0.668]\nB: [0.425, 0.474, 0.493, 0.706]\nC: [0.425, 0.474, 0.496, 0.647]\nD: [0.439, 0.428, 0.502, 0.622]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.059, 0.0, 0.673, 0.483]\nB: [0.0, 0.0, 0.576, 0.44]\nC: [0.123, 0.692, 0.539, 0.775]\nD: [0.059, 0.0, 0.634, 0.44]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_45_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_45_1.jpg"], "question": "Here is an object ([0.203, 0.0, 0.616, 0.404]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.059, 0.0, 0.673, 0.483]\nB: [0.0, 0.0, 0.576, 0.44]\nC: [0.123, 0.692, 0.539, 0.775]\nD: [0.059, 0.0, 0.634, 0.44]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.541, 0.742, 0.866, 0.985]\nB: [0.602, 0.447, 0.882, 0.914]\nC: [0.471, 0.222, 0.751, 0.689]\nD: [0.471, 0.222, 0.738, 0.765]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_46_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_46_1.jpg"], "question": "Here is an object ([0.479, 0.236, 0.73, 0.683]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.541, 0.742, 0.866, 0.985]\nB: [0.602, 0.447, 0.882, 0.914]\nC: [0.471, 0.222, 0.751, 0.689]\nD: [0.471, 0.222, 0.738, 0.765]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.201, 0.0, 0.727, 0.497]\nB: [0.201, 0.0, 0.724, 0.519]\nC: [0.201, 0.0, 0.67, 0.574]\nD: [0.201, 0.0, 0.666, 0.542]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_47_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_47_1.jpg"], "question": "Here is an object ([0.152, 0.0, 0.666, 0.521]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.201, 0.0, 0.727, 0.497]\nB: [0.201, 0.0, 0.724, 0.519]\nC: [0.201, 0.0, 0.67, 0.574]\nD: [0.201, 0.0, 0.666, 0.542]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.03, 0.099, 0.872, 1.0]\nB: [0.093, 0.028, 0.167, 0.235]\nC: [0.158, 0.0, 1.0, 0.901]\nD: [0.158, 0.099, 1.0, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_48_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_48_1.jpg"], "question": "Here is an object ([0.044, 0.067, 0.886, 0.978]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.03, 0.099, 0.872, 1.0]\nB: [0.093, 0.028, 0.167, 0.235]\nC: [0.158, 0.0, 1.0, 0.901]\nD: [0.158, 0.099, 1.0, 1.0]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.285, 0.124, 0.72, 0.76]\nB: [0.463, 0.321, 0.897, 0.957]\nC: [0.372, 0.103, 0.648, 0.493]\nD: [0.285, 0.124, 0.778, 0.754]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_49_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_49_1.jpg"], "question": "Here is an object ([0.282, 0.122, 0.711, 0.85]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.285, 0.124, 0.72, 0.76]\nB: [0.463, 0.321, 0.897, 0.957]\nC: [0.372, 0.103, 0.648, 0.493]\nD: [0.285, 0.124, 0.778, 0.754]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.259, 0.365, 0.986, 0.932]\nB: [0.01, 0.172, 0.737, 0.739]\nC: [0.273, 0.2, 1.0, 0.767]\nD: [0.091, 0.126, 0.818, 0.693]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_50_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_50_1.jpg"], "question": "Here is an object ([0.291, 0.342, 0.989, 0.933]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.259, 0.365, 0.986, 0.932]\nB: [0.01, 0.172, 0.737, 0.739]\nC: [0.273, 0.2, 1.0, 0.767]\nD: [0.091, 0.126, 0.818, 0.693]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.152, 0.16, 0.498, 0.782]\nB: [0.28, 0.342, 0.627, 0.964]\nC: [0.582, 0.299, 0.674, 0.576]\nD: [0.314, 0.397, 0.733, 0.424]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_51_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_51_1.jpg"], "question": "Here is an object ([0.072, 0.21, 0.463, 0.842]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.152, 0.16, 0.498, 0.782]\nB: [0.28, 0.342, 0.627, 0.964]\nC: [0.582, 0.299, 0.674, 0.576]\nD: [0.314, 0.397, 0.733, 0.424]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.269, 0.0, 1.13, 0.608]\nB: [0.163, 0.639, 0.546, 0.861]\nC: [0.069, 0.075, 0.8, 0.803]\nD: [0.269, 0.0, 1.0, 0.728]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_52_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_52_1.jpg"], "question": "Here is an object ([0.222, 0.0, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.269, 0.0, 1.13, 0.608]\nB: [0.163, 0.639, 0.546, 0.861]\nC: [0.069, 0.075, 0.8, 0.803]\nD: [0.269, 0.0, 1.0, 0.728]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.62, 0.253, 0.788, 0.403]\nB: [0.0, 0.097, 0.658, 0.765]\nC: [0.0, 0.0, 0.658, 0.668]\nD: [0.0, 0.097, 0.552, 0.667]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_53_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_53_1.jpg"], "question": "Here is an object ([0.0, 0.143, 0.421, 0.783]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.62, 0.253, 0.788, 0.403]\nB: [0.0, 0.097, 0.658, 0.765]\nC: [0.0, 0.0, 0.658, 0.668]\nD: [0.0, 0.097, 0.552, 0.667]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.582, 0.371, 1.0, 1.0]\nB: [0.544, 0.328, 0.894, 0.85]\nC: [0.205, 0.11, 0.595, 0.439]\nD: [0.544, 0.328, 0.962, 0.957]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_54_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_54_1.jpg"], "question": "Here is an object ([0.555, 0.332, 0.999, 0.976]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.582, 0.371, 1.0, 1.0]\nB: [0.544, 0.328, 0.894, 0.85]\nC: [0.205, 0.11, 0.595, 0.439]\nD: [0.544, 0.328, 0.962, 0.957]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.391, 0.686, 0.805, 0.978]\nB: [0.07, 0.022, 0.341, 0.492]\nC: [0.041, 0.082, 0.181, 0.229]\nD: [0.289, 0.708, 0.703, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_55_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_55_1.jpg"], "question": "Here is an object ([0.306, 0.303, 0.735, 0.643]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.391, 0.686, 0.805, 0.978]\nB: [0.07, 0.022, 0.341, 0.492]\nC: [0.041, 0.082, 0.181, 0.229]\nD: [0.289, 0.708, 0.703, 1.0]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.277, 0.308, 0.294, 0.386]\nB: [0.463, 0.725, 0.892, 0.968]\nC: [0.277, 0.274, 0.294, 0.351]\nD: [0.113, 0.29, 0.595, 0.572]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_56_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_56_1.jpg"], "question": "Here is an object ([0.277, 0.307, 0.298, 0.386]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.277, 0.308, 0.294, 0.386]\nB: [0.463, 0.725, 0.892, 0.968]\nC: [0.277, 0.274, 0.294, 0.351]\nD: [0.113, 0.29, 0.595, 0.572]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.296, 0.147, 0.609, 0.639]\nB: [0.296, 0.147, 0.559, 0.657]\nC: [0.296, 0.147, 0.628, 0.557]\nD: [0.296, 0.147, 0.656, 0.542]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_57_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_57_1.jpg"], "question": "Here is an object ([0.292, 0.154, 0.622, 0.629]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.296, 0.147, 0.609, 0.639]\nB: [0.296, 0.147, 0.559, 0.657]\nC: [0.296, 0.147, 0.628, 0.557]\nD: [0.296, 0.147, 0.656, 0.542]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.2, 0.249, 0.502, 0.331]\nB: [0.226, 0.244, 0.654, 0.808]\nC: [0.296, 0.436, 0.724, 1.0]\nD: [0.289, 0.436, 0.717, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_58_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_58_1.jpg"], "question": "Here is an object ([0.207, 0.207, 0.639, 0.775]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.2, 0.249, 0.502, 0.331]\nB: [0.226, 0.244, 0.654, 0.808]\nC: [0.296, 0.436, 0.724, 1.0]\nD: [0.289, 0.436, 0.717, 1.0]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.133, 0.075, 0.634, 1.0]\nB: [0.228, 0.069, 0.748, 0.904]\nC: [0.011, 0.0, 0.512, 0.925]\nD: [0.228, 0.069, 0.73, 0.994]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_59_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_59_1.jpg"], "question": "Here is an object ([0.227, 0.072, 0.729, 0.996]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.133, 0.075, 0.634, 1.0]\nB: [0.228, 0.069, 0.748, 0.904]\nC: [0.011, 0.0, 0.512, 0.925]\nD: [0.228, 0.069, 0.73, 0.994]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.616, 0.381, 0.995, 0.599]\nB: [0.62, 0.293, 1.0, 0.511]\nC: [0.62, 0.276, 1.0, 0.494]\nD: [0.616, 0.381, 0.992, 0.575]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_60_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_60_1.jpg"], "question": "Here is an object ([0.579, 0.451, 0.773, 0.635]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.616, 0.381, 0.995, 0.599]\nB: [0.62, 0.293, 1.0, 0.511]\nC: [0.62, 0.276, 1.0, 0.494]\nD: [0.616, 0.381, 0.992, 0.575]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.452, 0.315, 0.981, 0.608]\nB: [0.471, 0.414, 1.0, 0.707]\nC: [0.645, 0.575, 0.883, 0.993]\nD: [0.471, 0.414, 1.002, 0.722]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_61_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_61_1.jpg"], "question": "Here is an object ([0.427, 0.403, 1.0, 0.747]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.452, 0.315, 0.981, 0.608]\nB: [0.471, 0.414, 1.0, 0.707]\nC: [0.645, 0.575, 0.883, 0.993]\nD: [0.471, 0.414, 1.002, 0.722]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.473, 0.0, 0.755, 0.89]\nB: [0.411, 0.11, 0.694, 1.0]\nC: [0.411, 0.11, 0.677, 1.015]\nD: [0.411, 0.11, 0.737, 0.967]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_62_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_62_1.jpg"], "question": "Here is an object ([0.456, 0.044, 0.677, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.473, 0.0, 0.755, 0.89]\nB: [0.411, 0.11, 0.694, 1.0]\nC: [0.411, 0.11, 0.677, 1.015]\nD: [0.411, 0.11, 0.737, 0.967]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.31, 0.115, 0.683, 1.0]\nB: [0.216, 0.115, 0.589, 1.0]\nC: [0.367, 0.115, 0.74, 1.0]\nD: [0.455, 0.0, 0.827, 0.885]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_63_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_63_1.jpg"], "question": "Here is an object ([0.31, 0.121, 0.82, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.31, 0.115, 0.683, 1.0]\nB: [0.216, 0.115, 0.589, 1.0]\nC: [0.367, 0.115, 0.74, 1.0]\nD: [0.455, 0.0, 0.827, 0.885]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.252, 0.497, 0.337, 0.736]\nB: [0.29, 0.536, 0.373, 0.743]\nC: [0.442, 0.25, 0.609, 0.683]\nD: [0.252, 0.497, 0.334, 0.704]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_64_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_64_1.jpg"], "question": "Here is an object ([0.245, 0.492, 0.323, 0.704]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.252, 0.497, 0.337, 0.736]\nB: [0.29, 0.536, 0.373, 0.743]\nC: [0.442, 0.25, 0.609, 0.683]\nD: [0.252, 0.497, 0.334, 0.704]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.389, 0.19, 0.691, 0.878]\nB: [0.389, 0.19, 0.753, 0.963]\nC: [0.442, 0.512, 0.885, 0.811]\nD: [0.063, 0.44, 0.373, 0.589]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_65_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_65_1.jpg"], "question": "Here is an object ([0.397, 0.182, 0.833, 0.95]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.389, 0.19, 0.691, 0.878]\nB: [0.389, 0.19, 0.753, 0.963]\nC: [0.442, 0.512, 0.885, 0.811]\nD: [0.063, 0.44, 0.373, 0.589]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.529, 0.0, 0.904, 0.494]\nB: [0.529, 0.0, 0.939, 0.551]\nC: [0.52, 0.0, 0.93, 0.551]\nD: [0.331, 0.583, 0.744, 0.786]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_66_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_66_1.jpg"], "question": "Here is an object ([0.49, 0.0, 0.793, 0.537]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.529, 0.0, 0.904, 0.494]\nB: [0.529, 0.0, 0.939, 0.551]\nC: [0.52, 0.0, 0.93, 0.551]\nD: [0.331, 0.583, 0.744, 0.786]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.217, 0.232, 0.977, 1.054]\nB: [0.217, 0.232, 1.0, 1.0]\nC: [0.457, 0.535, 0.472, 0.564]\nD: [0.217, 0.232, 1.141, 1.086]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_67_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_67_1.jpg"], "question": "Here is an object ([0.18, 0.047, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.217, 0.232, 0.977, 1.054]\nB: [0.217, 0.232, 1.0, 1.0]\nC: [0.457, 0.535, 0.472, 0.564]\nD: [0.217, 0.232, 1.141, 1.086]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.53, 0.529, 0.565, 0.629]\nB: [0.536, 0.481, 0.57, 0.581]\nC: [0.53, 0.554, 0.564, 0.654]\nD: [0.514, 0.487, 0.548, 0.588]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_68_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_68_1.jpg"], "question": "Here is an object ([0.497, 0.794, 0.542, 0.875]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.53, 0.529, 0.565, 0.629]\nB: [0.536, 0.481, 0.57, 0.581]\nC: [0.53, 0.554, 0.564, 0.654]\nD: [0.514, 0.487, 0.548, 0.588]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.546, 0.211, 0.739, 1.0]\nB: [0.359, 0.875, 0.488, 0.932]\nC: [0.701, 0.114, 0.773, 0.361]\nD: [0.546, 0.211, 0.75, 1.131]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_69_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_69_1.jpg"], "question": "Here is an object ([0.492, 0.349, 0.672, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.546, 0.211, 0.739, 1.0]\nB: [0.359, 0.875, 0.488, 0.932]\nC: [0.701, 0.114, 0.773, 0.361]\nD: [0.546, 0.211, 0.75, 1.131]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.338, 0.222, 0.376, 0.406]\nB: [0.332, 0.132, 0.37, 0.315]\nC: [0.338, 0.222, 0.373, 0.375]\nD: [0.461, 0.596, 0.902, 0.999]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_70_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_70_1.jpg"], "question": "Here is an object ([0.28, 0.2, 0.309, 0.4]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.338, 0.222, 0.376, 0.406]\nB: [0.332, 0.132, 0.37, 0.315]\nC: [0.338, 0.222, 0.373, 0.375]\nD: [0.461, 0.596, 0.902, 0.999]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.016, 0.403, 0.025, 0.493]\nB: [0.373, 0.369, 0.523, 0.775]\nC: [0.373, 0.369, 0.531, 0.815]\nD: [0.328, 0.226, 0.478, 0.632]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_71_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_71_1.jpg"], "question": "Here is an object ([0.359, 0.286, 0.48, 0.728]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.016, 0.403, 0.025, 0.493]\nB: [0.373, 0.369, 0.523, 0.775]\nC: [0.373, 0.369, 0.531, 0.815]\nD: [0.328, 0.226, 0.478, 0.632]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.489, 0.412, 0.794, 0.653]\nB: [0.0, 0.0, 1.0, 1.0]\nC: [0.0, 0.0, 1.031, 1.006]\nD: [0.0, 0.0, 0.987, 1.135]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_72_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_72_1.jpg"], "question": "Here is an object ([0.0, 0.0, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.489, 0.412, 0.794, 0.653]\nB: [0.0, 0.0, 1.0, 1.0]\nC: [0.0, 0.0, 1.031, 1.006]\nD: [0.0, 0.0, 0.987, 1.135]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.607, 0.454, 0.948, 0.86]\nB: [0.589, 0.282, 0.745, 0.399]\nC: [0.095, 0.358, 0.78, 1.0]\nD: [0.0, 0.358, 0.686, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_73_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_73_1.jpg"], "question": "Here is an object ([0.0, 0.194, 0.704, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.607, 0.454, 0.948, 0.86]\nB: [0.589, 0.282, 0.745, 0.399]\nC: [0.095, 0.358, 0.78, 1.0]\nD: [0.0, 0.358, 0.686, 1.0]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.412, 0.367, 0.697, 0.811]\nB: [0.287, 0.19, 0.572, 0.635]\nC: [0.38, 0.192, 0.665, 0.636]\nD: [0.237, 0.568, 0.317, 0.772]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_74_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_74_1.jpg"], "question": "Here is an object ([0.397, 0.174, 0.659, 0.717]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.412, 0.367, 0.697, 0.811]\nB: [0.287, 0.19, 0.572, 0.635]\nC: [0.38, 0.192, 0.665, 0.636]\nD: [0.237, 0.568, 0.317, 0.772]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.367, 0.144, 0.611, 0.964]\nB: [0.337, 0.181, 0.58, 1.0]\nC: [0.367, 0.144, 0.632, 1.079]\nD: [0.031, 0.693, 0.361, 0.975]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_75_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_75_1.jpg"], "question": "Here is an object ([0.369, 0.153, 0.609, 0.965]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.367, 0.144, 0.611, 0.964]\nB: [0.337, 0.181, 0.58, 1.0]\nC: [0.367, 0.144, 0.632, 1.079]\nD: [0.031, 0.693, 0.361, 0.975]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.066, 0.762, 0.502, 0.981]\nB: [0.448, 0.158, 0.876, 0.285]\nC: [0.0, 0.782, 0.437, 1.0]\nD: [0.158, 0.832, 0.645, 0.868]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_76_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_76_1.jpg"], "question": "Here is an object ([0.0, 0.443, 0.603, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.066, 0.762, 0.502, 0.981]\nB: [0.448, 0.158, 0.876, 0.285]\nC: [0.0, 0.782, 0.437, 1.0]\nD: [0.158, 0.832, 0.645, 0.868]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.232, 0.325, 0.599, 0.582]\nB: [0.321, 0.0, 0.77, 1.0]\nC: [0.286, 0.044, 0.386, 0.461]\nD: [0.321, 0.0, 0.858, 0.894]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_77_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_77_1.jpg"], "question": "Here is an object ([0.394, 0.001, 0.947, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.232, 0.325, 0.599, 0.582]\nB: [0.321, 0.0, 0.77, 1.0]\nC: [0.286, 0.044, 0.386, 0.461]\nD: [0.321, 0.0, 0.858, 0.894]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.673, 0.375, 1.052, 0.929]\nB: [0.673, 0.375, 1.0, 1.0]\nC: [0.673, 0.375, 1.007, 0.979]\nD: [0.248, 0.358, 0.261, 0.589]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_78_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_78_1.jpg"], "question": "Here is an object ([0.532, 0.296, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.673, 0.375, 1.052, 0.929]\nB: [0.673, 0.375, 1.0, 1.0]\nC: [0.673, 0.375, 1.007, 0.979]\nD: [0.248, 0.358, 0.261, 0.589]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.327, 0.242, 0.479, 0.401]\nB: [0.397, 0.235, 0.548, 0.394]\nC: [0.669, 0.562, 0.69, 0.632]\nD: [0.766, 0.242, 0.848, 0.442]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_79_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_79_1.jpg"], "question": "Here is an object ([0.326, 0.249, 0.481, 0.422]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.327, 0.242, 0.479, 0.401]\nB: [0.397, 0.235, 0.548, 0.394]\nC: [0.669, 0.562, 0.69, 0.632]\nD: [0.766, 0.242, 0.848, 0.442]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.072, 0.064, 0.542, 0.249]\nB: [0.645, 0.408, 0.869, 0.582]\nC: [0.428, 0.0, 0.695, 0.919]\nD: [0.301, 0.0, 0.568, 0.919]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_80_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_80_1.jpg"], "question": "Here is an object ([0.31, 0.074, 0.576, 0.826]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.072, 0.064, 0.542, 0.249]\nB: [0.645, 0.408, 0.869, 0.582]\nC: [0.428, 0.0, 0.695, 0.919]\nD: [0.301, 0.0, 0.568, 0.919]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.08, 0.778, 0.391, 1.0]\nB: [0.508, 0.303, 0.577, 0.432]\nC: [0.155, 0.778, 0.466, 1.0]\nD: [0.046, 0.778, 0.357, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_81_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_81_1.jpg"], "question": "Here is an object ([0.044, 0.793, 0.334, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.08, 0.778, 0.391, 1.0]\nB: [0.508, 0.303, 0.577, 0.432]\nC: [0.155, 0.778, 0.466, 1.0]\nD: [0.046, 0.778, 0.357, 1.0]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.0, 1.0, 0.999]\nB: [0.0, 0.001, 1.0, 1.0]\nC: [0.411, 0.328, 0.752, 0.585]\nD: [0.525, 0.542, 0.97, 0.881]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_82_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_82_1.jpg"], "question": "Here is an object ([0.0, 0.001, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.0, 1.0, 0.999]\nB: [0.0, 0.001, 1.0, 1.0]\nC: [0.411, 0.328, 0.752, 0.585]\nD: [0.525, 0.542, 0.97, 0.881]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.304, 0.521, 0.474, 0.738]\nB: [0.18, 0.349, 0.421, 0.765]\nC: [0.18, 0.349, 0.401, 0.797]\nD: [0.282, 0.438, 0.726, 0.922]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_83_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_83_1.jpg"], "question": "Here is an object ([0.183, 0.338, 0.426, 0.754]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.304, 0.521, 0.474, 0.738]\nB: [0.18, 0.349, 0.421, 0.765]\nC: [0.18, 0.349, 0.401, 0.797]\nD: [0.282, 0.438, 0.726, 0.922]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.084, 0.0, 0.97, 0.975]\nB: [0.588, 0.258, 0.977, 0.639]\nC: [0.465, 0.197, 0.775, 0.597]\nD: [0.0, 0.0, 0.886, 0.975]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_84_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_84_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.884, 0.967]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.084, 0.0, 0.97, 0.975]\nB: [0.588, 0.258, 0.977, 0.639]\nC: [0.465, 0.197, 0.775, 0.597]\nD: [0.0, 0.0, 0.886, 0.975]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.096, 0.446, 1.0, 1.0]\nB: [0.089, 0.375, 0.936, 0.829]\nC: [0.089, 0.375, 0.993, 0.929]\nD: [0.096, 0.436, 1.0, 0.99]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_85_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_85_1.jpg"], "question": "Here is an object ([0.084, 0.376, 0.99, 0.903]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 406 and the height is 720.", "context": "Select from the following choices.\nA: [0.096, 0.446, 1.0, 1.0]\nB: [0.089, 0.375, 0.936, 0.829]\nC: [0.089, 0.375, 0.993, 0.929]\nD: [0.096, 0.436, 1.0, 0.99]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.121, 0.364, 0.447, 1.0]\nB: [0.17, 0.364, 0.495, 1.0]\nC: [0.26, 0.364, 0.586, 1.0]\nD: [0.149, 0.364, 0.475, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_86_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_86_1.jpg"], "question": "Here is an object ([0.291, 0.444, 0.606, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.121, 0.364, 0.447, 1.0]\nB: [0.17, 0.364, 0.495, 1.0]\nC: [0.26, 0.364, 0.586, 1.0]\nD: [0.149, 0.364, 0.475, 1.0]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.687, 0.894, 0.72, 0.951]\nB: [0.17, 0.021, 0.422, 0.332]\nC: [0.263, 0.164, 0.547, 0.483]\nD: [0.263, 0.164, 0.514, 0.475]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_87_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_87_1.jpg"], "question": "Here is an object ([0.247, 0.165, 0.501, 0.479]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.687, 0.894, 0.72, 0.951]\nB: [0.17, 0.021, 0.422, 0.332]\nC: [0.263, 0.164, 0.547, 0.483]\nD: [0.263, 0.164, 0.514, 0.475]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.528, 0.439, 0.573, 0.536]\nB: [0.45, 0.644, 0.761, 0.756]\nC: [0.528, 0.439, 0.577, 0.55]\nD: [0.542, 0.478, 0.588, 0.575]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_88_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_88_1.jpg"], "question": "Here is an object ([0.536, 0.414, 0.573, 0.528]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 640 and the height is 360.", "context": "Select from the following choices.\nA: [0.528, 0.439, 0.573, 0.536]\nB: [0.45, 0.644, 0.761, 0.756]\nC: [0.528, 0.439, 0.577, 0.55]\nD: [0.542, 0.478, 0.588, 0.575]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.598, 0.618, 0.884, 0.681]\nB: [0.141, 0.233, 0.448, 1.0]\nC: [0.473, 0.306, 0.578, 0.575]\nD: [0.141, 0.233, 0.455, 1.097]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_89_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_89_1.jpg"], "question": "Here is an object ([0.13, 0.26, 0.435, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.598, 0.618, 0.884, 0.681]\nB: [0.141, 0.233, 0.448, 1.0]\nC: [0.473, 0.306, 0.578, 0.575]\nD: [0.141, 0.233, 0.455, 1.097]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.25, 0.742, 1.0]\nB: [0.258, 0.14, 1.0, 0.89]\nC: [0.016, 0.108, 0.757, 0.858]\nD: [0.809, 0.283, 0.925, 0.317]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_90_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_90_1.jpg"], "question": "Here is an object ([0.065, 0.108, 1.0, 0.822]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1080 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.25, 0.742, 1.0]\nB: [0.258, 0.14, 1.0, 0.89]\nC: [0.016, 0.108, 0.757, 0.858]\nD: [0.809, 0.283, 0.925, 0.317]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.508, 0.017, 0.915, 0.2]\nB: [0.0, 0.004, 0.701, 0.935]\nC: [0.0, 0.004, 0.752, 1.0]\nD: [0.248, 0.004, 1.0, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_91_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_91_1.jpg"], "question": "Here is an object ([0.0, 0.021, 0.759, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.508, 0.017, 0.915, 0.2]\nB: [0.0, 0.004, 0.701, 0.935]\nC: [0.0, 0.004, 0.752, 1.0]\nD: [0.248, 0.004, 1.0, 1.0]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.518, 0.521, 0.582, 0.715]\nB: [0.512, 0.44, 0.566, 0.604]\nC: [0.518, 0.521, 0.573, 0.685]\nD: [0.518, 0.521, 0.578, 0.675]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_92_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_92_1.jpg"], "question": "Here is an object ([0.504, 0.521, 0.551, 0.662]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.518, 0.521, 0.582, 0.715]\nB: [0.512, 0.44, 0.566, 0.604]\nC: [0.518, 0.521, 0.573, 0.685]\nD: [0.518, 0.521, 0.578, 0.675]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.275, 0.518, 0.76, 1.0]\nB: [0.275, 0.518, 0.763, 0.928]\nC: [0.275, 0.518, 0.738, 1.083]\nD: [0.131, 0.343, 0.616, 0.825]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_93_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_93_1.jpg"], "question": "Here is an object ([0.677, 0.49, 0.845, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.275, 0.518, 0.76, 1.0]\nB: [0.275, 0.518, 0.763, 0.928]\nC: [0.275, 0.518, 0.738, 1.083]\nD: [0.131, 0.343, 0.616, 0.825]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.748, 0.189, 0.783, 0.533]\nB: [0.038, 0.267, 0.163, 0.346]\nC: [0.064, 0.235, 0.188, 0.314]\nD: [0.071, 0.322, 0.296, 0.649]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_94_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_94_1.jpg"], "question": "Here is an object ([0.109, 0.24, 0.23, 0.322]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.748, 0.189, 0.783, 0.533]\nB: [0.038, 0.267, 0.163, 0.346]\nC: [0.064, 0.235, 0.188, 0.314]\nD: [0.071, 0.322, 0.296, 0.649]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.436, 0.774, 0.797, 0.901]\nB: [0.478, 0.286, 0.601, 0.464]\nC: [0.439, 0.328, 0.561, 0.506]\nD: [0.652, 0.426, 0.946, 0.767]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_95_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_95_1.jpg"], "question": "Here is an object ([0.449, 0.339, 0.614, 0.582]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.436, 0.774, 0.797, 0.901]\nB: [0.478, 0.286, 0.601, 0.464]\nC: [0.439, 0.328, 0.561, 0.506]\nD: [0.652, 0.426, 0.946, 0.767]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.0, 0.217, 0.427, 1.0]\nB: [0.0, 0.217, 0.466, 0.968]\nC: [0.156, 0.217, 0.584, 1.0]\nD: [0.0, 0.217, 0.461, 0.944]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_96_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_96_1.jpg"], "question": "Here is an object ([0.0, 0.206, 0.405, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.217, 0.427, 1.0]\nB: [0.0, 0.217, 0.466, 0.968]\nC: [0.156, 0.217, 0.584, 1.0]\nD: [0.0, 0.217, 0.461, 0.944]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.412, 0.321, 0.501, 0.774]\nB: [0.047, 0.485, 0.552, 1.0]\nC: [0.119, 0.485, 0.623, 1.0]\nD: [0.119, 0.485, 0.693, 0.969]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_97_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_97_1.jpg"], "question": "Here is an object ([0.133, 0.522, 0.686, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.412, 0.321, 0.501, 0.774]\nB: [0.047, 0.485, 0.552, 1.0]\nC: [0.119, 0.485, 0.623, 1.0]\nD: [0.119, 0.485, 0.693, 0.969]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.416, 0.165, 0.517, 0.535]\nB: [0.43, 0.064, 0.532, 0.433]\nC: [0.422, 0.135, 0.523, 0.504]\nD: [0.422, 0.135, 0.505, 0.537]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_98_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_98_1.jpg"], "question": "Here is an object ([0.439, 0.157, 0.559, 0.557]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.416, 0.165, 0.517, 0.535]\nB: [0.43, 0.064, 0.532, 0.433]\nC: [0.422, 0.135, 0.523, 0.504]\nD: [0.422, 0.135, 0.505, 0.537]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.14, 0.257, 0.775, 0.714]\nB: [0.066, 0.125, 0.656, 0.619]\nC: [0.14, 0.257, 0.826, 0.689]\nD: [0.14, 0.257, 0.73, 0.751]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_99_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_99_1.jpg"], "question": "Here is an object ([0.154, 0.225, 0.735, 0.743]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.14, 0.257, 0.775, 0.714]\nB: [0.066, 0.125, 0.656, 0.619]\nC: [0.14, 0.257, 0.826, 0.689]\nD: [0.14, 0.257, 0.73, 0.751]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.321, 0.429, 0.502, 0.562]\nB: [0.321, 0.429, 0.493, 0.571]\nC: [0.399, 0.408, 0.58, 0.542]\nD: [0.287, 0.482, 0.467, 0.615]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_100_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_100_1.jpg"], "question": "Here is an object ([0.313, 0.362, 0.605, 0.611]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.321, 0.429, 0.502, 0.562]\nB: [0.321, 0.429, 0.493, 0.571]\nC: [0.399, 0.408, 0.58, 0.542]\nD: [0.287, 0.482, 0.467, 0.615]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.145, 0.1, 1.0, 1.0]\nB: [0.81, 0.383, 0.819, 0.604]\nC: [0.145, 0.0, 1.0, 0.9]\nD: [0.145, 0.1, 0.912, 0.946]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_101_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_101_1.jpg"], "question": "Here is an object ([0.15, 0.078, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.145, 0.1, 1.0, 1.0]\nB: [0.81, 0.383, 0.819, 0.604]\nC: [0.145, 0.0, 1.0, 0.9]\nD: [0.145, 0.1, 0.912, 0.946]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.077, 0.319, 0.395, 0.588]\nB: [0.563, 0.496, 0.853, 0.843]\nC: [0.576, 0.646, 0.911, 0.826]\nD: [0.498, 0.457, 0.788, 0.804]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_102_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_102_1.jpg"], "question": "Here is an object ([0.535, 0.507, 0.81, 0.825]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.077, 0.319, 0.395, 0.588]\nB: [0.563, 0.496, 0.853, 0.843]\nC: [0.576, 0.646, 0.911, 0.826]\nD: [0.498, 0.457, 0.788, 0.804]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.099, 0.274, 0.831, 1.0]\nB: [0.268, 0.156, 0.948, 0.758]\nC: [0.268, 0.156, 1.018, 0.989]\nD: [0.268, 0.156, 1.0, 0.882]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_103_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_103_1.jpg"], "question": "Here is an object ([0.295, 0.115, 0.986, 0.876]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.099, 0.274, 0.831, 1.0]\nB: [0.268, 0.156, 0.948, 0.758]\nC: [0.268, 0.156, 1.018, 0.989]\nD: [0.268, 0.156, 1.0, 0.882]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.159, 0.364, 0.504, 0.894]\nB: [0.192, 0.314, 0.537, 0.844]\nC: [0.198, 0.429, 0.423, 0.867]\nD: [0.72, 0.679, 0.87, 0.814]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_104_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_104_1.jpg"], "question": "Here is an object ([0.155, 0.296, 0.512, 0.847]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.159, 0.364, 0.504, 0.894]\nB: [0.192, 0.314, 0.537, 0.844]\nC: [0.198, 0.429, 0.423, 0.867]\nD: [0.72, 0.679, 0.87, 0.814]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.63, 0.36, 0.953, 0.414]\nB: [0.463, 0.172, 0.638, 0.432]\nC: [0.355, 0.146, 0.53, 0.406]\nD: [0.409, 0.218, 0.584, 0.478]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_105_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_105_1.jpg"], "question": "Here is an object ([0.372, 0.129, 0.613, 0.461]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.63, 0.36, 0.953, 0.414]\nB: [0.463, 0.172, 0.638, 0.432]\nC: [0.355, 0.146, 0.53, 0.406]\nD: [0.409, 0.218, 0.584, 0.478]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.712, 0.625, 0.841, 0.796]\nB: [0.351, 0.718, 0.397, 0.847]\nC: [0.364, 0.769, 0.41, 0.899]\nD: [0.409, 0.537, 0.505, 0.747]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_106_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_106_1.jpg"], "question": "Here is an object ([0.334, 0.714, 0.382, 0.814]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.712, 0.625, 0.841, 0.796]\nB: [0.351, 0.718, 0.397, 0.847]\nC: [0.364, 0.769, 0.41, 0.899]\nD: [0.409, 0.537, 0.505, 0.747]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.322, 0.412, 0.575, 0.818]\nB: [0.242, 0.253, 0.484, 0.642]\nC: [0.322, 0.412, 0.563, 0.801]\nD: [0.306, 0.432, 0.548, 0.821]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_107_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_107_1.jpg"], "question": "Here is an object ([0.298, 0.354, 0.506, 0.793]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.322, 0.412, 0.575, 0.818]\nB: [0.242, 0.253, 0.484, 0.642]\nC: [0.322, 0.412, 0.563, 0.801]\nD: [0.306, 0.432, 0.548, 0.821]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.48, 0.347, 0.798, 0.393]\nB: [0.207, 0.154, 0.544, 0.531]\nC: [0.207, 0.154, 0.597, 0.501]\nD: [0.332, 0.514, 0.696, 0.872]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_108_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_108_1.jpg"], "question": "Here is an object ([0.229, 0.156, 0.602, 0.49]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.48, 0.347, 0.798, 0.393]\nB: [0.207, 0.154, 0.544, 0.531]\nC: [0.207, 0.154, 0.597, 0.501]\nD: [0.332, 0.514, 0.696, 0.872]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.222, 0.832, 0.42, 0.985]\nB: [0.277, 0.832, 0.502, 1.0]\nC: [0.222, 0.832, 0.447, 1.0]\nD: [0.222, 0.832, 0.476, 1.031]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_109_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_109_1.jpg"], "question": "Here is an object ([0.0, 0.457, 0.234, 0.799]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.222, 0.832, 0.42, 0.985]\nB: [0.277, 0.832, 0.502, 1.0]\nC: [0.222, 0.832, 0.447, 1.0]\nD: [0.222, 0.832, 0.476, 1.031]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.0, 0.507, 1.0, 0.747]\nB: [0.0, 0.59, 1.0, 0.831]\nC: [0.0, 0.507, 1.165, 0.767]\nD: [0.72, 0.235, 0.856, 0.468]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_110_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_110_1.jpg"], "question": "Here is an object ([0.0, 0.514, 1.0, 0.725]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.507, 1.0, 0.747]\nB: [0.0, 0.59, 1.0, 0.831]\nC: [0.0, 0.507, 1.165, 0.767]\nD: [0.72, 0.235, 0.856, 0.468]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.198, 0.206, 0.652, 0.844]\nB: [0.374, 0.235, 0.77, 0.818]\nC: [0.626, 0.379, 0.905, 0.808]\nD: [0.198, 0.206, 0.594, 0.789]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_111_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_111_1.jpg"], "question": "Here is an object ([0.207, 0.212, 0.609, 0.786]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.198, 0.206, 0.652, 0.844]\nB: [0.374, 0.235, 0.77, 0.818]\nC: [0.626, 0.379, 0.905, 0.808]\nD: [0.198, 0.206, 0.594, 0.789]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.305, 0.0, 0.776, 0.582]\nB: [0.343, 0.211, 0.813, 0.793]\nC: [0.399, 0.029, 0.635, 0.49]\nD: [0.305, 0.0, 0.734, 0.481]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_112_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_112_1.jpg"], "question": "Here is an object ([0.302, 0.0, 0.73, 0.333]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.305, 0.0, 0.776, 0.582]\nB: [0.343, 0.211, 0.813, 0.793]\nC: [0.399, 0.029, 0.635, 0.49]\nD: [0.305, 0.0, 0.734, 0.481]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.322, 0.349, 1.0, 0.732]\nB: [0.127, 0.397, 0.805, 0.781]\nC: [0.314, 0.597, 0.748, 0.897]\nD: [0.003, 0.468, 0.254, 0.578]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_113_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_113_1.jpg"], "question": "Here is an object ([0.306, 0.381, 1.0, 0.722]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.322, 0.349, 1.0, 0.732]\nB: [0.127, 0.397, 0.805, 0.781]\nC: [0.314, 0.597, 0.748, 0.897]\nD: [0.003, 0.468, 0.254, 0.578]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.285, 0.218, 0.616, 0.626]\nB: [0.42, 0.044, 0.645, 0.375]\nC: [0.285, 0.218, 0.609, 0.671]\nD: [0.285, 0.218, 0.62, 0.713]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_114_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_114_1.jpg"], "question": "Here is an object ([0.395, 0.212, 0.702, 0.669]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.285, 0.218, 0.616, 0.626]\nB: [0.42, 0.044, 0.645, 0.375]\nC: [0.285, 0.218, 0.609, 0.671]\nD: [0.285, 0.218, 0.62, 0.713]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.861, 0.214, 0.959, 0.562]\nB: [0.861, 0.214, 0.968, 0.524]\nC: [0.893, 0.111, 1.0, 0.421]\nD: [0.147, 0.603, 0.412, 0.931]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_115_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_115_1.jpg"], "question": "Here is an object ([0.87, 0.222, 0.975, 0.528]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.861, 0.214, 0.959, 0.562]\nB: [0.861, 0.214, 0.968, 0.524]\nC: [0.893, 0.111, 1.0, 0.421]\nD: [0.147, 0.603, 0.412, 0.931]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.18, 0.113, 0.617, 0.567]\nB: [0.057, 0.256, 0.484, 0.771]\nC: [0.427, 0.164, 0.723, 0.478]\nD: [0.18, 0.113, 0.608, 0.628]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_116_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_116_1.jpg"], "question": "Here is an object ([0.164, 0.11, 0.591, 0.624]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.18, 0.113, 0.617, 0.567]\nB: [0.057, 0.256, 0.484, 0.771]\nC: [0.427, 0.164, 0.723, 0.478]\nD: [0.18, 0.113, 0.608, 0.628]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.362, 0.272, 0.645, 0.729]\nB: [0.362, 0.272, 0.713, 0.839]\nC: [0.241, 0.231, 0.585, 0.494]\nD: [0.604, 0.682, 0.843, 0.971]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_117_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_117_1.jpg"], "question": "Here is an object ([0.323, 0.211, 0.684, 0.831]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.362, 0.272, 0.645, 0.729]\nB: [0.362, 0.272, 0.713, 0.839]\nC: [0.241, 0.231, 0.585, 0.494]\nD: [0.604, 0.682, 0.843, 0.971]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.035, 0.06, 1.0, 1.0]\nB: [0.035, 0.06, 1.012, 1.072]\nC: [0.035, 0.06, 1.058, 1.111]\nD: [0.035, 0.06, 1.018, 0.933]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_118_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_118_1.jpg"], "question": "Here is an object ([0.105, 0.153, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.035, 0.06, 1.0, 1.0]\nB: [0.035, 0.06, 1.012, 1.072]\nC: [0.035, 0.06, 1.058, 1.111]\nD: [0.035, 0.06, 1.018, 0.933]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.452, 0.0, 1.0, 0.858]\nB: [0.434, 0.0, 0.982, 0.858]\nC: [0.277, 0.025, 0.845, 0.994]\nD: [0.277, 0.025, 0.824, 0.883]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_119_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_119_1.jpg"], "question": "Here is an object ([0.275, 0.033, 0.816, 0.889]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.452, 0.0, 1.0, 0.858]\nB: [0.434, 0.0, 0.982, 0.858]\nC: [0.277, 0.025, 0.845, 0.994]\nD: [0.277, 0.025, 0.824, 0.883]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.267, 0.72, 0.754]\nB: [0.044, 0.375, 0.259, 0.868]\nC: [0.0, 0.267, 0.838, 0.692]\nD: [0.0, 0.239, 0.838, 0.664]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_120_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_120_1.jpg"], "question": "Here is an object ([0.0, 0.268, 0.805, 0.74]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.267, 0.72, 0.754]\nB: [0.044, 0.375, 0.259, 0.868]\nC: [0.0, 0.267, 0.838, 0.692]\nD: [0.0, 0.239, 0.838, 0.664]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.0, 0.001, 0.979, 0.851]\nB: [0.0, 0.001, 1.0, 1.0]\nC: [0.0, 0.0, 1.0, 0.999]\nD: [0.0, 0.0, 1.0, 0.999]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_121_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_121_1.jpg"], "question": "Here is an object ([0.302, 0.026, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 480 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.001, 0.979, 0.851]\nB: [0.0, 0.001, 1.0, 1.0]\nC: [0.0, 0.0, 1.0, 0.999]\nD: [0.0, 0.0, 1.0, 0.999]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.281, 0.379, 0.819, 0.546]\nB: [0.018, 0.447, 0.457, 0.604]\nC: [0.018, 0.447, 0.555, 0.614]\nD: [0.414, 0.225, 0.912, 0.421]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_122_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_122_1.jpg"], "question": "Here is an object ([0.025, 0.489, 0.583, 0.636]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.281, 0.379, 0.819, 0.546]\nB: [0.018, 0.447, 0.457, 0.604]\nC: [0.018, 0.447, 0.555, 0.614]\nD: [0.414, 0.225, 0.912, 0.421]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.502, 0.561, 0.619, 0.736]\nB: [0.462, 0.122, 0.881, 0.621]\nC: [0.517, 0.637, 0.634, 0.812]\nD: [0.502, 0.561, 0.606, 0.724]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_123_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_123_1.jpg"], "question": "Here is an object ([0.515, 0.581, 0.582, 0.721]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.502, 0.561, 0.619, 0.736]\nB: [0.462, 0.122, 0.881, 0.621]\nC: [0.517, 0.637, 0.634, 0.812]\nD: [0.502, 0.561, 0.606, 0.724]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.2, 0.082, 0.523, 1.019]\nB: [0.413, 0.683, 0.617, 0.865]\nC: [0.2, 0.082, 0.583, 1.0]\nD: [0.178, 0.69, 0.47, 0.832]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_124_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_124_1.jpg"], "question": "Here is an object ([0.189, 0.138, 0.595, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.2, 0.082, 0.523, 1.019]\nB: [0.413, 0.683, 0.617, 0.865]\nC: [0.2, 0.082, 0.583, 1.0]\nD: [0.178, 0.69, 0.47, 0.832]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.36, 0.119, 0.56, 0.476]\nB: [0.095, 0.053, 0.541, 0.535]\nC: [0.36, 0.119, 0.557, 0.432]\nD: [0.36, 0.119, 0.534, 0.429]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_125_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_125_1.jpg"], "question": "Here is an object ([0.371, 0.131, 0.545, 0.589]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.36, 0.119, 0.56, 0.476]\nB: [0.095, 0.053, 0.541, 0.535]\nC: [0.36, 0.119, 0.557, 0.432]\nD: [0.36, 0.119, 0.534, 0.429]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.156, 0.181, 0.855, 1.158]\nB: [0.534, 0.085, 0.951, 0.11]\nC: [0.63, 0.921, 0.958, 0.972]\nD: [0.156, 0.181, 0.795, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_126_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_126_1.jpg"], "question": "Here is an object ([0.303, 0.033, 0.923, 0.899]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.156, 0.181, 0.855, 1.158]\nB: [0.534, 0.085, 0.951, 0.11]\nC: [0.63, 0.921, 0.958, 0.972]\nD: [0.156, 0.181, 0.795, 1.0]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.142, 0.242, 0.715]\nB: [0.0, 0.142, 0.28, 0.631]\nC: [0.749, 0.119, 0.961, 0.493]\nD: [0.0, 0.142, 0.267, 0.637]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_127_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_127_1.jpg"], "question": "Here is an object ([0.0, 0.143, 0.256, 0.608]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.142, 0.242, 0.715]\nB: [0.0, 0.142, 0.28, 0.631]\nC: [0.749, 0.119, 0.961, 0.493]\nD: [0.0, 0.142, 0.267, 0.637]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.131, 0.228, 0.717, 0.76]\nB: [0.219, 0.322, 0.315, 0.586]\nC: [0.131, 0.228, 0.64, 0.701]\nD: [0.648, 0.182, 0.732, 0.421]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_128_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_128_1.jpg"], "question": "Here is an object ([0.113, 0.224, 0.618, 0.713]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 406 and the height is 720.", "context": "Select from the following choices.\nA: [0.131, 0.228, 0.717, 0.76]\nB: [0.219, 0.322, 0.315, 0.586]\nC: [0.131, 0.228, 0.64, 0.701]\nD: [0.648, 0.182, 0.732, 0.421]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.531, 0.565, 0.643, 0.656]\nB: [0.008, 0.514, 0.429, 0.842]\nC: [0.531, 0.565, 0.629, 0.65]\nD: [0.077, 0.757, 0.463, 0.997]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_129_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_129_1.jpg"], "question": "Here is an object ([0.548, 0.553, 0.65, 0.622]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.531, 0.565, 0.643, 0.656]\nB: [0.008, 0.514, 0.429, 0.842]\nC: [0.531, 0.565, 0.629, 0.65]\nD: [0.077, 0.757, 0.463, 0.997]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.292, 0.122, 0.642, 0.711]\nB: [0.312, 0.0, 0.662, 0.589]\nC: [0.291, 0.154, 0.641, 0.743]\nD: [0.462, 0.358, 0.812, 0.947]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_130_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_130_1.jpg"], "question": "Here is an object ([0.295, 0.146, 0.641, 0.739]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.292, 0.122, 0.642, 0.711]\nB: [0.312, 0.0, 0.662, 0.589]\nC: [0.291, 0.154, 0.641, 0.743]\nD: [0.462, 0.358, 0.812, 0.947]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.416, 0.371, 0.48, 0.562]\nB: [0.404, 0.312, 0.468, 0.504]\nC: [0.241, 0.174, 0.517, 0.3]\nD: [0.426, 0.392, 0.49, 0.583]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_131_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_131_1.jpg"], "question": "Here is an object ([0.431, 0.4, 0.509, 0.603]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.416, 0.371, 0.48, 0.562]\nB: [0.404, 0.312, 0.468, 0.504]\nC: [0.241, 0.174, 0.517, 0.3]\nD: [0.426, 0.392, 0.49, 0.583]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.597, 0.102, 0.949]\nB: [0.0, 0.597, 0.112, 1.035]\nC: [0.0, 0.597, 0.096, 0.986]\nD: [0.493, 0.408, 0.527, 0.66]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_132_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_132_1.jpg"], "question": "Here is an object ([0.0, 0.621, 0.077, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.597, 0.102, 0.949]\nB: [0.0, 0.597, 0.112, 1.035]\nC: [0.0, 0.597, 0.096, 0.986]\nD: [0.493, 0.408, 0.527, 0.66]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.089, 0.829, 0.118, 0.868]\nB: [0.54, 0.044, 0.668, 0.643]\nC: [0.537, 0.218, 0.666, 0.817]\nD: [0.54, 0.044, 0.655, 0.714]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_133_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_133_1.jpg"], "question": "Here is an object ([0.595, 0.092, 0.691, 0.7]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.089, 0.829, 0.118, 0.868]\nB: [0.54, 0.044, 0.668, 0.643]\nC: [0.537, 0.218, 0.666, 0.817]\nD: [0.54, 0.044, 0.655, 0.714]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.395, 0.317, 0.602, 0.821]\nB: [0.504, 0.408, 0.513, 0.686]\nC: [0.484, 0.439, 0.69, 0.943]\nD: [0.313, 0.244, 0.52, 0.749]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_134_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_134_1.jpg"], "question": "Here is an object ([0.429, 0.154, 0.625, 0.786]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.395, 0.317, 0.602, 0.821]\nB: [0.504, 0.408, 0.513, 0.686]\nC: [0.484, 0.439, 0.69, 0.943]\nD: [0.313, 0.244, 0.52, 0.749]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.13, 0.0, 0.852, 1.0]\nB: [0.071, 0.0, 0.793, 1.0]\nC: [0.095, 0.306, 0.59, 0.322]\nD: [0.98, 0.435, 0.996, 0.803]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_135_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_135_1.jpg"], "question": "Here is an object ([0.063, 0.0, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.13, 0.0, 0.852, 1.0]\nB: [0.071, 0.0, 0.793, 1.0]\nC: [0.095, 0.306, 0.59, 0.322]\nD: [0.98, 0.435, 0.996, 0.803]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.277, 0.0, 0.519, 0.45]\nB: [0.395, 0.013, 0.637, 0.463]\nC: [0.497, 0.199, 0.843, 0.696]\nD: [0.281, 0.114, 0.523, 0.564]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_136_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_136_1.jpg"], "question": "Here is an object ([0.264, 0.0, 0.491, 0.404]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.277, 0.0, 0.519, 0.45]\nB: [0.395, 0.013, 0.637, 0.463]\nC: [0.497, 0.199, 0.843, 0.696]\nD: [0.281, 0.114, 0.523, 0.564]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.901, 0.401, 0.985, 1.051]\nB: [0.901, 0.401, 1.0, 1.0]\nC: [0.504, 0.157, 0.877, 0.589]\nD: [0.901, 0.206, 1.0, 0.804]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_137_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_137_1.jpg"], "question": "Here is an object ([0.934, 0.432, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.901, 0.401, 0.985, 1.051]\nB: [0.901, 0.401, 1.0, 1.0]\nC: [0.504, 0.157, 0.877, 0.589]\nD: [0.901, 0.206, 1.0, 0.804]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.267, 0.299, 0.561]\nB: [0.0, 0.267, 0.309, 0.537]\nC: [0.0, 0.267, 0.323, 0.568]\nD: [0.0, 0.171, 0.323, 0.472]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_138_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_138_1.jpg"], "question": "Here is an object ([0.0, 0.246, 0.424, 0.611]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.267, 0.299, 0.561]\nB: [0.0, 0.267, 0.309, 0.537]\nC: [0.0, 0.267, 0.323, 0.568]\nD: [0.0, 0.171, 0.323, 0.472]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.0, 0.606, 1.0]\nB: [0.502, 0.601, 0.622, 0.924]\nC: [0.287, 0.311, 0.747, 0.39]\nD: [0.0, 0.0, 0.535, 1.157]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_139_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_139_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.923, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.0, 0.606, 1.0]\nB: [0.502, 0.601, 0.622, 0.924]\nC: [0.287, 0.311, 0.747, 0.39]\nD: [0.0, 0.0, 0.535, 1.157]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.05, 0.728, 0.195, 0.956]\nB: [0.193, 0.054, 0.217, 0.426]\nC: [0.434, 0.371, 0.787, 1.0]\nD: [0.519, 0.371, 0.872, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_140_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_140_1.jpg"], "question": "Here is an object ([0.529, 0.507, 0.775, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.05, 0.728, 0.195, 0.956]\nB: [0.193, 0.054, 0.217, 0.426]\nC: [0.434, 0.371, 0.787, 1.0]\nD: [0.519, 0.371, 0.872, 1.0]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.409, 0.479, 0.546, 0.554]\nB: [0.409, 0.479, 0.537, 0.55]\nC: [0.429, 0.487, 0.557, 0.558]\nD: [0.409, 0.479, 0.516, 0.56]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_141_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_141_1.jpg"], "question": "Here is an object ([0.455, 0.471, 0.564, 0.543]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.409, 0.479, 0.546, 0.554]\nB: [0.409, 0.479, 0.537, 0.55]\nC: [0.429, 0.487, 0.557, 0.558]\nD: [0.409, 0.479, 0.516, 0.56]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.514, 0.244, 0.854, 0.649]\nB: [0.601, 0.221, 1.0, 0.662]\nC: [0.514, 0.244, 0.913, 0.686]\nD: [0.601, 0.308, 1.0, 0.75]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_142_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_142_1.jpg"], "question": "Here is an object ([0.589, 0.235, 0.943, 0.722]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.514, 0.244, 0.854, 0.649]\nB: [0.601, 0.221, 1.0, 0.662]\nC: [0.514, 0.244, 0.913, 0.686]\nD: [0.601, 0.308, 1.0, 0.75]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.488, 0.207, 0.569, 0.358]\nB: [0.469, 0.228, 0.549, 0.379]\nC: [0.432, 0.458, 0.816, 0.517]\nD: [0.019, 0.432, 0.448, 0.564]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_143_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_143_1.jpg"], "question": "Here is an object ([0.496, 0.242, 0.566, 0.381]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.488, 0.207, 0.569, 0.358]\nB: [0.469, 0.228, 0.549, 0.379]\nC: [0.432, 0.458, 0.816, 0.517]\nD: [0.019, 0.432, 0.448, 0.564]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.161, 0.049, 0.542]\nB: [0.699, 0.242, 0.79, 0.568]\nC: [0.0, 0.099, 0.049, 0.479]\nD: [0.0, 0.101, 0.049, 0.482]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_144_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_144_1.jpg"], "question": "Here is an object ([0.0, 0.094, 0.1, 0.554]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.161, 0.049, 0.542]\nB: [0.699, 0.242, 0.79, 0.568]\nC: [0.0, 0.099, 0.049, 0.479]\nD: [0.0, 0.101, 0.049, 0.482]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.377, 0.336, 1.024, 0.835]\nB: [0.377, 0.336, 0.956, 0.956]\nC: [0.377, 0.336, 1.061, 1.003]\nD: [0.101, 0.381, 0.68, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_145_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_145_1.jpg"], "question": "Here is an object ([0.433, 0.271, 0.981, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.377, 0.336, 1.024, 0.835]\nB: [0.377, 0.336, 0.956, 0.956]\nC: [0.377, 0.336, 1.061, 1.003]\nD: [0.101, 0.381, 0.68, 1.0]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.364, 0.487, 0.55, 0.693]\nB: [0.364, 0.487, 0.529, 0.668]\nC: [0.273, 0.447, 0.459, 0.653]\nD: [0.378, 0.558, 0.564, 0.764]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_146_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_146_1.jpg"], "question": "Here is an object ([0.342, 0.415, 0.542, 0.607]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.364, 0.487, 0.55, 0.693]\nB: [0.364, 0.487, 0.529, 0.668]\nC: [0.273, 0.447, 0.459, 0.653]\nD: [0.378, 0.558, 0.564, 0.764]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.116, 0.26, 0.833, 0.936]\nB: [0.116, 0.26, 0.734, 1.0]\nC: [0.0, 0.26, 0.619, 1.0]\nD: [0.116, 0.626, 0.322, 0.66]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_147_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_147_1.jpg"], "question": "Here is an object ([0.113, 0.256, 0.725, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.116, 0.26, 0.833, 0.936]\nB: [0.116, 0.26, 0.734, 1.0]\nC: [0.0, 0.26, 0.619, 1.0]\nD: [0.116, 0.626, 0.322, 0.66]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.409, 0.407, 0.471, 0.524]\nB: [0.402, 0.449, 0.465, 0.565]\nC: [0.404, 0.357, 0.466, 0.474]\nD: [0.137, 0.357, 0.261, 0.697]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_148_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_148_1.jpg"], "question": "Here is an object ([0.479, 0.539, 0.527, 0.662]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.409, 0.407, 0.471, 0.524]\nB: [0.402, 0.449, 0.465, 0.565]\nC: [0.404, 0.357, 0.466, 0.474]\nD: [0.137, 0.357, 0.261, 0.697]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.453, 0.503, 0.507, 0.681]\nB: [0.128, 0.867, 0.552, 0.899]\nC: [0.276, 0.35, 0.747, 0.397]\nD: [0.453, 0.503, 0.503, 0.706]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_149_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_149_1.jpg"], "question": "Here is an object ([0.487, 0.506, 0.544, 0.672]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.453, 0.503, 0.507, 0.681]\nB: [0.128, 0.867, 0.552, 0.899]\nC: [0.276, 0.35, 0.747, 0.397]\nD: [0.453, 0.503, 0.503, 0.706]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.22, 0.242, 0.862, 0.635]\nB: [0.161, 0.114, 0.634, 0.354]\nC: [0.562, 0.422, 0.925, 0.835]\nD: [0.359, 0.388, 1.0, 0.781]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_150_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_150_1.jpg"], "question": "Here is an object ([0.209, 0.215, 0.863, 0.618]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.22, 0.242, 0.862, 0.635]\nB: [0.161, 0.114, 0.634, 0.354]\nC: [0.562, 0.422, 0.925, 0.835]\nD: [0.359, 0.388, 1.0, 0.781]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.285, 0.511, 1.0, 0.756]\nB: [0.606, 0.539, 0.62, 0.972]\nC: [0.22, 0.585, 0.935, 0.829]\nD: [0.285, 0.511, 1.085, 0.719]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_151_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_151_1.jpg"], "question": "Here is an object ([0.435, 0.412, 1.0, 0.749]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.285, 0.511, 1.0, 0.756]\nB: [0.606, 0.539, 0.62, 0.972]\nC: [0.22, 0.585, 0.935, 0.829]\nD: [0.285, 0.511, 1.085, 0.719]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.209, 0.343, 0.797, 0.886]\nB: [0.028, 0.369, 0.616, 0.912]\nC: [0.0, 0.146, 0.588, 0.689]\nD: [0.337, 0.056, 0.549, 0.196]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_152_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_152_1.jpg"], "question": "Here is an object ([0.021, 0.375, 0.605, 0.915]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.209, 0.343, 0.797, 0.886]\nB: [0.028, 0.369, 0.616, 0.912]\nC: [0.0, 0.146, 0.588, 0.689]\nD: [0.337, 0.056, 0.549, 0.196]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.36, 0.068, 0.727, 0.486]\nB: [0.048, 0.221, 0.545, 0.911]\nC: [0.116, 0.31, 0.613, 1.0]\nD: [0.116, 0.31, 0.68, 1.039]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_153_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_153_1.jpg"], "question": "Here is an object ([0.116, 0.312, 0.606, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.36, 0.068, 0.727, 0.486]\nB: [0.048, 0.221, 0.545, 0.911]\nC: [0.116, 0.31, 0.613, 1.0]\nD: [0.116, 0.31, 0.68, 1.039]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.251, 0.228, 1.0, 1.0]\nB: [0.0, 0.0, 0.749, 0.772]\nC: [0.0, 0.228, 0.749, 1.0]\nD: [0.0, 0.113, 0.749, 0.885]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_154_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_154_1.jpg"], "question": "Here is an object ([0.0, 0.119, 0.75, 0.885]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.251, 0.228, 1.0, 1.0]\nB: [0.0, 0.0, 0.749, 0.772]\nC: [0.0, 0.228, 0.749, 1.0]\nD: [0.0, 0.113, 0.749, 0.885]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.084, 0.149, 0.391, 0.438]\nB: [0.0, 0.071, 0.836, 1.133]\nC: [0.0, 0.071, 0.905, 1.0]\nD: [0.095, 0.0, 1.0, 0.929]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_155_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_155_1.jpg"], "question": "Here is an object ([0.0, 0.001, 0.894, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.084, 0.149, 0.391, 0.438]\nB: [0.0, 0.071, 0.836, 1.133]\nC: [0.0, 0.071, 0.905, 1.0]\nD: [0.095, 0.0, 1.0, 0.929]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.066, 0.203, 0.189, 0.603]\nB: [0.204, 0.146, 0.611, 1.0]\nC: [0.03, 0.146, 0.437, 1.0]\nD: [0.03, 0.146, 0.445, 0.939]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_156_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_156_1.jpg"], "question": "Here is an object ([0.034, 0.21, 0.511, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.066, 0.203, 0.189, 0.603]\nB: [0.204, 0.146, 0.611, 1.0]\nC: [0.03, 0.146, 0.437, 1.0]\nD: [0.03, 0.146, 0.445, 0.939]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.3, 0.251, 0.613, 1.0]\nB: [0.39, 0.0, 0.712, 0.697]\nC: [0.708, 0.621, 0.739, 0.844]\nD: [0.39, 0.0, 0.703, 0.749]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_157_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_157_1.jpg"], "question": "Here is an object ([0.242, 0.0, 0.613, 0.656]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.3, 0.251, 0.613, 1.0]\nB: [0.39, 0.0, 0.712, 0.697]\nC: [0.708, 0.621, 0.739, 0.844]\nD: [0.39, 0.0, 0.703, 0.749]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.408, 0.212, 0.655, 0.739]\nB: [0.17, 0.383, 0.197, 0.639]\nC: [0.408, 0.212, 0.661, 0.754]\nD: [0.408, 0.212, 0.665, 0.856]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_158_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_158_1.jpg"], "question": "Here is an object ([0.403, 0.207, 0.651, 0.767]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.408, 0.212, 0.655, 0.739]\nB: [0.17, 0.383, 0.197, 0.639]\nC: [0.408, 0.212, 0.661, 0.754]\nD: [0.408, 0.212, 0.665, 0.856]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.594, 0.279, 0.91, 0.968]\nB: [0.486, 0.013, 0.765, 0.59]\nC: [0.446, 0.122, 0.805, 0.543]\nD: [0.594, 0.279, 0.872, 0.857]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_159_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_159_1.jpg"], "question": "Here is an object ([0.596, 0.289, 0.867, 0.853]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1270 and the height is 720.", "context": "Select from the following choices.\nA: [0.594, 0.279, 0.91, 0.968]\nB: [0.486, 0.013, 0.765, 0.59]\nC: [0.446, 0.122, 0.805, 0.543]\nD: [0.594, 0.279, 0.872, 0.857]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.002, 0.087, 0.223, 0.472]\nB: [0.194, 0.114, 0.683, 0.775]\nC: [0.069, 0.221, 0.233, 0.621]\nD: [0.179, 0.339, 0.668, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_160_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_160_1.jpg"], "question": "Here is an object ([0.228, 0.0, 0.719, 0.607]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.002, 0.087, 0.223, 0.472]\nB: [0.194, 0.114, 0.683, 0.775]\nC: [0.069, 0.221, 0.233, 0.621]\nD: [0.179, 0.339, 0.668, 1.0]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.459, 0.085, 0.476, 0.549]\nB: [0.248, 0.667, 0.747, 0.828]\nC: [0.512, 0.371, 0.652, 0.542]\nD: [0.512, 0.371, 0.626, 0.522]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_161_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_161_1.jpg"], "question": "Here is an object ([0.509, 0.357, 0.635, 0.535]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.459, 0.085, 0.476, 0.549]\nB: [0.248, 0.667, 0.747, 0.828]\nC: [0.512, 0.371, 0.652, 0.542]\nD: [0.512, 0.371, 0.626, 0.522]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.32, 0.046, 0.584, 0.879]\nB: [0.177, 0.0, 0.491, 0.917]\nC: [0.494, 0.643, 0.716, 0.814]\nD: [0.32, 0.046, 0.634, 0.963]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_162_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_162_1.jpg"], "question": "Here is an object ([0.324, 0.046, 0.635, 0.968]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.32, 0.046, 0.584, 0.879]\nB: [0.177, 0.0, 0.491, 0.917]\nC: [0.494, 0.643, 0.716, 0.814]\nD: [0.32, 0.046, 0.634, 0.963]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.43, 0.485, 0.783, 0.656]\nB: [0.502, 0.41, 0.579, 0.64]\nC: [0.463, 0.338, 0.54, 0.568]\nD: [0.488, 0.294, 0.566, 0.525]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_163_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_163_1.jpg"], "question": "Here is an object ([0.476, 0.335, 0.562, 0.568]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.43, 0.485, 0.783, 0.656]\nB: [0.502, 0.41, 0.579, 0.64]\nC: [0.463, 0.338, 0.54, 0.568]\nD: [0.488, 0.294, 0.566, 0.525]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.461, 0.357, 0.537, 0.714]\nB: [0.461, 0.357, 0.526, 0.771]\nC: [0.095, 0.572, 0.489, 0.808]\nD: [0.465, 0.401, 0.541, 0.758]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_164_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_164_1.jpg"], "question": "Here is an object ([0.466, 0.358, 0.545, 0.706]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.461, 0.357, 0.537, 0.714]\nB: [0.461, 0.357, 0.526, 0.771]\nC: [0.095, 0.572, 0.489, 0.808]\nD: [0.465, 0.401, 0.541, 0.758]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.442, 0.604, 0.598, 0.832]\nB: [0.49, 0.487, 0.658, 0.771]\nC: [0.442, 0.604, 0.61, 0.887]\nD: [0.409, 0.69, 0.577, 0.974]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_165_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_165_1.jpg"], "question": "Here is an object ([0.455, 0.621, 0.626, 0.886]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.442, 0.604, 0.598, 0.832]\nB: [0.49, 0.487, 0.658, 0.771]\nC: [0.442, 0.604, 0.61, 0.887]\nD: [0.409, 0.69, 0.577, 0.974]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.416, 0.133, 0.897, 0.514]\nB: [0.416, 0.133, 0.995, 0.537]\nC: [0.433, 0.497, 0.685, 0.806]\nD: [0.421, 0.0, 1.0, 0.404]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_166_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_166_1.jpg"], "question": "Here is an object ([0.436, 0.083, 0.995, 0.561]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 406 and the height is 720.", "context": "Select from the following choices.\nA: [0.416, 0.133, 0.897, 0.514]\nB: [0.416, 0.133, 0.995, 0.537]\nC: [0.433, 0.497, 0.685, 0.806]\nD: [0.421, 0.0, 1.0, 0.404]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.228, 0.108, 0.396, 0.479]\nB: [0.171, 0.0, 0.923, 0.742]\nC: [0.171, 0.093, 1.0, 0.824]\nD: [0.171, 0.0, 1.0, 0.731]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_167_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_167_1.jpg"], "question": "Here is an object ([0.165, 0.0, 1.0, 0.726]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.228, 0.108, 0.396, 0.479]\nB: [0.171, 0.0, 0.923, 0.742]\nC: [0.171, 0.093, 1.0, 0.824]\nD: [0.171, 0.0, 1.0, 0.731]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.074, 0.186, 0.488, 1.0]\nB: [0.058, 0.151, 0.472, 0.965]\nC: [0.159, 0.186, 0.639, 0.935]\nD: [0.159, 0.186, 0.573, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_168_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_168_1.jpg"], "question": "Here is an object ([0.179, 0.022, 0.554, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.074, 0.186, 0.488, 1.0]\nB: [0.058, 0.151, 0.472, 0.965]\nC: [0.159, 0.186, 0.639, 0.935]\nD: [0.159, 0.186, 0.573, 1.0]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.0, 0.287, 0.342, 0.665]\nB: [0.078, 0.428, 0.42, 0.806]\nC: [0.34, 0.412, 0.643, 0.438]\nD: [0.0, 0.287, 0.341, 0.682]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_169_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_169_1.jpg"], "question": "Here is an object ([0.0, 0.297, 0.397, 0.665]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.287, 0.342, 0.665]\nB: [0.078, 0.428, 0.42, 0.806]\nC: [0.34, 0.412, 0.643, 0.438]\nD: [0.0, 0.287, 0.341, 0.682]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.464, 0.276, 0.727, 1.0]\nB: [0.464, 0.276, 0.745, 0.993]\nC: [0.517, 0.276, 0.78, 1.0]\nD: [0.464, 0.276, 0.692, 0.875]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_170_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_170_1.jpg"], "question": "Here is an object ([0.455, 0.276, 0.688, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.464, 0.276, 0.727, 1.0]\nB: [0.464, 0.276, 0.745, 0.993]\nC: [0.517, 0.276, 0.78, 1.0]\nD: [0.464, 0.276, 0.692, 0.875]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.534, 0.237, 0.687, 0.515]\nB: [0.534, 0.237, 0.662, 0.522]\nC: [0.534, 0.237, 0.641, 0.497]\nD: [0.499, 0.261, 0.628, 0.546]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_171_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_171_1.jpg"], "question": "Here is an object ([0.58, 0.235, 0.755, 0.518]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.534, 0.237, 0.687, 0.515]\nB: [0.534, 0.237, 0.662, 0.522]\nC: [0.534, 0.237, 0.641, 0.497]\nD: [0.499, 0.261, 0.628, 0.546]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.081, 0.196, 0.637, 1.131]\nB: [0.081, 0.196, 0.748, 1.113]\nC: [0.081, 0.196, 0.658, 0.994]\nD: [0.611, 0.761, 0.737, 0.843]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_172_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_172_1.jpg"], "question": "Here is an object ([0.136, 0.15, 0.672, 0.881]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.081, 0.196, 0.637, 1.131]\nB: [0.081, 0.196, 0.748, 1.113]\nC: [0.081, 0.196, 0.658, 0.994]\nD: [0.611, 0.761, 0.737, 0.843]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.23, 0.069, 0.931, 1.0]\nB: [0.23, 0.069, 0.792, 1.121]\nC: [0.218, 0.069, 0.919, 1.0]\nD: [0.457, 0.265, 0.69, 0.581]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_173_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_173_1.jpg"], "question": "Here is an object ([0.231, 0.124, 0.86, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.23, 0.069, 0.931, 1.0]\nB: [0.23, 0.069, 0.792, 1.121]\nC: [0.218, 0.069, 0.919, 1.0]\nD: [0.457, 0.265, 0.69, 0.581]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.159, 0.225, 0.294, 0.533]\nB: [0.218, 0.453, 0.636, 0.631]\nC: [0.292, 0.406, 0.459, 0.643]\nD: [0.292, 0.406, 0.456, 0.7]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_174_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_174_1.jpg"], "question": "Here is an object ([0.29, 0.426, 0.471, 0.7]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.159, 0.225, 0.294, 0.533]\nB: [0.218, 0.453, 0.636, 0.631]\nC: [0.292, 0.406, 0.459, 0.643]\nD: [0.292, 0.406, 0.456, 0.7]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.143, 0.454, 0.249, 0.654]\nB: [0.077, 0.669, 0.136, 0.985]\nC: [0.145, 0.525, 0.252, 0.725]\nD: [0.143, 0.454, 0.266, 0.657]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_175_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_175_1.jpg"], "question": "Here is an object ([0.12, 0.461, 0.237, 0.653]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.143, 0.454, 0.249, 0.654]\nB: [0.077, 0.669, 0.136, 0.985]\nC: [0.145, 0.525, 0.252, 0.725]\nD: [0.143, 0.454, 0.266, 0.657]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.602, 0.0, 0.946, 0.739]\nB: [0.468, 0.376, 0.48, 0.842]\nC: [0.44, 0.261, 0.783, 1.0]\nD: [0.393, 0.261, 0.736, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_176_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_176_1.jpg"], "question": "Here is an object ([0.446, 0.211, 0.622, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.602, 0.0, 0.946, 0.739]\nB: [0.468, 0.376, 0.48, 0.842]\nC: [0.44, 0.261, 0.783, 1.0]\nD: [0.393, 0.261, 0.736, 1.0]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.001, 0.722, 1.126]\nB: [0.0, 0.001, 0.598, 1.193]\nC: [0.0, 0.001, 0.724, 0.999]\nD: [0.0, 0.001, 0.738, 1.196]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_177_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_177_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.755, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.001, 0.722, 1.126]\nB: [0.0, 0.001, 0.598, 1.193]\nC: [0.0, 0.001, 0.724, 0.999]\nD: [0.0, 0.001, 0.738, 1.196]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.183, 0.761, 0.235, 0.919]\nB: [0.683, 0.257, 0.857, 0.718]\nC: [0.351, 0.0, 1.0, 1.0]\nD: [0.351, 0.0, 0.877, 0.803]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_178_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_178_1.jpg"], "question": "Here is an object ([0.313, 0.0, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.183, 0.761, 0.235, 0.919]\nB: [0.683, 0.257, 0.857, 0.718]\nC: [0.351, 0.0, 1.0, 1.0]\nD: [0.351, 0.0, 0.877, 0.803]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.334, 0.014, 1.103, 1.108]\nB: [0.2, 0.281, 0.454, 0.629]\nC: [0.334, 0.014, 0.926, 0.993]\nD: [0.334, 0.014, 1.0, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_179_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_179_1.jpg"], "question": "Here is an object ([0.235, 0.001, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.334, 0.014, 1.103, 1.108]\nB: [0.2, 0.281, 0.454, 0.629]\nC: [0.334, 0.014, 0.926, 0.993]\nD: [0.334, 0.014, 1.0, 1.0]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.356, 0.011, 0.83, 0.357]\nB: [0.183, 0.207, 0.581, 1.011]\nC: [0.183, 0.207, 0.68, 0.996]\nD: [0.183, 0.207, 0.616, 1.11]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_180_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_180_1.jpg"], "question": "Here is an object ([0.211, 0.165, 0.67, 0.982]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.356, 0.011, 0.83, 0.357]\nB: [0.183, 0.207, 0.581, 1.011]\nC: [0.183, 0.207, 0.68, 0.996]\nD: [0.183, 0.207, 0.616, 1.11]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.479, 0.108, 0.884, 0.665]\nB: [0.552, 0.097, 0.956, 0.654]\nC: [0.317, 0.204, 0.722, 0.761]\nD: [0.479, 0.108, 0.859, 0.699]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_181_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_181_1.jpg"], "question": "Here is an object ([0.457, 0.218, 0.777, 0.725]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.479, 0.108, 0.884, 0.665]\nB: [0.552, 0.097, 0.956, 0.654]\nC: [0.317, 0.204, 0.722, 0.761]\nD: [0.479, 0.108, 0.859, 0.699]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.603, 0.522, 0.715, 0.79]\nB: [0.531, 0.461, 0.641, 0.671]\nC: [0.523, 0.396, 0.632, 0.606]\nD: [0.537, 0.519, 0.702, 0.668]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_182_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_182_1.jpg"], "question": "Here is an object ([0.584, 0.392, 0.634, 0.551]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.603, 0.522, 0.715, 0.79]\nB: [0.531, 0.461, 0.641, 0.671]\nC: [0.523, 0.396, 0.632, 0.606]\nD: [0.537, 0.519, 0.702, 0.668]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.013, 0.871, 1.0]\nB: [0.129, 0.013, 1.047, 0.982]\nC: [0.696, 0.489, 0.793, 0.943]\nD: [0.129, 0.013, 1.0, 1.0]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_183_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_183_1.jpg"], "question": "Here is an object ([0.059, 0.0, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.013, 0.871, 1.0]\nB: [0.129, 0.013, 1.047, 0.982]\nC: [0.696, 0.489, 0.793, 0.943]\nD: [0.129, 0.013, 1.0, 1.0]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.42, 0.268, 0.625, 0.971]\nB: [0.42, 0.268, 0.636, 0.778]\nC: [0.42, 0.268, 0.66, 0.865]\nD: [0.42, 0.268, 0.639, 0.919]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_184_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_184_1.jpg"], "question": "Here is an object ([0.411, 0.272, 0.654, 0.865]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.42, 0.268, 0.625, 0.971]\nB: [0.42, 0.268, 0.636, 0.778]\nC: [0.42, 0.268, 0.66, 0.865]\nD: [0.42, 0.268, 0.639, 0.919]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.174, 0.0, 0.691, 0.558]\nB: [0.483, 0.21, 1.0, 0.768]\nC: [0.382, 0.046, 0.899, 0.604]\nD: [0.432, 0.364, 0.76, 0.779]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_185_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_185_1.jpg"], "question": "Here is an object ([0.384, 0.018, 0.968, 0.479]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.174, 0.0, 0.691, 0.558]\nB: [0.483, 0.21, 1.0, 0.768]\nC: [0.382, 0.046, 0.899, 0.604]\nD: [0.432, 0.364, 0.76, 0.779]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.595, 0.536, 0.865, 0.782]\nB: [0.595, 0.536, 0.829, 0.744]\nC: [0.074, 0.478, 0.275, 0.861]\nD: [0.059, 0.325, 0.287, 0.339]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_186_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_186_1.jpg"], "question": "Here is an object ([0.487, 0.554, 0.705, 0.758]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.595, 0.536, 0.865, 0.782]\nB: [0.595, 0.536, 0.829, 0.744]\nC: [0.074, 0.478, 0.275, 0.861]\nD: [0.059, 0.325, 0.287, 0.339]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.384, 0.524, 0.459, 0.842]\nB: [0.0, 0.0, 0.77, 0.999]\nC: [0.126, 0.49, 0.423, 0.603]\nD: [0.0, 0.0, 0.685, 0.894]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_187_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_187_1.jpg"], "question": "Here is an object ([0.0, 0.0, 0.784, 0.999]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.384, 0.524, 0.459, 0.842]\nB: [0.0, 0.0, 0.77, 0.999]\nC: [0.126, 0.49, 0.423, 0.603]\nD: [0.0, 0.0, 0.685, 0.894]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.0, 0.046, 0.939, 0.84]\nB: [0.057, 0.29, 0.25, 0.646]\nC: [0.578, 0.11, 0.852, 0.163]\nD: [0.0, 0.046, 0.89, 0.84]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_188_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_188_1.jpg"], "question": "Here is an object ([0.0, 0.001, 0.961, 0.874]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.046, 0.939, 0.84]\nB: [0.057, 0.29, 0.25, 0.646]\nC: [0.578, 0.11, 0.852, 0.163]\nD: [0.0, 0.046, 0.89, 0.84]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.334, 0.31, 0.566, 0.938]\nB: [0.275, 0.312, 0.504, 1.0]\nC: [0.334, 0.31, 0.563, 0.997]\nD: [0.591, 0.644, 0.888, 0.765]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_189_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_189_1.jpg"], "question": "Here is an object ([0.262, 0.143, 0.509, 0.997]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.334, 0.31, 0.566, 0.938]\nB: [0.275, 0.312, 0.504, 1.0]\nC: [0.334, 0.31, 0.563, 0.997]\nD: [0.591, 0.644, 0.888, 0.765]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.0, 0.565, 0.12, 0.9]\nB: [0.0, 0.565, 0.126, 0.917]\nC: [0.055, 0.589, 0.181, 0.94]\nD: [0.825, 0.094, 0.94, 0.535]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_190_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_190_1.jpg"], "question": "Here is an object ([0.0, 0.05, 1.0, 0.86]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.0, 0.565, 0.12, 0.9]\nB: [0.0, 0.565, 0.126, 0.917]\nC: [0.055, 0.589, 0.181, 0.94]\nD: [0.825, 0.094, 0.94, 0.535]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.644, 0.44, 0.805, 0.861]\nB: [0.587, 0.544, 0.748, 0.965]\nC: [0.644, 0.44, 0.811, 0.821]\nD: [0.644, 0.44, 0.801, 0.908]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_191_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_191_1.jpg"], "question": "Here is an object ([0.572, 0.41, 0.747, 0.842]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.644, 0.44, 0.805, 0.861]\nB: [0.587, 0.544, 0.748, 0.965]\nC: [0.644, 0.44, 0.811, 0.821]\nD: [0.644, 0.44, 0.801, 0.908]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.6, 0.292, 0.677, 0.412]\nB: [0.747, 0.479, 0.991, 1.056]\nC: [0.747, 0.479, 1.0, 1.0]\nD: [0.042, 0.16, 0.117, 0.547]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_192_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_192_1.jpg"], "question": "Here is an object ([0.755, 0.472, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.6, 0.292, 0.677, 0.412]\nB: [0.747, 0.479, 0.991, 1.056]\nC: [0.747, 0.479, 1.0, 1.0]\nD: [0.042, 0.16, 0.117, 0.547]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.268, 0.356, 0.665, 1.0]\nB: [0.384, 0.329, 0.781, 0.974]\nC: [0.5, 0.258, 0.897, 0.903]\nD: [0.466, 0.153, 0.863, 0.797]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_193_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_193_1.jpg"], "question": "Here is an object ([0.386, 0.329, 0.791, 0.968]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.268, 0.356, 0.665, 1.0]\nB: [0.384, 0.329, 0.781, 0.974]\nC: [0.5, 0.258, 0.897, 0.903]\nD: [0.466, 0.153, 0.863, 0.797]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.243, 0.956, 0.284, 0.975]\nB: [0.382, 0.301, 0.875, 0.646]\nC: [0.382, 0.301, 1.019, 0.606]\nD: [0.382, 0.301, 0.919, 0.646]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_194_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_194_1.jpg"], "question": "Here is an object ([0.411, 0.268, 0.728, 0.903]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 960 and the height is 720.", "context": "Select from the following choices.\nA: [0.243, 0.956, 0.284, 0.975]\nB: [0.382, 0.301, 0.875, 0.646]\nC: [0.382, 0.301, 1.019, 0.606]\nD: [0.382, 0.301, 0.919, 0.646]"}, "output": {"output_text": "D"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.388, 0.347, 0.992, 0.839]\nB: [0.388, 0.347, 0.977, 0.91]\nC: [0.477, 0.579, 0.912, 0.9]\nD: [0.388, 0.347, 1.089, 0.938]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_195_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_195_1.jpg"], "question": "Here is an object ([0.386, 0.367, 0.98, 0.921]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.388, 0.347, 0.992, 0.839]\nB: [0.388, 0.347, 0.977, 0.91]\nC: [0.477, 0.579, 0.912, 0.9]\nD: [0.388, 0.347, 1.089, 0.938]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.228, 0.114, 0.254, 0.601]\nB: [0.327, 0.138, 1.0, 1.0]\nC: [0.327, 0.138, 1.021, 0.939]\nD: [0.327, 0.0, 1.0, 0.863]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_196_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_196_1.jpg"], "question": "Here is an object ([0.332, 0.122, 1.0, 1.0]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.228, 0.114, 0.254, 0.601]\nB: [0.327, 0.138, 1.0, 1.0]\nC: [0.327, 0.138, 1.021, 0.939]\nD: [0.327, 0.0, 1.0, 0.863]"}, "output": {"output_text": "B"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.426, 0.447, 0.531, 0.756]\nB: [0.426, 0.447, 0.534, 0.776]\nC: [0.426, 0.447, 0.53, 0.783]\nD: [0.867, 0.138, 0.923, 0.214]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_197_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_197_1.jpg"], "question": "Here is an object ([0.431, 0.433, 0.585, 0.769]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.426, 0.447, 0.531, 0.756]\nB: [0.426, 0.447, 0.534, 0.776]\nC: [0.426, 0.447, 0.53, 0.783]\nD: [0.867, 0.138, 0.923, 0.214]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "youtubevis2019_sot", "options": "A: [0.341, 0.492, 0.753, 0.8]\nB: [0.152, 0.436, 0.563, 0.744]\nC: [0.168, 0.04, 0.502, 0.061]\nD: [0.593, 0.619, 0.761, 0.656]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_198_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_198_1.jpg"], "question": "Here is an object ([0.366, 0.504, 0.786, 0.806]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.341, 0.492, 0.753, 0.8]\nB: [0.152, 0.436, 0.563, 0.744]\nC: [0.168, 0.04, 0.502, 0.061]\nD: [0.593, 0.619, 0.761, 0.656]"}, "output": {"output_text": "A"}, "task": "single_object_tracking"}
{"source": "ovis_sot", "options": "A: [0.049, 0.143, 0.895, 0.719]\nB: [0.049, 0.143, 0.806, 0.606]\nC: [0.049, 0.143, 0.788, 0.667]\nD: [0.246, 0.05, 0.512, 0.212]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/single_object_tracking/single_object_tracking_199_0.jpg", "2D-spatial/single_object_tracking/single_object_tracking_199_1.jpg"], "question": "Here is an object ([0.056, 0.144, 0.791, 0.665]) in the Image 1. Please give the coordinations of this object in the Image 2. The bounding box coordinates are in the format [x1, y1, x2, y2], where [x1, y1] are the top-left coordinates and [x2, y2] are the bottom-right coordinates of the target object's bounding box. Note that the width of the input RGB image is 1280 and the height is 720.", "context": "Select from the following choices.\nA: [0.049, 0.143, 0.895, 0.719]\nB: [0.049, 0.143, 0.806, 0.606]\nC: [0.049, 0.143, 0.788, 0.667]\nD: [0.246, 0.05, 0.512, 0.212]"}, "output": {"output_text": "C"}, "task": "single_object_tracking"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -42.98651909317854, \"translation_dx\": 114.49293313374625, \"translation_dy\": -39.53290228333596, \"scale\": 1.442019387031135}\nB: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}\nC: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}\nD: {\"rotation_angle\": 52.0207999596704, \"translation_dx\": 62.052266940503074, \"translation_dy\": 15.318990484280505, \"scale\": 1.1445040102422772}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_0_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_0_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -42.98651909317854, \"translation_dx\": 114.49293313374625, \"translation_dy\": -39.53290228333596, \"scale\": 1.442019387031135}\nB: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}\nC: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}\nD: {\"rotation_angle\": 52.0207999596704, \"translation_dx\": 62.052266940503074, \"translation_dy\": 15.318990484280505, \"scale\": 1.1445040102422772}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}\nB: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}\nC: {\"rotation_angle\": 44.2601421515034, \"translation_dx\": -84.9832744911761, \"translation_dy\": -78.07982572554322, \"scale\": 0.5612120736859965}\nD: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_1_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_1_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}\nB: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}\nC: {\"rotation_angle\": 44.2601421515034, \"translation_dx\": -84.9832744911761, \"translation_dy\": -78.07982572554322, \"scale\": 0.5612120736859965}\nD: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -70.18179574394556, \"translation_dx\": -84.02989442213027, \"translation_dy\": 45.46342410564398, \"scale\": 1.28660403831869}\nB: {\"rotation_angle\": 1.3693998936690264, \"translation_dx\": -71.94174431428723, \"translation_dy\": 25.661133958182248, \"scale\": 1.468813327861592}\nC: {\"rotation_angle\": -110.51021822636605, \"translation_dx\": -17.924195571284486, \"translation_dy\": -0.10679752473519954, \"scale\": 1.4066663412939815}\nD: {\"rotation_angle\": 141.74747753602782, \"translation_dx\": -54.793360600935046, \"translation_dy\": -29.72546528603263, \"scale\": 0.6563706152769926}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_2_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_2_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -70.18179574394556, \"translation_dx\": -84.02989442213027, \"translation_dy\": 45.46342410564398, \"scale\": 1.28660403831869}\nB: {\"rotation_angle\": 1.3693998936690264, \"translation_dx\": -71.94174431428723, \"translation_dy\": 25.661133958182248, \"scale\": 1.468813327861592}\nC: {\"rotation_angle\": -110.51021822636605, \"translation_dx\": -17.924195571284486, \"translation_dy\": -0.10679752473519954, \"scale\": 1.4066663412939815}\nD: {\"rotation_angle\": 141.74747753602782, \"translation_dx\": -54.793360600935046, \"translation_dy\": -29.72546528603263, \"scale\": 0.6563706152769926}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -59.18065174130953, \"translation_dx\": -66.15733764198566, \"translation_dy\": -32.06450758946801, \"scale\": 1.1967157159259998}\nB: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}\nC: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nD: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_3_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_3_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -59.18065174130953, \"translation_dx\": -66.15733764198566, \"translation_dy\": -32.06450758946801, \"scale\": 1.1967157159259998}\nB: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}\nC: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nD: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}\nB: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nC: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nD: {\"rotation_angle\": 4.3566947214011975, \"translation_dx\": 60.69356846846577, \"translation_dy\": 19.542677658157032, \"scale\": 1.353031271581857}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_4_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_4_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}\nB: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nC: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nD: {\"rotation_angle\": 4.3566947214011975, \"translation_dx\": 60.69356846846577, \"translation_dy\": 19.542677658157032, \"scale\": 1.353031271581857}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}\nB: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}\nC: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nD: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_5_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_5_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}\nB: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}\nC: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nD: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 67.74863170033868, \"translation_dx\": 0.9436916559104702, \"translation_dy\": 79.02717939495389, \"scale\": 1.0490112177140545}\nB: {\"rotation_angle\": -149.34069149386406, \"translation_dx\": 81.63420911320063, \"translation_dy\": -26.073567429384056, \"scale\": 1.427947630130646}\nC: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nD: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_6_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_6_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 67.74863170033868, \"translation_dx\": 0.9436916559104702, \"translation_dy\": 79.02717939495389, \"scale\": 1.0490112177140545}\nB: {\"rotation_angle\": -149.34069149386406, \"translation_dx\": 81.63420911320063, \"translation_dy\": -26.073567429384056, \"scale\": 1.427947630130646}\nC: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nD: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 178.3015459217881, \"translation_dx\": 2.1592483018484785, \"translation_dy\": -86.15095567396924, \"scale\": 1.206185814877298}\nB: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}\nC: {\"rotation_angle\": 179.8013352752547, \"translation_dx\": -90.5548533247824, \"translation_dy\": 17.23782922418306, \"scale\": 0.9885365626195518}\nD: {\"rotation_angle\": 49.90656423603761, \"translation_dx\": 85.27067294320437, \"translation_dy\": -8.928665399863448, \"scale\": 0.9370060594249733}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_7_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_7_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 178.3015459217881, \"translation_dx\": 2.1592483018484785, \"translation_dy\": -86.15095567396924, \"scale\": 1.206185814877298}\nB: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}\nC: {\"rotation_angle\": 179.8013352752547, \"translation_dx\": -90.5548533247824, \"translation_dy\": 17.23782922418306, \"scale\": 0.9885365626195518}\nD: {\"rotation_angle\": 49.90656423603761, \"translation_dx\": 85.27067294320437, \"translation_dy\": -8.928665399863448, \"scale\": 0.9370060594249733}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}\nB: {\"rotation_angle\": -126.23248080179604, \"translation_dx\": -18.04313623288388, \"translation_dy\": 59.052880720386156, \"scale\": 1.3827835175940266}\nC: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}\nD: {\"rotation_angle\": -68.79930104020924, \"translation_dx\": -103.12901971602221, \"translation_dy\": 94.89161684072867, \"scale\": 1.2295411735859756}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_8_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_8_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}\nB: {\"rotation_angle\": -126.23248080179604, \"translation_dx\": -18.04313623288388, \"translation_dy\": 59.052880720386156, \"scale\": 1.3827835175940266}\nC: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}\nD: {\"rotation_angle\": -68.79930104020924, \"translation_dx\": -103.12901971602221, \"translation_dy\": 94.89161684072867, \"scale\": 1.2295411735859756}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nB: {\"rotation_angle\": -0.45613579718829556, \"translation_dx\": 98.71619714866841, \"translation_dy\": 70.1100439641223, \"scale\": 0.6491919010173006}\nC: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nD: {\"rotation_angle\": -79.27003163090343, \"translation_dx\": 8.207736130313549, \"translation_dy\": 6.670417118750038, \"scale\": 1.3327657238113826}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_9_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_9_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nB: {\"rotation_angle\": -0.45613579718829556, \"translation_dx\": 98.71619714866841, \"translation_dy\": 70.1100439641223, \"scale\": 0.6491919010173006}\nC: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nD: {\"rotation_angle\": -79.27003163090343, \"translation_dx\": 8.207736130313549, \"translation_dy\": 6.670417118750038, \"scale\": 1.3327657238113826}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}\nB: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}\nC: {\"rotation_angle\": -94.06455293225282, \"translation_dx\": -52.04430006776356, \"translation_dy\": 88.55937507710391, \"scale\": 0.8369046461483086}\nD: {\"rotation_angle\": 136.76946369368522, \"translation_dx\": 86.13615517916296, \"translation_dy\": 47.49597577737802, \"scale\": 1.1842967613683704}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_10_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_10_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}\nB: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}\nC: {\"rotation_angle\": -94.06455293225282, \"translation_dx\": -52.04430006776356, \"translation_dy\": 88.55937507710391, \"scale\": 0.8369046461483086}\nD: {\"rotation_angle\": 136.76946369368522, \"translation_dx\": 86.13615517916296, \"translation_dy\": 47.49597577737802, \"scale\": 1.1842967613683704}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nB: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nC: {\"rotation_angle\": -23.02063628299686, \"translation_dx\": -42.06347070905805, \"translation_dy\": 68.90308226059909, \"scale\": 0.7321107429069119}\nD: {\"rotation_angle\": 33.426384392539006, \"translation_dx\": -12.448609293998487, \"translation_dy\": 64.03367069956386, \"scale\": 0.6340926377236346}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_11_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_11_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nB: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nC: {\"rotation_angle\": -23.02063628299686, \"translation_dx\": -42.06347070905805, \"translation_dy\": 68.90308226059909, \"scale\": 0.7321107429069119}\nD: {\"rotation_angle\": 33.426384392539006, \"translation_dx\": -12.448609293998487, \"translation_dy\": 64.03367069956386, \"scale\": 0.6340926377236346}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nB: {\"rotation_angle\": -123.92621597373325, \"translation_dx\": 115.25994331141689, \"translation_dy\": -45.13111299141354, \"scale\": 1.164470344420729}\nC: {\"rotation_angle\": 95.56102360167273, \"translation_dx\": -57.629857243876444, \"translation_dy\": -95.34824117323305, \"scale\": 0.9533126568708786}\nD: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_12_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_12_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nB: {\"rotation_angle\": -123.92621597373325, \"translation_dx\": 115.25994331141689, \"translation_dy\": -45.13111299141354, \"scale\": 1.164470344420729}\nC: {\"rotation_angle\": 95.56102360167273, \"translation_dx\": -57.629857243876444, \"translation_dy\": -95.34824117323305, \"scale\": 0.9533126568708786}\nD: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -124.74198080809023, \"translation_dx\": -48.23531115232953, \"translation_dy\": 52.62526617026404, \"scale\": 1.3484625774406969}\nB: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}\nC: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nD: {\"rotation_angle\": 110.02825264959768, \"translation_dx\": -53.26387197670213, \"translation_dy\": 88.43864976013427, \"scale\": 1.4833645013101147}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_13_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_13_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -124.74198080809023, \"translation_dx\": -48.23531115232953, \"translation_dy\": 52.62526617026404, \"scale\": 1.3484625774406969}\nB: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}\nC: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nD: {\"rotation_angle\": 110.02825264959768, \"translation_dx\": -53.26387197670213, \"translation_dy\": 88.43864976013427, \"scale\": 1.4833645013101147}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}\nB: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}\nC: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nD: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_14_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_14_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}\nB: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}\nC: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nD: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nB: {\"rotation_angle\": -51.98717119490195, \"translation_dx\": -83.93544420557635, \"translation_dy\": -17.359661719977098, \"scale\": 1.0858344969275349}\nC: {\"rotation_angle\": 162.9787629733711, \"translation_dx\": 56.68968820785494, \"translation_dy\": 63.47754229449794, \"scale\": 0.7767697180212818}\nD: {\"rotation_angle\": 74.4727172984789, \"translation_dx\": 83.0498783040965, \"translation_dy\": 24.573318419119772, \"scale\": 1.4775593630739356}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_15_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_15_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nB: {\"rotation_angle\": -51.98717119490195, \"translation_dx\": -83.93544420557635, \"translation_dy\": -17.359661719977098, \"scale\": 1.0858344969275349}\nC: {\"rotation_angle\": 162.9787629733711, \"translation_dx\": 56.68968820785494, \"translation_dy\": 63.47754229449794, \"scale\": 0.7767697180212818}\nD: {\"rotation_angle\": 74.4727172984789, \"translation_dx\": 83.0498783040965, \"translation_dy\": 24.573318419119772, \"scale\": 1.4775593630739356}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -32.96407209098831, \"translation_dx\": -27.518946535455143, \"translation_dy\": 2.5370159689679213, \"scale\": 1.259328459428434}\nB: {\"rotation_angle\": -70.18179574394556, \"translation_dx\": -84.02989442213027, \"translation_dy\": 45.46342410564398, \"scale\": 1.28660403831869}\nC: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}\nD: {\"rotation_angle\": -103.24791656906933, \"translation_dx\": -2.2454836983213227, \"translation_dy\": 24.014319900588845, \"scale\": 1.3204557483507742}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_16_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_16_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -32.96407209098831, \"translation_dx\": -27.518946535455143, \"translation_dy\": 2.5370159689679213, \"scale\": 1.259328459428434}\nB: {\"rotation_angle\": -70.18179574394556, \"translation_dx\": -84.02989442213027, \"translation_dy\": 45.46342410564398, \"scale\": 1.28660403831869}\nC: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}\nD: {\"rotation_angle\": -103.24791656906933, \"translation_dx\": -2.2454836983213227, \"translation_dy\": 24.014319900588845, \"scale\": 1.3204557483507742}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}\nB: {\"rotation_angle\": -148.06770236959966, \"translation_dx\": 76.71938731609727, \"translation_dy\": 125.67697929104389, \"scale\": 1.1600663307259453}\nC: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nD: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_17_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_17_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}\nB: {\"rotation_angle\": -148.06770236959966, \"translation_dx\": 76.71938731609727, \"translation_dy\": 125.67697929104389, \"scale\": 1.1600663307259453}\nC: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nD: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 98.62110540120432, \"translation_dx\": 55.8324503005326, \"translation_dy\": -53.32963696213369, \"scale\": 1.3342375308232577}\nB: {\"rotation_angle\": 133.22970053001933, \"translation_dx\": 30.83867253278636, \"translation_dy\": 9.987607615316023, \"scale\": 0.9746642566652708}\nC: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nD: {\"rotation_angle\": -110.51021822636605, \"translation_dx\": -17.924195571284486, \"translation_dy\": -0.10679752473519954, \"scale\": 1.4066663412939815}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_18_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_18_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 98.62110540120432, \"translation_dx\": 55.8324503005326, \"translation_dy\": -53.32963696213369, \"scale\": 1.3342375308232577}\nB: {\"rotation_angle\": 133.22970053001933, \"translation_dx\": 30.83867253278636, \"translation_dy\": 9.987607615316023, \"scale\": 0.9746642566652708}\nC: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nD: {\"rotation_angle\": -110.51021822636605, \"translation_dx\": -17.924195571284486, \"translation_dy\": -0.10679752473519954, \"scale\": 1.4066663412939815}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 98.12478073081388, \"translation_dx\": 82.24255679101596, \"translation_dy\": 10.638794739410258, \"scale\": 1.454613875934863}\nB: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nC: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nD: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_19_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_19_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 98.12478073081388, \"translation_dx\": 82.24255679101596, \"translation_dy\": 10.638794739410258, \"scale\": 1.454613875934863}\nB: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nC: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nD: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 47.16467358014893, \"translation_dx\": -87.19318159487975, \"translation_dy\": -49.56686010575127, \"scale\": 1.2416587716965684}\nB: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}\nC: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nD: {\"rotation_angle\": -126.15991399279281, \"translation_dx\": 24.895638463286446, \"translation_dy\": -35.71086816730676, \"scale\": 1.30648936857296}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_20_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_20_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 47.16467358014893, \"translation_dx\": -87.19318159487975, \"translation_dy\": -49.56686010575127, \"scale\": 1.2416587716965684}\nB: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}\nC: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nD: {\"rotation_angle\": -126.15991399279281, \"translation_dx\": 24.895638463286446, \"translation_dy\": -35.71086816730676, \"scale\": 1.30648936857296}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}\nB: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nC: {\"rotation_angle\": 44.2601421515034, \"translation_dx\": -84.9832744911761, \"translation_dy\": -78.07982572554322, \"scale\": 0.5612120736859965}\nD: {\"rotation_angle\": -72.82027143369304, \"translation_dx\": -44.85481158127062, \"translation_dy\": 106.69131407191517, \"scale\": 0.716080341101258}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_21_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_21_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}\nB: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nC: {\"rotation_angle\": 44.2601421515034, \"translation_dx\": -84.9832744911761, \"translation_dy\": -78.07982572554322, \"scale\": 0.5612120736859965}\nD: {\"rotation_angle\": -72.82027143369304, \"translation_dx\": -44.85481158127062, \"translation_dy\": 106.69131407191517, \"scale\": 0.716080341101258}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -70.97525301082955, \"translation_dx\": -28.380848037876873, \"translation_dy\": 54.37723426674512, \"scale\": 0.9024922197892329}\nB: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}\nC: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nD: {\"rotation_angle\": 127.0599036632886, \"translation_dx\": -26.73103881794438, \"translation_dy\": 16.785326739741976, \"scale\": 1.1214331244941351}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_22_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_22_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -70.97525301082955, \"translation_dx\": -28.380848037876873, \"translation_dy\": 54.37723426674512, \"scale\": 0.9024922197892329}\nB: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}\nC: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nD: {\"rotation_angle\": 127.0599036632886, \"translation_dx\": -26.73103881794438, \"translation_dy\": 16.785326739741976, \"scale\": 1.1214331244941351}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nB: {\"rotation_angle\": 168.86687879669455, \"translation_dx\": 30.327287286076626, \"translation_dy\": -73.84263373893171, \"scale\": 1.0887904122788439}\nC: {\"rotation_angle\": -126.23248080179604, \"translation_dx\": -18.04313623288388, \"translation_dy\": 59.052880720386156, \"scale\": 1.3827835175940266}\nD: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_23_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_23_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nB: {\"rotation_angle\": 168.86687879669455, \"translation_dx\": 30.327287286076626, \"translation_dy\": -73.84263373893171, \"scale\": 1.0887904122788439}\nC: {\"rotation_angle\": -126.23248080179604, \"translation_dx\": -18.04313623288388, \"translation_dy\": 59.052880720386156, \"scale\": 1.3827835175940266}\nD: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}\nB: {\"rotation_angle\": -115.34417090075787, \"translation_dx\": -118.63121430094503, \"translation_dy\": 41.63412082488844, \"scale\": 0.9001856788272352}\nC: {\"rotation_angle\": 159.18509857624855, \"translation_dx\": 94.5972413522399, \"translation_dy\": -87.01463724053234, \"scale\": 0.7914176569510836}\nD: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_24_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_24_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}\nB: {\"rotation_angle\": -115.34417090075787, \"translation_dx\": -118.63121430094503, \"translation_dy\": 41.63412082488844, \"scale\": 0.9001856788272352}\nC: {\"rotation_angle\": 159.18509857624855, \"translation_dx\": 94.5972413522399, \"translation_dy\": -87.01463724053234, \"scale\": 0.7914176569510836}\nD: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}\nB: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}\nC: {\"rotation_angle\": 28.728757892682808, \"translation_dx\": 12.065384659700086, \"translation_dy\": -119.64549643343977, \"scale\": 1.126100132224236}\nD: {\"rotation_angle\": 28.728757892682808, \"translation_dx\": 12.065384659700086, \"translation_dy\": -119.64549643343977, \"scale\": 1.126100132224236}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_25_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_25_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}\nB: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}\nC: {\"rotation_angle\": 28.728757892682808, \"translation_dx\": 12.065384659700086, \"translation_dy\": -119.64549643343977, \"scale\": 1.126100132224236}\nD: {\"rotation_angle\": 28.728757892682808, \"translation_dx\": 12.065384659700086, \"translation_dy\": -119.64549643343977, \"scale\": 1.126100132224236}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}\nB: {\"rotation_angle\": 134.22497079750707, \"translation_dx\": -56.33244292094708, \"translation_dy\": 12.15417280277697, \"scale\": 1.260404381889235}\nC: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}\nD: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_26_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_26_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}\nB: {\"rotation_angle\": 134.22497079750707, \"translation_dx\": -56.33244292094708, \"translation_dy\": 12.15417280277697, \"scale\": 1.260404381889235}\nC: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}\nD: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 51.652651058291696, \"translation_dx\": -79.60059266318888, \"translation_dy\": 40.24223939512936, \"scale\": 1.045377495061187}\nB: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}\nC: {\"rotation_angle\": 106.62912259997893, \"translation_dx\": -62.19399566166837, \"translation_dy\": -63.078041204745844, \"scale\": 1.4577244189370733}\nD: {\"rotation_angle\": -44.902472769484746, \"translation_dx\": -36.85475324083902, \"translation_dy\": 36.81692000181951, \"scale\": 1.0769710077370194}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_27_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_27_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 51.652651058291696, \"translation_dx\": -79.60059266318888, \"translation_dy\": 40.24223939512936, \"scale\": 1.045377495061187}\nB: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}\nC: {\"rotation_angle\": 106.62912259997893, \"translation_dx\": -62.19399566166837, \"translation_dy\": -63.078041204745844, \"scale\": 1.4577244189370733}\nD: {\"rotation_angle\": -44.902472769484746, \"translation_dx\": -36.85475324083902, \"translation_dy\": 36.81692000181951, \"scale\": 1.0769710077370194}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 127.1396993936072, \"translation_dx\": -29.08894824101361, \"translation_dy\": -80.84475014775404, \"scale\": 1.2834497894588772}\nB: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nC: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}\nD: {\"rotation_angle\": -147.17742740700606, \"translation_dx\": 99.79022385553455, \"translation_dy\": -46.32888217161055, \"scale\": 1.2561938294527635}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_28_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_28_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 127.1396993936072, \"translation_dx\": -29.08894824101361, \"translation_dy\": -80.84475014775404, \"scale\": 1.2834497894588772}\nB: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nC: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}\nD: {\"rotation_angle\": -147.17742740700606, \"translation_dx\": 99.79022385553455, \"translation_dy\": -46.32888217161055, \"scale\": 1.2561938294527635}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nB: {\"rotation_angle\": -37.8135886633452, \"translation_dx\": 94.09848811207868, \"translation_dy\": -28.846940165704815, \"scale\": 0.7423292461324351}\nC: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nD: {\"rotation_angle\": -84.90425841207441, \"translation_dx\": -96.22975116611923, \"translation_dy\": -54.13037688992304, \"scale\": 1.161476925450186}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_29_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_29_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nB: {\"rotation_angle\": -37.8135886633452, \"translation_dx\": 94.09848811207868, \"translation_dy\": -28.846940165704815, \"scale\": 0.7423292461324351}\nC: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nD: {\"rotation_angle\": -84.90425841207441, \"translation_dx\": -96.22975116611923, \"translation_dy\": -54.13037688992304, \"scale\": 1.161476925450186}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 18.52926347539298, \"translation_dx\": -26.155433185237058, \"translation_dy\": -39.799299198218556, \"scale\": 0.9355127285855813}\nB: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nC: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nD: {\"rotation_angle\": -32.057796286961064, \"translation_dx\": 119.50392135854452, \"translation_dy\": -17.786253698900993, \"scale\": 1.4583062003808291}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_30_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_30_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 18.52926347539298, \"translation_dx\": -26.155433185237058, \"translation_dy\": -39.799299198218556, \"scale\": 0.9355127285855813}\nB: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nC: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nD: {\"rotation_angle\": -32.057796286961064, \"translation_dx\": 119.50392135854452, \"translation_dy\": -17.786253698900993, \"scale\": 1.4583062003808291}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nB: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nC: {\"rotation_angle\": 47.16467358014893, \"translation_dx\": -87.19318159487975, \"translation_dy\": -49.56686010575127, \"scale\": 1.2416587716965684}\nD: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_31_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_31_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nB: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nC: {\"rotation_angle\": 47.16467358014893, \"translation_dx\": -87.19318159487975, \"translation_dy\": -49.56686010575127, \"scale\": 1.2416587716965684}\nD: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 160.04018122869564, \"translation_dx\": -10.031879581871024, \"translation_dy\": 74.10075881851205, \"scale\": 0.8976020445815951}\nB: {\"rotation_angle\": -131.1795029858263, \"translation_dx\": 17.908074544940433, \"translation_dy\": 120.17637833747304, \"scale\": 0.9471882483559888}\nC: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nD: {\"rotation_angle\": -117.26843352521382, \"translation_dx\": 17.28573283600312, \"translation_dy\": -92.45781352854672, \"scale\": 1.478727361005855}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_32_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_32_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 160.04018122869564, \"translation_dx\": -10.031879581871024, \"translation_dy\": 74.10075881851205, \"scale\": 0.8976020445815951}\nB: {\"rotation_angle\": -131.1795029858263, \"translation_dx\": 17.908074544940433, \"translation_dy\": 120.17637833747304, \"scale\": 0.9471882483559888}\nC: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nD: {\"rotation_angle\": -117.26843352521382, \"translation_dx\": 17.28573283600312, \"translation_dy\": -92.45781352854672, \"scale\": 1.478727361005855}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 33.36657735274014, \"translation_dx\": -110.42271839281483, \"translation_dy\": 35.783043595963875, \"scale\": 1.1017945125321793}\nB: {\"rotation_angle\": -153.95647753312159, \"translation_dx\": 64.08546266437509, \"translation_dy\": -34.554486291313935, \"scale\": 1.423360690418288}\nC: {\"rotation_angle\": 162.98131081099467, \"translation_dx\": -80.19473687776261, \"translation_dy\": -17.70282064458462, \"scale\": 1.2855975600149028}\nD: {\"rotation_angle\": 127.0599036632886, \"translation_dx\": -26.73103881794438, \"translation_dy\": 16.785326739741976, \"scale\": 1.1214331244941351}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_33_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_33_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 33.36657735274014, \"translation_dx\": -110.42271839281483, \"translation_dy\": 35.783043595963875, \"scale\": 1.1017945125321793}\nB: {\"rotation_angle\": -153.95647753312159, \"translation_dx\": 64.08546266437509, \"translation_dy\": -34.554486291313935, \"scale\": 1.423360690418288}\nC: {\"rotation_angle\": 162.98131081099467, \"translation_dx\": -80.19473687776261, \"translation_dy\": -17.70282064458462, \"scale\": 1.2855975600149028}\nD: {\"rotation_angle\": 127.0599036632886, \"translation_dx\": -26.73103881794438, \"translation_dy\": 16.785326739741976, \"scale\": 1.1214331244941351}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}\nB: {\"rotation_angle\": -92.49508697379828, \"translation_dx\": 63.09853740086383, \"translation_dy\": 99.47995409556995, \"scale\": 0.9495145406508286}\nC: {\"rotation_angle\": 8.705969178532513, \"translation_dx\": -108.98578445869327, \"translation_dy\": -85.91179454441009, \"scale\": 0.5132717751865925}\nD: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_34_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_34_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}\nB: {\"rotation_angle\": -92.49508697379828, \"translation_dx\": 63.09853740086383, \"translation_dy\": 99.47995409556995, \"scale\": 0.9495145406508286}\nC: {\"rotation_angle\": 8.705969178532513, \"translation_dx\": -108.98578445869327, \"translation_dy\": -85.91179454441009, \"scale\": 0.5132717751865925}\nD: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -8.756342422911757, \"translation_dx\": -120.12147874311805, \"translation_dy\": -16.659510954699698, \"scale\": 0.8471832394055047}\nB: {\"rotation_angle\": 88.55038325147228, \"translation_dx\": -17.272344447388633, \"translation_dy\": -67.72549137992362, \"scale\": 0.5810098703790367}\nC: {\"rotation_angle\": 1.3693998936690264, \"translation_dx\": -71.94174431428723, \"translation_dy\": 25.661133958182248, \"scale\": 1.468813327861592}\nD: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_35_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_35_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -8.756342422911757, \"translation_dx\": -120.12147874311805, \"translation_dy\": -16.659510954699698, \"scale\": 0.8471832394055047}\nB: {\"rotation_angle\": 88.55038325147228, \"translation_dx\": -17.272344447388633, \"translation_dy\": -67.72549137992362, \"scale\": 0.5810098703790367}\nC: {\"rotation_angle\": 1.3693998936690264, \"translation_dx\": -71.94174431428723, \"translation_dy\": 25.661133958182248, \"scale\": 1.468813327861592}\nD: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -41.748048059314925, \"translation_dx\": 84.2495675740148, \"translation_dy\": -81.02778113177463, \"scale\": 1.207158201764622}\nB: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}\nC: {\"rotation_angle\": 52.0207999596704, \"translation_dx\": 62.052266940503074, \"translation_dy\": 15.318990484280505, \"scale\": 1.1445040102422772}\nD: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_36_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_36_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -41.748048059314925, \"translation_dx\": 84.2495675740148, \"translation_dy\": -81.02778113177463, \"scale\": 1.207158201764622}\nB: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}\nC: {\"rotation_angle\": 52.0207999596704, \"translation_dx\": 62.052266940503074, \"translation_dy\": 15.318990484280505, \"scale\": 1.1445040102422772}\nD: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 130.382151153576, \"translation_dx\": 48.77925626504499, \"translation_dy\": 54.89982459749416, \"scale\": 1.3647831130001666}\nB: {\"rotation_angle\": -126.15991399279281, \"translation_dx\": 24.895638463286446, \"translation_dy\": -35.71086816730676, \"scale\": 1.30648936857296}\nC: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}\nD: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_37_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_37_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 130.382151153576, \"translation_dx\": 48.77925626504499, \"translation_dy\": 54.89982459749416, \"scale\": 1.3647831130001666}\nB: {\"rotation_angle\": -126.15991399279281, \"translation_dx\": 24.895638463286446, \"translation_dy\": -35.71086816730676, \"scale\": 1.30648936857296}\nC: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}\nD: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nB: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}\nC: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}\nD: {\"rotation_angle\": 178.3015459217881, \"translation_dx\": 2.1592483018484785, \"translation_dy\": -86.15095567396924, \"scale\": 1.206185814877298}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_38_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_38_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nB: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}\nC: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}\nD: {\"rotation_angle\": 178.3015459217881, \"translation_dx\": 2.1592483018484785, \"translation_dy\": -86.15095567396924, \"scale\": 1.206185814877298}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -81.11314702551758, \"translation_dx\": -115.5554336511824, \"translation_dy\": 81.04425747964075, \"scale\": 0.8604764063335847}\nB: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nC: {\"rotation_angle\": 22.924180775031914, \"translation_dx\": 8.278066534063711, \"translation_dy\": 39.03722404706397, \"scale\": 0.6972670428813228}\nD: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_39_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_39_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -81.11314702551758, \"translation_dx\": -115.5554336511824, \"translation_dy\": 81.04425747964075, \"scale\": 0.8604764063335847}\nB: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nC: {\"rotation_angle\": 22.924180775031914, \"translation_dx\": 8.278066534063711, \"translation_dy\": 39.03722404706397, \"scale\": 0.6972670428813228}\nD: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -79.27003163090343, \"translation_dx\": 8.207736130313549, \"translation_dy\": 6.670417118750038, \"scale\": 1.3327657238113826}\nB: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}\nC: {\"rotation_angle\": 49.90656423603761, \"translation_dx\": 85.27067294320437, \"translation_dy\": -8.928665399863448, \"scale\": 0.9370060594249733}\nD: {\"rotation_angle\": -72.82027143369304, \"translation_dx\": -44.85481158127062, \"translation_dy\": 106.69131407191517, \"scale\": 0.716080341101258}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_40_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_40_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -79.27003163090343, \"translation_dx\": 8.207736130313549, \"translation_dy\": 6.670417118750038, \"scale\": 1.3327657238113826}\nB: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}\nC: {\"rotation_angle\": 49.90656423603761, \"translation_dx\": 85.27067294320437, \"translation_dy\": -8.928665399863448, \"scale\": 0.9370060594249733}\nD: {\"rotation_angle\": -72.82027143369304, \"translation_dx\": -44.85481158127062, \"translation_dy\": 106.69131407191517, \"scale\": 0.716080341101258}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -161.22593365548192, \"translation_dx\": -119.73961882572601, \"translation_dy\": -93.50838821854722, \"scale\": 1.4476413063179399}\nB: {\"rotation_angle\": 52.0207999596704, \"translation_dx\": 62.052266940503074, \"translation_dy\": 15.318990484280505, \"scale\": 1.1445040102422772}\nC: {\"rotation_angle\": -98.17490649350026, \"translation_dx\": 5.744855173473269, \"translation_dy\": -10.705504600001973, \"scale\": 1.1182428392253487}\nD: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_41_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_41_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -161.22593365548192, \"translation_dx\": -119.73961882572601, \"translation_dy\": -93.50838821854722, \"scale\": 1.4476413063179399}\nB: {\"rotation_angle\": 52.0207999596704, \"translation_dx\": 62.052266940503074, \"translation_dy\": 15.318990484280505, \"scale\": 1.1445040102422772}\nC: {\"rotation_angle\": -98.17490649350026, \"translation_dx\": 5.744855173473269, \"translation_dy\": -10.705504600001973, \"scale\": 1.1182428392253487}\nD: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -98.17490649350026, \"translation_dx\": 5.744855173473269, \"translation_dy\": -10.705504600001973, \"scale\": 1.1182428392253487}\nB: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}\nC: {\"rotation_angle\": -61.308258156024195, \"translation_dx\": -92.42627707406731, \"translation_dy\": -21.076199203141364, \"scale\": 1.1133621977071444}\nD: {\"rotation_angle\": -22.98450105670534, \"translation_dx\": -24.343109907781525, \"translation_dy\": -75.50859401578859, \"scale\": 0.5077440368943875}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_42_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_42_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -98.17490649350026, \"translation_dx\": 5.744855173473269, \"translation_dy\": -10.705504600001973, \"scale\": 1.1182428392253487}\nB: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}\nC: {\"rotation_angle\": -61.308258156024195, \"translation_dx\": -92.42627707406731, \"translation_dy\": -21.076199203141364, \"scale\": 1.1133621977071444}\nD: {\"rotation_angle\": -22.98450105670534, \"translation_dx\": -24.343109907781525, \"translation_dy\": -75.50859401578859, \"scale\": 0.5077440368943875}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": -101.64893396855386, \"translation_dx\": -96.08306753711838, \"translation_dy\": 14.852477797043775, \"scale\": 1.3017377870800058}\nC: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nD: {\"rotation_angle\": -35.37165300247324, \"translation_dx\": -51.674784510203665, \"translation_dy\": 35.0550301640573, \"scale\": 1.181842779166554}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_43_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_43_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": -101.64893396855386, \"translation_dx\": -96.08306753711838, \"translation_dy\": 14.852477797043775, \"scale\": 1.3017377870800058}\nC: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nD: {\"rotation_angle\": -35.37165300247324, \"translation_dx\": -51.674784510203665, \"translation_dy\": 35.0550301640573, \"scale\": 1.181842779166554}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -103.5561502427767, \"translation_dx\": -75.76940431238745, \"translation_dy\": -48.3479107136017, \"scale\": 1.0522987713432983}\nB: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}\nC: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}\nD: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_44_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_44_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -103.5561502427767, \"translation_dx\": -75.76940431238745, \"translation_dy\": -48.3479107136017, \"scale\": 1.0522987713432983}\nB: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}\nC: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}\nD: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}\nB: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nC: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}\nD: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_45_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_45_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}\nB: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nC: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}\nD: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}\nB: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nC: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}\nD: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_46_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_46_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}\nB: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nC: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}\nD: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 45.786611297437304, \"translation_dx\": 45.53183354666939, \"translation_dy\": -112.45880863798888, \"scale\": 0.5686394776423458}\nB: {\"rotation_angle\": -101.64893396855386, \"translation_dx\": -96.08306753711838, \"translation_dy\": 14.852477797043775, \"scale\": 1.3017377870800058}\nC: {\"rotation_angle\": 98.62110540120432, \"translation_dx\": 55.8324503005326, \"translation_dy\": -53.32963696213369, \"scale\": 1.3342375308232577}\nD: {\"rotation_angle\": 98.12478073081388, \"translation_dx\": 82.24255679101596, \"translation_dy\": 10.638794739410258, \"scale\": 1.454613875934863}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_47_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_47_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 45.786611297437304, \"translation_dx\": 45.53183354666939, \"translation_dy\": -112.45880863798888, \"scale\": 0.5686394776423458}\nB: {\"rotation_angle\": -101.64893396855386, \"translation_dx\": -96.08306753711838, \"translation_dy\": 14.852477797043775, \"scale\": 1.3017377870800058}\nC: {\"rotation_angle\": 98.62110540120432, \"translation_dx\": 55.8324503005326, \"translation_dy\": -53.32963696213369, \"scale\": 1.3342375308232577}\nD: {\"rotation_angle\": 98.12478073081388, \"translation_dx\": 82.24255679101596, \"translation_dy\": 10.638794739410258, \"scale\": 1.454613875934863}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nB: {\"rotation_angle\": 162.6656255846617, \"translation_dx\": -24.713919503645087, \"translation_dy\": -0.6846177496217649, \"scale\": 0.967192316827237}\nC: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nD: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_48_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_48_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nB: {\"rotation_angle\": 162.6656255846617, \"translation_dx\": -24.713919503645087, \"translation_dy\": -0.6846177496217649, \"scale\": 0.967192316827237}\nC: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nD: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nB: {\"rotation_angle\": 49.90656423603761, \"translation_dx\": 85.27067294320437, \"translation_dy\": -8.928665399863448, \"scale\": 0.9370060594249733}\nC: {\"rotation_angle\": -41.748048059314925, \"translation_dx\": 84.2495675740148, \"translation_dy\": -81.02778113177463, \"scale\": 1.207158201764622}\nD: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_49_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_49_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nB: {\"rotation_angle\": 49.90656423603761, \"translation_dx\": 85.27067294320437, \"translation_dy\": -8.928665399863448, \"scale\": 0.9370060594249733}\nC: {\"rotation_angle\": -41.748048059314925, \"translation_dx\": 84.2495675740148, \"translation_dy\": -81.02778113177463, \"scale\": 1.207158201764622}\nD: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -94.06455293225282, \"translation_dx\": -52.04430006776356, \"translation_dy\": 88.55937507710391, \"scale\": 0.8369046461483086}\nB: {\"rotation_angle\": -44.902472769484746, \"translation_dx\": -36.85475324083902, \"translation_dy\": 36.81692000181951, \"scale\": 1.0769710077370194}\nC: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nD: {\"rotation_angle\": 67.74863170033868, \"translation_dx\": 0.9436916559104702, \"translation_dy\": 79.02717939495389, \"scale\": 1.0490112177140545}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_50_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_50_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -94.06455293225282, \"translation_dx\": -52.04430006776356, \"translation_dy\": 88.55937507710391, \"scale\": 0.8369046461483086}\nB: {\"rotation_angle\": -44.902472769484746, \"translation_dx\": -36.85475324083902, \"translation_dy\": 36.81692000181951, \"scale\": 1.0769710077370194}\nC: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nD: {\"rotation_angle\": 67.74863170033868, \"translation_dx\": 0.9436916559104702, \"translation_dy\": 79.02717939495389, \"scale\": 1.0490112177140545}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nC: {\"rotation_angle\": -70.18179574394556, \"translation_dx\": -84.02989442213027, \"translation_dy\": 45.46342410564398, \"scale\": 1.28660403831869}\nD: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_51_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_51_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nC: {\"rotation_angle\": -70.18179574394556, \"translation_dx\": -84.02989442213027, \"translation_dy\": 45.46342410564398, \"scale\": 1.28660403831869}\nD: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}\nB: {\"rotation_angle\": 168.86687879669455, \"translation_dx\": 30.327287286076626, \"translation_dy\": -73.84263373893171, \"scale\": 1.0887904122788439}\nC: {\"rotation_angle\": 141.74747753602782, \"translation_dx\": -54.793360600935046, \"translation_dy\": -29.72546528603263, \"scale\": 0.6563706152769926}\nD: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_52_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_52_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}\nB: {\"rotation_angle\": 168.86687879669455, \"translation_dx\": 30.327287286076626, \"translation_dy\": -73.84263373893171, \"scale\": 1.0887904122788439}\nC: {\"rotation_angle\": 141.74747753602782, \"translation_dx\": -54.793360600935046, \"translation_dy\": -29.72546528603263, \"scale\": 0.6563706152769926}\nD: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nB: {\"rotation_angle\": 52.27392299801002, \"translation_dx\": -7.943242591889941, \"translation_dy\": -1.8318597711701017, \"scale\": 1.489664776133741}\nC: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nD: {\"rotation_angle\": -68.79930104020924, \"translation_dx\": -103.12901971602221, \"translation_dy\": 94.89161684072867, \"scale\": 1.2295411735859756}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_53_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_53_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nB: {\"rotation_angle\": 52.27392299801002, \"translation_dx\": -7.943242591889941, \"translation_dy\": -1.8318597711701017, \"scale\": 1.489664776133741}\nC: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nD: {\"rotation_angle\": -68.79930104020924, \"translation_dx\": -103.12901971602221, \"translation_dy\": 94.89161684072867, \"scale\": 1.2295411735859756}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 159.18509857624855, \"translation_dx\": 94.5972413522399, \"translation_dy\": -87.01463724053234, \"scale\": 0.7914176569510836}\nB: {\"rotation_angle\": 67.74863170033868, \"translation_dx\": 0.9436916559104702, \"translation_dy\": 79.02717939495389, \"scale\": 1.0490112177140545}\nC: {\"rotation_angle\": 161.7596265938729, \"translation_dx\": -9.170216354863072, \"translation_dy\": -19.23222492696047, \"scale\": 1.1821087248622173}\nD: {\"rotation_angle\": -51.98717119490195, \"translation_dx\": -83.93544420557635, \"translation_dy\": -17.359661719977098, \"scale\": 1.0858344969275349}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_54_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_54_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 159.18509857624855, \"translation_dx\": 94.5972413522399, \"translation_dy\": -87.01463724053234, \"scale\": 0.7914176569510836}\nB: {\"rotation_angle\": 67.74863170033868, \"translation_dx\": 0.9436916559104702, \"translation_dy\": 79.02717939495389, \"scale\": 1.0490112177140545}\nC: {\"rotation_angle\": 161.7596265938729, \"translation_dx\": -9.170216354863072, \"translation_dy\": -19.23222492696047, \"scale\": 1.1821087248622173}\nD: {\"rotation_angle\": -51.98717119490195, \"translation_dx\": -83.93544420557635, \"translation_dy\": -17.359661719977098, \"scale\": 1.0858344969275349}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}\nB: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nC: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}\nD: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_55_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_55_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}\nB: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nC: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}\nD: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}\nB: {\"rotation_angle\": -110.51021822636605, \"translation_dx\": -17.924195571284486, \"translation_dy\": -0.10679752473519954, \"scale\": 1.4066663412939815}\nC: {\"rotation_angle\": 12.872370969250312, \"translation_dx\": -43.1533458138392, \"translation_dy\": -64.88511529320917, \"scale\": 1.3092068537816153}\nD: {\"rotation_angle\": -22.98450105670534, \"translation_dx\": -24.343109907781525, \"translation_dy\": -75.50859401578859, \"scale\": 0.5077440368943875}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_56_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_56_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}\nB: {\"rotation_angle\": -110.51021822636605, \"translation_dx\": -17.924195571284486, \"translation_dy\": -0.10679752473519954, \"scale\": 1.4066663412939815}\nC: {\"rotation_angle\": 12.872370969250312, \"translation_dx\": -43.1533458138392, \"translation_dy\": -64.88511529320917, \"scale\": 1.3092068537816153}\nD: {\"rotation_angle\": -22.98450105670534, \"translation_dx\": -24.343109907781525, \"translation_dy\": -75.50859401578859, \"scale\": 0.5077440368943875}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -6.258618837806779, \"translation_dx\": -117.56200624611057, \"translation_dy\": -84.92852320396813, \"scale\": 0.8703619649920769}\nB: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nC: {\"rotation_angle\": -6.38420562293993, \"translation_dx\": -106.80670691302902, \"translation_dy\": -3.5935098985529663, \"scale\": 1.3037846299861797}\nD: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_57_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_57_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -6.258618837806779, \"translation_dx\": -117.56200624611057, \"translation_dy\": -84.92852320396813, \"scale\": 0.8703619649920769}\nB: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nC: {\"rotation_angle\": -6.38420562293993, \"translation_dx\": -106.80670691302902, \"translation_dy\": -3.5935098985529663, \"scale\": 1.3037846299861797}\nD: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 28.186459007199005, \"translation_dx\": -85.64869298892413, \"translation_dy\": -90.9589081114641, \"scale\": 0.5939510579225048}\nB: {\"rotation_angle\": -126.15991399279281, \"translation_dx\": 24.895638463286446, \"translation_dy\": -35.71086816730676, \"scale\": 1.30648936857296}\nC: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nD: {\"rotation_angle\": 99.4759866737457, \"translation_dx\": -117.67383777244245, \"translation_dy\": -44.645046657688624, \"scale\": 1.4332006009229632}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_58_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_58_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 28.186459007199005, \"translation_dx\": -85.64869298892413, \"translation_dy\": -90.9589081114641, \"scale\": 0.5939510579225048}\nB: {\"rotation_angle\": -126.15991399279281, \"translation_dx\": 24.895638463286446, \"translation_dy\": -35.71086816730676, \"scale\": 1.30648936857296}\nC: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nD: {\"rotation_angle\": 99.4759866737457, \"translation_dx\": -117.67383777244245, \"translation_dy\": -44.645046657688624, \"scale\": 1.4332006009229632}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}\nB: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}\nC: {\"rotation_angle\": -37.8135886633452, \"translation_dx\": 94.09848811207868, \"translation_dy\": -28.846940165704815, \"scale\": 0.7423292461324351}\nD: {\"rotation_angle\": -120.90208363304777, \"translation_dx\": -24.471100960859047, \"translation_dy\": -96.60346561133943, \"scale\": 1.2238954631080248}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_59_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_59_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}\nB: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}\nC: {\"rotation_angle\": -37.8135886633452, \"translation_dx\": 94.09848811207868, \"translation_dy\": -28.846940165704815, \"scale\": 0.7423292461324351}\nD: {\"rotation_angle\": -120.90208363304777, \"translation_dx\": -24.471100960859047, \"translation_dy\": -96.60346561133943, \"scale\": 1.2238954631080248}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}\nB: {\"rotation_angle\": 74.4727172984789, \"translation_dx\": 83.0498783040965, \"translation_dy\": 24.573318419119772, \"scale\": 1.4775593630739356}\nC: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nD: {\"rotation_angle\": 134.66606893121838, \"translation_dx\": 30.71289427748178, \"translation_dy\": 31.00111281943242, \"scale\": 0.9716368665085688}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_60_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_60_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}\nB: {\"rotation_angle\": 74.4727172984789, \"translation_dx\": 83.0498783040965, \"translation_dy\": 24.573318419119772, \"scale\": 1.4775593630739356}\nC: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nD: {\"rotation_angle\": 134.66606893121838, \"translation_dx\": 30.71289427748178, \"translation_dy\": 31.00111281943242, \"scale\": 0.9716368665085688}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nB: {\"rotation_angle\": 52.27392299801002, \"translation_dx\": -7.943242591889941, \"translation_dy\": -1.8318597711701017, \"scale\": 1.489664776133741}\nC: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nD: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_61_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_61_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nB: {\"rotation_angle\": 52.27392299801002, \"translation_dx\": -7.943242591889941, \"translation_dy\": -1.8318597711701017, \"scale\": 1.489664776133741}\nC: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nD: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nB: {\"rotation_angle\": -26.00307697103628, \"translation_dx\": -100.91027332279833, \"translation_dy\": 27.120302875093685, \"scale\": 0.9546103505495939}\nC: {\"rotation_angle\": 99.4759866737457, \"translation_dx\": -117.67383777244245, \"translation_dy\": -44.645046657688624, \"scale\": 1.4332006009229632}\nD: {\"rotation_angle\": -165.5576257925042, \"translation_dx\": 120.02978270991923, \"translation_dy\": -94.68626204020723, \"scale\": 1.377433782383828}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_62_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_62_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nB: {\"rotation_angle\": -26.00307697103628, \"translation_dx\": -100.91027332279833, \"translation_dy\": 27.120302875093685, \"scale\": 0.9546103505495939}\nC: {\"rotation_angle\": 99.4759866737457, \"translation_dx\": -117.67383777244245, \"translation_dy\": -44.645046657688624, \"scale\": 1.4332006009229632}\nD: {\"rotation_angle\": -165.5576257925042, \"translation_dx\": 120.02978270991923, \"translation_dy\": -94.68626204020723, \"scale\": 1.377433782383828}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -37.8135886633452, \"translation_dx\": 94.09848811207868, \"translation_dy\": -28.846940165704815, \"scale\": 0.7423292461324351}\nB: {\"rotation_angle\": 98.12478073081388, \"translation_dx\": 82.24255679101596, \"translation_dy\": 10.638794739410258, \"scale\": 1.454613875934863}\nC: {\"rotation_angle\": -98.17490649350026, \"translation_dx\": 5.744855173473269, \"translation_dy\": -10.705504600001973, \"scale\": 1.1182428392253487}\nD: {\"rotation_angle\": 4.601729825002167, \"translation_dx\": -92.34842360064926, \"translation_dy\": 78.34726427877602, \"scale\": 0.7620115680057987}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_63_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_63_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -37.8135886633452, \"translation_dx\": 94.09848811207868, \"translation_dy\": -28.846940165704815, \"scale\": 0.7423292461324351}\nB: {\"rotation_angle\": 98.12478073081388, \"translation_dx\": 82.24255679101596, \"translation_dy\": 10.638794739410258, \"scale\": 1.454613875934863}\nC: {\"rotation_angle\": -98.17490649350026, \"translation_dx\": 5.744855173473269, \"translation_dy\": -10.705504600001973, \"scale\": 1.1182428392253487}\nD: {\"rotation_angle\": 4.601729825002167, \"translation_dx\": -92.34842360064926, \"translation_dy\": 78.34726427877602, \"scale\": 0.7620115680057987}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -59.18065174130953, \"translation_dx\": -66.15733764198566, \"translation_dy\": -32.06450758946801, \"scale\": 1.1967157159259998}\nB: {\"rotation_angle\": 48.71833122181758, \"translation_dx\": -105.22683210092106, \"translation_dy\": -63.34096559919908, \"scale\": 0.7204478932238769}\nC: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nD: {\"rotation_angle\": -149.42147215379055, \"translation_dx\": 2.3444194857030283, \"translation_dy\": 35.92779325530762, \"scale\": 1.0223945055206394}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_64_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_64_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -59.18065174130953, \"translation_dx\": -66.15733764198566, \"translation_dy\": -32.06450758946801, \"scale\": 1.1967157159259998}\nB: {\"rotation_angle\": 48.71833122181758, \"translation_dx\": -105.22683210092106, \"translation_dy\": -63.34096559919908, \"scale\": 0.7204478932238769}\nC: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nD: {\"rotation_angle\": -149.42147215379055, \"translation_dx\": 2.3444194857030283, \"translation_dy\": 35.92779325530762, \"scale\": 1.0223945055206394}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}\nB: {\"rotation_angle\": -103.24791656906933, \"translation_dx\": -2.2454836983213227, \"translation_dy\": 24.014319900588845, \"scale\": 1.3204557483507742}\nC: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}\nD: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_65_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_65_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}\nB: {\"rotation_angle\": -103.24791656906933, \"translation_dx\": -2.2454836983213227, \"translation_dy\": 24.014319900588845, \"scale\": 1.3204557483507742}\nC: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}\nD: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nB: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nC: {\"rotation_angle\": 99.4759866737457, \"translation_dx\": -117.67383777244245, \"translation_dy\": -44.645046657688624, \"scale\": 1.4332006009229632}\nD: {\"rotation_angle\": 52.27392299801002, \"translation_dx\": -7.943242591889941, \"translation_dy\": -1.8318597711701017, \"scale\": 1.489664776133741}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_66_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_66_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nB: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nC: {\"rotation_angle\": 99.4759866737457, \"translation_dx\": -117.67383777244245, \"translation_dy\": -44.645046657688624, \"scale\": 1.4332006009229632}\nD: {\"rotation_angle\": 52.27392299801002, \"translation_dx\": -7.943242591889941, \"translation_dy\": -1.8318597711701017, \"scale\": 1.489664776133741}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 23.955007488404988, \"translation_dx\": 90.0018582930472, \"translation_dy\": 38.03553582875617, \"scale\": 1.3380437802347522}\nB: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nC: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}\nD: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_67_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_67_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 23.955007488404988, \"translation_dx\": 90.0018582930472, \"translation_dy\": 38.03553582875617, \"scale\": 1.3380437802347522}\nB: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nC: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}\nD: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nB: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nC: {\"rotation_angle\": 49.896013394485834, \"translation_dx\": -25.763756683237403, \"translation_dy\": -26.432232271484168, \"scale\": 1.1619310734744932}\nD: {\"rotation_angle\": -110.46391589612124, \"translation_dx\": -77.96644542647721, \"translation_dy\": -50.23500265461973, \"scale\": 0.7651088884143488}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_68_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_68_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nB: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nC: {\"rotation_angle\": 49.896013394485834, \"translation_dx\": -25.763756683237403, \"translation_dy\": -26.432232271484168, \"scale\": 1.1619310734744932}\nD: {\"rotation_angle\": -110.46391589612124, \"translation_dx\": -77.96644542647721, \"translation_dy\": -50.23500265461973, \"scale\": 0.7651088884143488}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": -153.95647753312159, \"translation_dx\": 64.08546266437509, \"translation_dy\": -34.554486291313935, \"scale\": 1.423360690418288}\nC: {\"rotation_angle\": 141.74747753602782, \"translation_dx\": -54.793360600935046, \"translation_dy\": -29.72546528603263, \"scale\": 0.6563706152769926}\nD: {\"rotation_angle\": 138.15953129001275, \"translation_dx\": 108.29077351507729, \"translation_dy\": 11.25207260435026, \"scale\": 1.2682750116992958}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_69_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_69_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": -153.95647753312159, \"translation_dx\": 64.08546266437509, \"translation_dy\": -34.554486291313935, \"scale\": 1.423360690418288}\nC: {\"rotation_angle\": 141.74747753602782, \"translation_dx\": -54.793360600935046, \"translation_dy\": -29.72546528603263, \"scale\": 0.6563706152769926}\nD: {\"rotation_angle\": 138.15953129001275, \"translation_dx\": 108.29077351507729, \"translation_dy\": 11.25207260435026, \"scale\": 1.2682750116992958}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}\nB: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}\nC: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nD: {\"rotation_angle\": 162.98131081099467, \"translation_dx\": -80.19473687776261, \"translation_dy\": -17.70282064458462, \"scale\": 1.2855975600149028}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_70_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_70_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}\nB: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}\nC: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nD: {\"rotation_angle\": 162.98131081099467, \"translation_dx\": -80.19473687776261, \"translation_dy\": -17.70282064458462, \"scale\": 1.2855975600149028}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nB: {\"rotation_angle\": 115.44035395260755, \"translation_dx\": 104.38539690843712, \"translation_dy\": -82.71757148170198, \"scale\": 0.6534862534786243}\nC: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}\nD: {\"rotation_angle\": 130.382151153576, \"translation_dx\": 48.77925626504499, \"translation_dy\": 54.89982459749416, \"scale\": 1.3647831130001666}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_71_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_71_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nB: {\"rotation_angle\": 115.44035395260755, \"translation_dx\": 104.38539690843712, \"translation_dy\": -82.71757148170198, \"scale\": 0.6534862534786243}\nC: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}\nD: {\"rotation_angle\": 130.382151153576, \"translation_dx\": 48.77925626504499, \"translation_dy\": 54.89982459749416, \"scale\": 1.3647831130001666}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}\nB: {\"rotation_angle\": -147.17742740700606, \"translation_dx\": 99.79022385553455, \"translation_dy\": -46.32888217161055, \"scale\": 1.2561938294527635}\nC: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nD: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_72_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_72_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}\nB: {\"rotation_angle\": -147.17742740700606, \"translation_dx\": 99.79022385553455, \"translation_dy\": -46.32888217161055, \"scale\": 1.2561938294527635}\nC: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nD: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}\nB: {\"rotation_angle\": 138.15953129001275, \"translation_dx\": 108.29077351507729, \"translation_dy\": 11.25207260435026, \"scale\": 1.2682750116992958}\nC: {\"rotation_angle\": -8.756342422911757, \"translation_dx\": -120.12147874311805, \"translation_dy\": -16.659510954699698, \"scale\": 0.8471832394055047}\nD: {\"rotation_angle\": -70.18179574394556, \"translation_dx\": -84.02989442213027, \"translation_dy\": 45.46342410564398, \"scale\": 1.28660403831869}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_73_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_73_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}\nB: {\"rotation_angle\": 138.15953129001275, \"translation_dx\": 108.29077351507729, \"translation_dy\": 11.25207260435026, \"scale\": 1.2682750116992958}\nC: {\"rotation_angle\": -8.756342422911757, \"translation_dx\": -120.12147874311805, \"translation_dy\": -16.659510954699698, \"scale\": 0.8471832394055047}\nD: {\"rotation_angle\": -70.18179574394556, \"translation_dx\": -84.02989442213027, \"translation_dy\": 45.46342410564398, \"scale\": 1.28660403831869}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}\nB: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}\nC: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nD: {\"rotation_angle\": -120.90208363304777, \"translation_dx\": -24.471100960859047, \"translation_dy\": -96.60346561133943, \"scale\": 1.2238954631080248}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_74_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_74_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}\nB: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}\nC: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nD: {\"rotation_angle\": -120.90208363304777, \"translation_dx\": -24.471100960859047, \"translation_dy\": -96.60346561133943, \"scale\": 1.2238954631080248}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 88.55038325147228, \"translation_dx\": -17.272344447388633, \"translation_dy\": -67.72549137992362, \"scale\": 0.5810098703790367}\nB: {\"rotation_angle\": 127.1396993936072, \"translation_dx\": -29.08894824101361, \"translation_dy\": -80.84475014775404, \"scale\": 1.2834497894588772}\nC: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nD: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_75_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_75_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 88.55038325147228, \"translation_dx\": -17.272344447388633, \"translation_dy\": -67.72549137992362, \"scale\": 0.5810098703790367}\nB: {\"rotation_angle\": 127.1396993936072, \"translation_dx\": -29.08894824101361, \"translation_dy\": -80.84475014775404, \"scale\": 1.2834497894588772}\nC: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nD: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -94.06455293225282, \"translation_dx\": -52.04430006776356, \"translation_dy\": 88.55937507710391, \"scale\": 0.8369046461483086}\nB: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}\nC: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}\nD: {\"rotation_angle\": -126.23248080179604, \"translation_dx\": -18.04313623288388, \"translation_dy\": 59.052880720386156, \"scale\": 1.3827835175940266}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_76_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_76_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -94.06455293225282, \"translation_dx\": -52.04430006776356, \"translation_dy\": 88.55937507710391, \"scale\": 0.8369046461483086}\nB: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}\nC: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}\nD: {\"rotation_angle\": -126.23248080179604, \"translation_dx\": -18.04313623288388, \"translation_dy\": 59.052880720386156, \"scale\": 1.3827835175940266}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -72.82027143369304, \"translation_dx\": -44.85481158127062, \"translation_dy\": 106.69131407191517, \"scale\": 0.716080341101258}\nB: {\"rotation_angle\": 74.4727172984789, \"translation_dx\": 83.0498783040965, \"translation_dy\": 24.573318419119772, \"scale\": 1.4775593630739356}\nC: {\"rotation_angle\": 33.426384392539006, \"translation_dx\": -12.448609293998487, \"translation_dy\": 64.03367069956386, \"scale\": 0.6340926377236346}\nD: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_77_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_77_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -72.82027143369304, \"translation_dx\": -44.85481158127062, \"translation_dy\": 106.69131407191517, \"scale\": 0.716080341101258}\nB: {\"rotation_angle\": 74.4727172984789, \"translation_dx\": 83.0498783040965, \"translation_dy\": 24.573318419119772, \"scale\": 1.4775593630739356}\nC: {\"rotation_angle\": 33.426384392539006, \"translation_dx\": -12.448609293998487, \"translation_dy\": 64.03367069956386, \"scale\": 0.6340926377236346}\nD: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 28.186459007199005, \"translation_dx\": -85.64869298892413, \"translation_dy\": -90.9589081114641, \"scale\": 0.5939510579225048}\nB: {\"rotation_angle\": -161.22593365548192, \"translation_dx\": -119.73961882572601, \"translation_dy\": -93.50838821854722, \"scale\": 1.4476413063179399}\nC: {\"rotation_angle\": -46.75272698463425, \"translation_dx\": 16.424107524155175, \"translation_dy\": -60.683488552754085, \"scale\": 1.375025476214386}\nD: {\"rotation_angle\": 141.74747753602782, \"translation_dx\": -54.793360600935046, \"translation_dy\": -29.72546528603263, \"scale\": 0.6563706152769926}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_78_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_78_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 28.186459007199005, \"translation_dx\": -85.64869298892413, \"translation_dy\": -90.9589081114641, \"scale\": 0.5939510579225048}\nB: {\"rotation_angle\": -161.22593365548192, \"translation_dx\": -119.73961882572601, \"translation_dy\": -93.50838821854722, \"scale\": 1.4476413063179399}\nC: {\"rotation_angle\": -46.75272698463425, \"translation_dx\": 16.424107524155175, \"translation_dy\": -60.683488552754085, \"scale\": 1.375025476214386}\nD: {\"rotation_angle\": 141.74747753602782, \"translation_dx\": -54.793360600935046, \"translation_dy\": -29.72546528603263, \"scale\": 0.6563706152769926}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -174.94064668132228, \"translation_dx\": 73.73079207136513, \"translation_dy\": 58.25534486945551, \"scale\": 1.178357936048121}\nB: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}\nC: {\"rotation_angle\": 33.426384392539006, \"translation_dx\": -12.448609293998487, \"translation_dy\": 64.03367069956386, \"scale\": 0.6340926377236346}\nD: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_79_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_79_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -174.94064668132228, \"translation_dx\": 73.73079207136513, \"translation_dy\": 58.25534486945551, \"scale\": 1.178357936048121}\nB: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}\nC: {\"rotation_angle\": 33.426384392539006, \"translation_dx\": -12.448609293998487, \"translation_dy\": 64.03367069956386, \"scale\": 0.6340926377236346}\nD: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nB: {\"rotation_angle\": 95.56102360167273, \"translation_dx\": -57.629857243876444, \"translation_dy\": -95.34824117323305, \"scale\": 0.9533126568708786}\nC: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nD: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_80_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_80_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}\nB: {\"rotation_angle\": 95.56102360167273, \"translation_dx\": -57.629857243876444, \"translation_dy\": -95.34824117323305, \"scale\": 0.9533126568708786}\nC: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nD: {\"rotation_angle\": 112.15713698429767, \"translation_dx\": -0.833180316164956, \"translation_dy\": -100.57740000976534, \"scale\": 1.21487245494624}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nC: {\"rotation_angle\": -126.23248080179604, \"translation_dx\": -18.04313623288388, \"translation_dy\": 59.052880720386156, \"scale\": 1.3827835175940266}\nD: {\"rotation_angle\": -120.90208363304777, \"translation_dx\": -24.471100960859047, \"translation_dy\": -96.60346561133943, \"scale\": 1.2238954631080248}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_81_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_81_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nC: {\"rotation_angle\": -126.23248080179604, \"translation_dx\": -18.04313623288388, \"translation_dy\": 59.052880720386156, \"scale\": 1.3827835175940266}\nD: {\"rotation_angle\": -120.90208363304777, \"translation_dx\": -24.471100960859047, \"translation_dy\": -96.60346561133943, \"scale\": 1.2238954631080248}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}\nB: {\"rotation_angle\": -59.18065174130953, \"translation_dx\": -66.15733764198566, \"translation_dy\": -32.06450758946801, \"scale\": 1.1967157159259998}\nC: {\"rotation_angle\": 134.66606893121838, \"translation_dx\": 30.71289427748178, \"translation_dy\": 31.00111281943242, \"scale\": 0.9716368665085688}\nD: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_82_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_82_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}\nB: {\"rotation_angle\": -59.18065174130953, \"translation_dx\": -66.15733764198566, \"translation_dy\": -32.06450758946801, \"scale\": 1.1967157159259998}\nC: {\"rotation_angle\": 134.66606893121838, \"translation_dx\": 30.71289427748178, \"translation_dy\": 31.00111281943242, \"scale\": 0.9716368665085688}\nD: {\"rotation_angle\": 32.170058088704565, \"translation_dx\": 62.48780444449932, \"translation_dy\": 36.464458087386475, \"scale\": 0.8338243238440678}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}\nB: {\"rotation_angle\": 178.3015459217881, \"translation_dx\": 2.1592483018484785, \"translation_dy\": -86.15095567396924, \"scale\": 1.206185814877298}\nC: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nD: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_83_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_83_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}\nB: {\"rotation_angle\": 178.3015459217881, \"translation_dx\": 2.1592483018484785, \"translation_dy\": -86.15095567396924, \"scale\": 1.206185814877298}\nC: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nD: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nB: {\"rotation_angle\": 98.12478073081388, \"translation_dx\": 82.24255679101596, \"translation_dy\": 10.638794739410258, \"scale\": 1.454613875934863}\nC: {\"rotation_angle\": -4.364889011784271, \"translation_dx\": 74.89385338851659, \"translation_dy\": 29.259521498010997, \"scale\": 1.2877948451877137}\nD: {\"rotation_angle\": 106.62912259997893, \"translation_dx\": -62.19399566166837, \"translation_dy\": -63.078041204745844, \"scale\": 1.4577244189370733}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_84_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_84_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nB: {\"rotation_angle\": 98.12478073081388, \"translation_dx\": 82.24255679101596, \"translation_dy\": 10.638794739410258, \"scale\": 1.454613875934863}\nC: {\"rotation_angle\": -4.364889011784271, \"translation_dx\": 74.89385338851659, \"translation_dy\": 29.259521498010997, \"scale\": 1.2877948451877137}\nD: {\"rotation_angle\": 106.62912259997893, \"translation_dx\": -62.19399566166837, \"translation_dy\": -63.078041204745844, \"scale\": 1.4577244189370733}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 12.872370969250312, \"translation_dx\": -43.1533458138392, \"translation_dy\": -64.88511529320917, \"scale\": 1.3092068537816153}\nB: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}\nC: {\"rotation_angle\": 48.71833122181758, \"translation_dx\": -105.22683210092106, \"translation_dy\": -63.34096559919908, \"scale\": 0.7204478932238769}\nD: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_85_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_85_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 12.872370969250312, \"translation_dx\": -43.1533458138392, \"translation_dy\": -64.88511529320917, \"scale\": 1.3092068537816153}\nB: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}\nC: {\"rotation_angle\": 48.71833122181758, \"translation_dx\": -105.22683210092106, \"translation_dy\": -63.34096559919908, \"scale\": 0.7204478932238769}\nD: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nB: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}\nC: {\"rotation_angle\": -0.45613579718829556, \"translation_dx\": 98.71619714866841, \"translation_dy\": 70.1100439641223, \"scale\": 0.6491919010173006}\nD: {\"rotation_angle\": -161.22593365548192, \"translation_dx\": -119.73961882572601, \"translation_dy\": -93.50838821854722, \"scale\": 1.4476413063179399}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_86_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_86_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nB: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}\nC: {\"rotation_angle\": -0.45613579718829556, \"translation_dx\": 98.71619714866841, \"translation_dy\": 70.1100439641223, \"scale\": 0.6491919010173006}\nD: {\"rotation_angle\": -161.22593365548192, \"translation_dx\": -119.73961882572601, \"translation_dy\": -93.50838821854722, \"scale\": 1.4476413063179399}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -32.96407209098831, \"translation_dx\": -27.518946535455143, \"translation_dy\": 2.5370159689679213, \"scale\": 1.259328459428434}\nB: {\"rotation_angle\": -50.19218790392131, \"translation_dx\": -27.31734251737683, \"translation_dy\": 8.514724344494553, \"scale\": 1.0874517053433594}\nC: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}\nD: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_87_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_87_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -32.96407209098831, \"translation_dx\": -27.518946535455143, \"translation_dy\": 2.5370159689679213, \"scale\": 1.259328459428434}\nB: {\"rotation_angle\": -50.19218790392131, \"translation_dx\": -27.31734251737683, \"translation_dy\": 8.514724344494553, \"scale\": 1.0874517053433594}\nC: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}\nD: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}\nB: {\"rotation_angle\": -137.69110011960493, \"translation_dx\": -11.76155657697187, \"translation_dy\": 15.916526895382503, \"scale\": 1.164396221339579}\nC: {\"rotation_angle\": -51.98717119490195, \"translation_dx\": -83.93544420557635, \"translation_dy\": -17.359661719977098, \"scale\": 1.0858344969275349}\nD: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_88_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_88_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}\nB: {\"rotation_angle\": -137.69110011960493, \"translation_dx\": -11.76155657697187, \"translation_dy\": 15.916526895382503, \"scale\": 1.164396221339579}\nC: {\"rotation_angle\": -51.98717119490195, \"translation_dx\": -83.93544420557635, \"translation_dy\": -17.359661719977098, \"scale\": 1.0858344969275349}\nD: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}\nB: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nC: {\"rotation_angle\": -101.64893396855386, \"translation_dx\": -96.08306753711838, \"translation_dy\": 14.852477797043775, \"scale\": 1.3017377870800058}\nD: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_89_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_89_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}\nB: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nC: {\"rotation_angle\": -101.64893396855386, \"translation_dx\": -96.08306753711838, \"translation_dy\": 14.852477797043775, \"scale\": 1.3017377870800058}\nD: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -163.07830945514343, \"translation_dx\": 107.25371607945826, \"translation_dy\": 44.19319462200147, \"scale\": 1.0330497674624493}\nB: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}\nC: {\"rotation_angle\": 179.8013352752547, \"translation_dx\": -90.5548533247824, \"translation_dy\": 17.23782922418306, \"scale\": 0.9885365626195518}\nD: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_90_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_90_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -163.07830945514343, \"translation_dx\": 107.25371607945826, \"translation_dy\": 44.19319462200147, \"scale\": 1.0330497674624493}\nB: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}\nC: {\"rotation_angle\": 179.8013352752547, \"translation_dx\": -90.5548533247824, \"translation_dy\": 17.23782922418306, \"scale\": 0.9885365626195518}\nD: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}\nB: {\"rotation_angle\": 171.23105805984426, \"translation_dx\": 28.800906238980815, \"translation_dy\": 60.921924115709544, \"scale\": 1.4441070487112413}\nC: {\"rotation_angle\": 159.18509857624855, \"translation_dx\": 94.5972413522399, \"translation_dy\": -87.01463724053234, \"scale\": 0.7914176569510836}\nD: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_91_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_91_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}\nB: {\"rotation_angle\": 171.23105805984426, \"translation_dx\": 28.800906238980815, \"translation_dy\": 60.921924115709544, \"scale\": 1.4441070487112413}\nC: {\"rotation_angle\": 159.18509857624855, \"translation_dx\": 94.5972413522399, \"translation_dy\": -87.01463724053234, \"scale\": 0.7914176569510836}\nD: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -137.69110011960493, \"translation_dx\": -11.76155657697187, \"translation_dy\": 15.916526895382503, \"scale\": 1.164396221339579}\nB: {\"rotation_angle\": 22.924180775031914, \"translation_dx\": 8.278066534063711, \"translation_dy\": 39.03722404706397, \"scale\": 0.6972670428813228}\nC: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}\nD: {\"rotation_angle\": 32.25033099080062, \"translation_dx\": -33.246475706714875, \"translation_dy\": -9.848772328845214, \"scale\": 0.986502265576198}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_92_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_92_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -137.69110011960493, \"translation_dx\": -11.76155657697187, \"translation_dy\": 15.916526895382503, \"scale\": 1.164396221339579}\nB: {\"rotation_angle\": 22.924180775031914, \"translation_dx\": 8.278066534063711, \"translation_dy\": 39.03722404706397, \"scale\": 0.6972670428813228}\nC: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}\nD: {\"rotation_angle\": 32.25033099080062, \"translation_dx\": -33.246475706714875, \"translation_dy\": -9.848772328845214, \"scale\": 0.986502265576198}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}\nB: {\"rotation_angle\": 138.15953129001275, \"translation_dx\": 108.29077351507729, \"translation_dy\": 11.25207260435026, \"scale\": 1.2682750116992958}\nC: {\"rotation_angle\": 22.924180775031914, \"translation_dx\": 8.278066534063711, \"translation_dy\": 39.03722404706397, \"scale\": 0.6972670428813228}\nD: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_93_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_93_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}\nB: {\"rotation_angle\": 138.15953129001275, \"translation_dx\": 108.29077351507729, \"translation_dy\": 11.25207260435026, \"scale\": 1.2682750116992958}\nC: {\"rotation_angle\": 22.924180775031914, \"translation_dx\": 8.278066534063711, \"translation_dy\": 39.03722404706397, \"scale\": 0.6972670428813228}\nD: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 161.7596265938729, \"translation_dx\": -9.170216354863072, \"translation_dy\": -19.23222492696047, \"scale\": 1.1821087248622173}\nB: {\"rotation_angle\": -103.24791656906933, \"translation_dx\": -2.2454836983213227, \"translation_dy\": 24.014319900588845, \"scale\": 1.3204557483507742}\nC: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}\nD: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_94_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_94_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 161.7596265938729, \"translation_dx\": -9.170216354863072, \"translation_dy\": -19.23222492696047, \"scale\": 1.1821087248622173}\nB: {\"rotation_angle\": -103.24791656906933, \"translation_dx\": -2.2454836983213227, \"translation_dy\": 24.014319900588845, \"scale\": 1.3204557483507742}\nC: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}\nD: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}\nB: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}\nC: {\"rotation_angle\": 36.19361803007027, \"translation_dx\": -50.40071399889004, \"translation_dy\": -85.39533040467117, \"scale\": 0.6522247071940848}\nD: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_95_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_95_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}\nB: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}\nC: {\"rotation_angle\": 36.19361803007027, \"translation_dx\": -50.40071399889004, \"translation_dy\": -85.39533040467117, \"scale\": 0.6522247071940848}\nD: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}\nB: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nC: {\"rotation_angle\": -173.49565975712173, \"translation_dx\": 30.5303454517925, \"translation_dy\": 77.86216107455405, \"scale\": 1.067173806992701}\nD: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_96_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_96_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}\nB: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nC: {\"rotation_angle\": -173.49565975712173, \"translation_dx\": 30.5303454517925, \"translation_dy\": 77.86216107455405, \"scale\": 1.067173806992701}\nD: {\"rotation_angle\": 78.52234880801677, \"translation_dx\": -41.05806913924104, \"translation_dy\": -5.158893155372851, \"scale\": 1.0182841116233097}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -50.19218790392131, \"translation_dx\": -27.31734251737683, \"translation_dy\": 8.514724344494553, \"scale\": 1.0874517053433594}\nB: {\"rotation_angle\": 33.426384392539006, \"translation_dx\": -12.448609293998487, \"translation_dy\": 64.03367069956386, \"scale\": 0.6340926377236346}\nC: {\"rotation_angle\": 22.924180775031914, \"translation_dx\": 8.278066534063711, \"translation_dy\": 39.03722404706397, \"scale\": 0.6972670428813228}\nD: {\"rotation_angle\": -115.34417090075787, \"translation_dx\": -118.63121430094503, \"translation_dy\": 41.63412082488844, \"scale\": 0.9001856788272352}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_97_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_97_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -50.19218790392131, \"translation_dx\": -27.31734251737683, \"translation_dy\": 8.514724344494553, \"scale\": 1.0874517053433594}\nB: {\"rotation_angle\": 33.426384392539006, \"translation_dx\": -12.448609293998487, \"translation_dy\": 64.03367069956386, \"scale\": 0.6340926377236346}\nC: {\"rotation_angle\": 22.924180775031914, \"translation_dx\": 8.278066534063711, \"translation_dy\": 39.03722404706397, \"scale\": 0.6972670428813228}\nD: {\"rotation_angle\": -115.34417090075787, \"translation_dx\": -118.63121430094503, \"translation_dy\": 41.63412082488844, \"scale\": 0.9001856788272352}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -68.79930104020924, \"translation_dx\": -103.12901971602221, \"translation_dy\": 94.89161684072867, \"scale\": 1.2295411735859756}\nB: {\"rotation_angle\": -164.42105085554024, \"translation_dx\": 53.959081038248144, \"translation_dy\": -27.892450679654182, \"scale\": 1.1369631742880046}\nC: {\"rotation_angle\": 103.56580652114087, \"translation_dx\": -76.88940345297716, \"translation_dy\": -3.4544443607121593, \"scale\": 1.3949152683659345}\nD: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_98_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_98_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -68.79930104020924, \"translation_dx\": -103.12901971602221, \"translation_dy\": 94.89161684072867, \"scale\": 1.2295411735859756}\nB: {\"rotation_angle\": -164.42105085554024, \"translation_dx\": 53.959081038248144, \"translation_dy\": -27.892450679654182, \"scale\": 1.1369631742880046}\nC: {\"rotation_angle\": 103.56580652114087, \"translation_dx\": -76.88940345297716, \"translation_dy\": -3.4544443607121593, \"scale\": 1.3949152683659345}\nD: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}\nB: {\"rotation_angle\": 18.52926347539298, \"translation_dx\": -26.155433185237058, \"translation_dy\": -39.799299198218556, \"scale\": 0.9355127285855813}\nC: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nD: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_99_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_99_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}\nB: {\"rotation_angle\": 18.52926347539298, \"translation_dx\": -26.155433185237058, \"translation_dy\": -39.799299198218556, \"scale\": 0.9355127285855813}\nC: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nD: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nB: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}\nC: {\"rotation_angle\": -124.27587082376021, \"translation_dx\": -88.19288051455345, \"translation_dy\": 24.145134775980125, \"scale\": 1.4414104211047083}\nD: {\"rotation_angle\": 8.705969178532513, \"translation_dx\": -108.98578445869327, \"translation_dy\": -85.91179454441009, \"scale\": 0.5132717751865925}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_100_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_100_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nB: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}\nC: {\"rotation_angle\": -124.27587082376021, \"translation_dx\": -88.19288051455345, \"translation_dy\": 24.145134775980125, \"scale\": 1.4414104211047083}\nD: {\"rotation_angle\": 8.705969178532513, \"translation_dx\": -108.98578445869327, \"translation_dy\": -85.91179454441009, \"scale\": 0.5132717751865925}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}\nB: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}\nC: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nD: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_101_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_101_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}\nB: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}\nC: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nD: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}\nB: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nC: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}\nD: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_102_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_102_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}\nB: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nC: {\"rotation_angle\": 95.69634927891752, \"translation_dx\": -96.46148729426875, \"translation_dy\": -25.496381966922478, \"scale\": 0.7479348241153333}\nD: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nB: {\"rotation_angle\": -153.3687774434925, \"translation_dx\": 50.92336593606055, \"translation_dy\": -56.81603844715568, \"scale\": 1.398231264497651}\nC: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}\nD: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_103_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_103_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nB: {\"rotation_angle\": -153.3687774434925, \"translation_dx\": 50.92336593606055, \"translation_dy\": -56.81603844715568, \"scale\": 1.398231264497651}\nC: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}\nD: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}\nB: {\"rotation_angle\": -176.3085334768787, \"translation_dx\": -26.09189325642553, \"translation_dy\": 21.458056495366975, \"scale\": 0.7934334422653395}\nC: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}\nD: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_104_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_104_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}\nB: {\"rotation_angle\": -176.3085334768787, \"translation_dx\": -26.09189325642553, \"translation_dy\": 21.458056495366975, \"scale\": 0.7934334422653395}\nC: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}\nD: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}\nB: {\"rotation_angle\": -5.816806483512181, \"translation_dx\": -70.40329792935935, \"translation_dy\": -21.418007440252175, \"scale\": 1.0041476956174793}\nC: {\"rotation_angle\": 171.23105805984426, \"translation_dx\": 28.800906238980815, \"translation_dy\": 60.921924115709544, \"scale\": 1.4441070487112413}\nD: {\"rotation_angle\": 1.3693998936690264, \"translation_dx\": -71.94174431428723, \"translation_dy\": 25.661133958182248, \"scale\": 1.468813327861592}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_105_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_105_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}\nB: {\"rotation_angle\": -5.816806483512181, \"translation_dx\": -70.40329792935935, \"translation_dy\": -21.418007440252175, \"scale\": 1.0041476956174793}\nC: {\"rotation_angle\": 171.23105805984426, \"translation_dx\": 28.800906238980815, \"translation_dy\": 60.921924115709544, \"scale\": 1.4441070487112413}\nD: {\"rotation_angle\": 1.3693998936690264, \"translation_dx\": -71.94174431428723, \"translation_dy\": 25.661133958182248, \"scale\": 1.468813327861592}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 162.98131081099467, \"translation_dx\": -80.19473687776261, \"translation_dy\": -17.70282064458462, \"scale\": 1.2855975600149028}\nB: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nC: {\"rotation_angle\": 33.36657735274014, \"translation_dx\": -110.42271839281483, \"translation_dy\": 35.783043595963875, \"scale\": 1.1017945125321793}\nD: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_106_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_106_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 162.98131081099467, \"translation_dx\": -80.19473687776261, \"translation_dy\": -17.70282064458462, \"scale\": 1.2855975600149028}\nB: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nC: {\"rotation_angle\": 33.36657735274014, \"translation_dx\": -110.42271839281483, \"translation_dy\": 35.783043595963875, \"scale\": 1.1017945125321793}\nD: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 179.8013352752547, \"translation_dx\": -90.5548533247824, \"translation_dy\": 17.23782922418306, \"scale\": 0.9885365626195518}\nB: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}\nC: {\"rotation_angle\": -164.42105085554024, \"translation_dx\": 53.959081038248144, \"translation_dy\": -27.892450679654182, \"scale\": 1.1369631742880046}\nD: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_107_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_107_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 179.8013352752547, \"translation_dx\": -90.5548533247824, \"translation_dy\": 17.23782922418306, \"scale\": 0.9885365626195518}\nB: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}\nC: {\"rotation_angle\": -164.42105085554024, \"translation_dx\": 53.959081038248144, \"translation_dy\": -27.892450679654182, \"scale\": 1.1369631742880046}\nD: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -164.42105085554024, \"translation_dx\": 53.959081038248144, \"translation_dy\": -27.892450679654182, \"scale\": 1.1369631742880046}\nB: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}\nC: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nD: {\"rotation_angle\": 136.76946369368522, \"translation_dx\": 86.13615517916296, \"translation_dy\": 47.49597577737802, \"scale\": 1.1842967613683704}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_108_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_108_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -164.42105085554024, \"translation_dx\": 53.959081038248144, \"translation_dy\": -27.892450679654182, \"scale\": 1.1369631742880046}\nB: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}\nC: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nD: {\"rotation_angle\": 136.76946369368522, \"translation_dx\": 86.13615517916296, \"translation_dy\": 47.49597577737802, \"scale\": 1.1842967613683704}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nB: {\"rotation_angle\": -4.364889011784271, \"translation_dx\": 74.89385338851659, \"translation_dy\": 29.259521498010997, \"scale\": 1.2877948451877137}\nC: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nD: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_109_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_109_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nB: {\"rotation_angle\": -4.364889011784271, \"translation_dx\": 74.89385338851659, \"translation_dy\": 29.259521498010997, \"scale\": 1.2877948451877137}\nC: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nD: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 49.896013394485834, \"translation_dx\": -25.763756683237403, \"translation_dy\": -26.432232271484168, \"scale\": 1.1619310734744932}\nB: {\"rotation_angle\": 133.22970053001933, \"translation_dx\": 30.83867253278636, \"translation_dy\": 9.987607615316023, \"scale\": 0.9746642566652708}\nC: {\"rotation_angle\": 36.19361803007027, \"translation_dx\": -50.40071399889004, \"translation_dy\": -85.39533040467117, \"scale\": 0.6522247071940848}\nD: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_110_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_110_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 49.896013394485834, \"translation_dx\": -25.763756683237403, \"translation_dy\": -26.432232271484168, \"scale\": 1.1619310734744932}\nB: {\"rotation_angle\": 133.22970053001933, \"translation_dx\": 30.83867253278636, \"translation_dy\": 9.987607615316023, \"scale\": 0.9746642566652708}\nC: {\"rotation_angle\": 36.19361803007027, \"translation_dx\": -50.40071399889004, \"translation_dy\": -85.39533040467117, \"scale\": 0.6522247071940848}\nD: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nB: {\"rotation_angle\": -72.82027143369304, \"translation_dx\": -44.85481158127062, \"translation_dy\": 106.69131407191517, \"scale\": 0.716080341101258}\nC: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nD: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_111_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_111_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nB: {\"rotation_angle\": -72.82027143369304, \"translation_dx\": -44.85481158127062, \"translation_dy\": 106.69131407191517, \"scale\": 0.716080341101258}\nC: {\"rotation_angle\": -113.69332067912192, \"translation_dx\": -23.005200251858383, \"translation_dy\": 57.916315250854666, \"scale\": 0.5483419258047426}\nD: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nB: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nC: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nD: {\"rotation_angle\": -128.74497971799806, \"translation_dx\": -55.835206426128764, \"translation_dy\": 54.178252983369276, \"scale\": 0.8905979693160588}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_112_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_112_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 159.39197876032466, \"translation_dx\": -101.87275621292875, \"translation_dy\": -32.606176111808466, \"scale\": 0.6647290774480178}\nB: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nC: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nD: {\"rotation_angle\": -128.74497971799806, \"translation_dx\": -55.835206426128764, \"translation_dy\": 54.178252983369276, \"scale\": 0.8905979693160588}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 127.0599036632886, \"translation_dx\": -26.73103881794438, \"translation_dy\": 16.785326739741976, \"scale\": 1.1214331244941351}\nB: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}\nC: {\"rotation_angle\": -174.94064668132228, \"translation_dx\": 73.73079207136513, \"translation_dy\": 58.25534486945551, \"scale\": 1.178357936048121}\nD: {\"rotation_angle\": 168.86687879669455, \"translation_dx\": 30.327287286076626, \"translation_dy\": -73.84263373893171, \"scale\": 1.0887904122788439}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_113_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_113_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 127.0599036632886, \"translation_dx\": -26.73103881794438, \"translation_dy\": 16.785326739741976, \"scale\": 1.1214331244941351}\nB: {\"rotation_angle\": -137.69315675508605, \"translation_dx\": -14.965017175186233, \"translation_dy\": 28.85856493302694, \"scale\": 0.6970825252863025}\nC: {\"rotation_angle\": -174.94064668132228, \"translation_dx\": 73.73079207136513, \"translation_dy\": 58.25534486945551, \"scale\": 1.178357936048121}\nD: {\"rotation_angle\": 168.86687879669455, \"translation_dx\": 30.327287286076626, \"translation_dy\": -73.84263373893171, \"scale\": 1.0887904122788439}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nB: {\"rotation_angle\": 45.786611297437304, \"translation_dx\": 45.53183354666939, \"translation_dy\": -112.45880863798888, \"scale\": 0.5686394776423458}\nC: {\"rotation_angle\": -83.37935946961306, \"translation_dx\": -63.440112200681114, \"translation_dy\": -47.62616010479583, \"scale\": 0.6518247509991958}\nD: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_114_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_114_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nB: {\"rotation_angle\": 45.786611297437304, \"translation_dx\": 45.53183354666939, \"translation_dy\": -112.45880863798888, \"scale\": 0.5686394776423458}\nC: {\"rotation_angle\": -83.37935946961306, \"translation_dx\": -63.440112200681114, \"translation_dy\": -47.62616010479583, \"scale\": 0.6518247509991958}\nD: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -68.79930104020924, \"translation_dx\": -103.12901971602221, \"translation_dy\": 94.89161684072867, \"scale\": 1.2295411735859756}\nB: {\"rotation_angle\": -176.3085334768787, \"translation_dx\": -26.09189325642553, \"translation_dy\": 21.458056495366975, \"scale\": 0.7934334422653395}\nC: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nD: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_115_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_115_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -68.79930104020924, \"translation_dx\": -103.12901971602221, \"translation_dy\": 94.89161684072867, \"scale\": 1.2295411735859756}\nB: {\"rotation_angle\": -176.3085334768787, \"translation_dx\": -26.09189325642553, \"translation_dy\": 21.458056495366975, \"scale\": 0.7934334422653395}\nC: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nD: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}\nB: {\"rotation_angle\": 49.896013394485834, \"translation_dx\": -25.763756683237403, \"translation_dy\": -26.432232271484168, \"scale\": 1.1619310734744932}\nC: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nD: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_116_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_116_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}\nB: {\"rotation_angle\": 49.896013394485834, \"translation_dx\": -25.763756683237403, \"translation_dy\": -26.432232271484168, \"scale\": 1.1619310734744932}\nC: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nD: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nB: {\"rotation_angle\": -46.75272698463425, \"translation_dx\": 16.424107524155175, \"translation_dy\": -60.683488552754085, \"scale\": 1.375025476214386}\nC: {\"rotation_angle\": 4.601729825002167, \"translation_dx\": -92.34842360064926, \"translation_dy\": 78.34726427877602, \"scale\": 0.7620115680057987}\nD: {\"rotation_angle\": -75.97132980340905, \"translation_dx\": 6.960702322199779, \"translation_dy\": 90.08754109424518, \"scale\": 1.363389071715864}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_117_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_117_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nB: {\"rotation_angle\": -46.75272698463425, \"translation_dx\": 16.424107524155175, \"translation_dy\": -60.683488552754085, \"scale\": 1.375025476214386}\nC: {\"rotation_angle\": 4.601729825002167, \"translation_dx\": -92.34842360064926, \"translation_dy\": 78.34726427877602, \"scale\": 0.7620115680057987}\nD: {\"rotation_angle\": -75.97132980340905, \"translation_dx\": 6.960702322199779, \"translation_dy\": 90.08754109424518, \"scale\": 1.363389071715864}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nB: {\"rotation_angle\": -44.30781692045639, \"translation_dx\": -23.473696812537305, \"translation_dy\": -94.42952089946652, \"scale\": 1.4029179362735564}\nC: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nD: {\"rotation_angle\": 110.02825264959768, \"translation_dx\": -53.26387197670213, \"translation_dy\": 88.43864976013427, \"scale\": 1.4833645013101147}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_118_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_118_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nB: {\"rotation_angle\": -44.30781692045639, \"translation_dx\": -23.473696812537305, \"translation_dy\": -94.42952089946652, \"scale\": 1.4029179362735564}\nC: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nD: {\"rotation_angle\": 110.02825264959768, \"translation_dx\": -53.26387197670213, \"translation_dy\": 88.43864976013427, \"scale\": 1.4833645013101147}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}\nB: {\"rotation_angle\": -32.057796286961064, \"translation_dx\": 119.50392135854452, \"translation_dy\": -17.786253698900993, \"scale\": 1.4583062003808291}\nC: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nD: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_119_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_119_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}\nB: {\"rotation_angle\": -32.057796286961064, \"translation_dx\": 119.50392135854452, \"translation_dy\": -17.786253698900993, \"scale\": 1.4583062003808291}\nC: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nD: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -110.46391589612124, \"translation_dx\": -77.96644542647721, \"translation_dy\": -50.23500265461973, \"scale\": 0.7651088884143488}\nB: {\"rotation_angle\": -22.98450105670534, \"translation_dx\": -24.343109907781525, \"translation_dy\": -75.50859401578859, \"scale\": 0.5077440368943875}\nC: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nD: {\"rotation_angle\": -147.17742740700606, \"translation_dx\": 99.79022385553455, \"translation_dy\": -46.32888217161055, \"scale\": 1.2561938294527635}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_120_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_120_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -110.46391589612124, \"translation_dx\": -77.96644542647721, \"translation_dy\": -50.23500265461973, \"scale\": 0.7651088884143488}\nB: {\"rotation_angle\": -22.98450105670534, \"translation_dx\": -24.343109907781525, \"translation_dy\": -75.50859401578859, \"scale\": 0.5077440368943875}\nC: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nD: {\"rotation_angle\": -147.17742740700606, \"translation_dx\": 99.79022385553455, \"translation_dy\": -46.32888217161055, \"scale\": 1.2561938294527635}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nB: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}\nC: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}\nD: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_121_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_121_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nB: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}\nC: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}\nD: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nB: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}\nC: {\"rotation_angle\": -148.06770236959966, \"translation_dx\": 76.71938731609727, \"translation_dy\": 125.67697929104389, \"scale\": 1.1600663307259453}\nD: {\"rotation_angle\": -131.1795029858263, \"translation_dx\": 17.908074544940433, \"translation_dy\": 120.17637833747304, \"scale\": 0.9471882483559888}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_122_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_122_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nB: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}\nC: {\"rotation_angle\": -148.06770236959966, \"translation_dx\": 76.71938731609727, \"translation_dy\": 125.67697929104389, \"scale\": 1.1600663307259453}\nD: {\"rotation_angle\": -131.1795029858263, \"translation_dx\": 17.908074544940433, \"translation_dy\": 120.17637833747304, \"scale\": 0.9471882483559888}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 70.46713054198463, \"translation_dx\": 21.906055640356044, \"translation_dy\": -12.161170387444017, \"scale\": 0.6983211043742098}\nB: {\"rotation_angle\": -176.3085334768787, \"translation_dx\": -26.09189325642553, \"translation_dy\": 21.458056495366975, \"scale\": 0.7934334422653395}\nC: {\"rotation_angle\": -5.816806483512181, \"translation_dx\": -70.40329792935935, \"translation_dy\": -21.418007440252175, \"scale\": 1.0041476956174793}\nD: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_123_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_123_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 70.46713054198463, \"translation_dx\": 21.906055640356044, \"translation_dy\": -12.161170387444017, \"scale\": 0.6983211043742098}\nB: {\"rotation_angle\": -176.3085334768787, \"translation_dx\": -26.09189325642553, \"translation_dy\": 21.458056495366975, \"scale\": 0.7934334422653395}\nC: {\"rotation_angle\": -5.816806483512181, \"translation_dx\": -70.40329792935935, \"translation_dy\": -21.418007440252175, \"scale\": 1.0041476956174793}\nD: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}\nB: {\"rotation_angle\": -165.5576257925042, \"translation_dx\": 120.02978270991923, \"translation_dy\": -94.68626204020723, \"scale\": 1.377433782383828}\nC: {\"rotation_angle\": -174.94064668132228, \"translation_dx\": 73.73079207136513, \"translation_dy\": 58.25534486945551, \"scale\": 1.178357936048121}\nD: {\"rotation_angle\": -128.74497971799806, \"translation_dx\": -55.835206426128764, \"translation_dy\": 54.178252983369276, \"scale\": 0.8905979693160588}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_124_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_124_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -38.67054772511392, \"translation_dx\": 68.1059088983965, \"translation_dy\": -80.75433684597641, \"scale\": 1.0669693911306672}\nB: {\"rotation_angle\": -165.5576257925042, \"translation_dx\": 120.02978270991923, \"translation_dy\": -94.68626204020723, \"scale\": 1.377433782383828}\nC: {\"rotation_angle\": -174.94064668132228, \"translation_dx\": 73.73079207136513, \"translation_dy\": 58.25534486945551, \"scale\": 1.178357936048121}\nD: {\"rotation_angle\": -128.74497971799806, \"translation_dx\": -55.835206426128764, \"translation_dy\": 54.178252983369276, \"scale\": 0.8905979693160588}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 95.56102360167273, \"translation_dx\": -57.629857243876444, \"translation_dy\": -95.34824117323305, \"scale\": 0.9533126568708786}\nB: {\"rotation_angle\": 36.19361803007027, \"translation_dx\": -50.40071399889004, \"translation_dy\": -85.39533040467117, \"scale\": 0.6522247071940848}\nC: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}\nD: {\"rotation_angle\": -23.02063628299686, \"translation_dx\": -42.06347070905805, \"translation_dy\": 68.90308226059909, \"scale\": 0.7321107429069119}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_125_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_125_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 95.56102360167273, \"translation_dx\": -57.629857243876444, \"translation_dy\": -95.34824117323305, \"scale\": 0.9533126568708786}\nB: {\"rotation_angle\": 36.19361803007027, \"translation_dx\": -50.40071399889004, \"translation_dy\": -85.39533040467117, \"scale\": 0.6522247071940848}\nC: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}\nD: {\"rotation_angle\": -23.02063628299686, \"translation_dx\": -42.06347070905805, \"translation_dy\": 68.90308226059909, \"scale\": 0.7321107429069119}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -103.24791656906933, \"translation_dx\": -2.2454836983213227, \"translation_dy\": 24.014319900588845, \"scale\": 1.3204557483507742}\nB: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nC: {\"rotation_angle\": -94.06455293225282, \"translation_dx\": -52.04430006776356, \"translation_dy\": 88.55937507710391, \"scale\": 0.8369046461483086}\nD: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_126_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_126_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -103.24791656906933, \"translation_dx\": -2.2454836983213227, \"translation_dy\": 24.014319900588845, \"scale\": 1.3204557483507742}\nB: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nC: {\"rotation_angle\": -94.06455293225282, \"translation_dx\": -52.04430006776356, \"translation_dy\": 88.55937507710391, \"scale\": 0.8369046461483086}\nD: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}\nB: {\"rotation_angle\": -149.34069149386406, \"translation_dx\": 81.63420911320063, \"translation_dy\": -26.073567429384056, \"scale\": 1.427947630130646}\nC: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nD: {\"rotation_angle\": 98.62110540120432, \"translation_dx\": 55.8324503005326, \"translation_dy\": -53.32963696213369, \"scale\": 1.3342375308232577}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_127_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_127_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}\nB: {\"rotation_angle\": -149.34069149386406, \"translation_dx\": 81.63420911320063, \"translation_dy\": -26.073567429384056, \"scale\": 1.427947630130646}\nC: {\"rotation_angle\": 37.640985396206986, \"translation_dx\": -97.39428669742068, \"translation_dy\": 17.900860680283458, \"scale\": 1.0930243251030827}\nD: {\"rotation_angle\": 98.62110540120432, \"translation_dx\": 55.8324503005326, \"translation_dy\": -53.32963696213369, \"scale\": 1.3342375308232577}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}\nB: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}\nC: {\"rotation_angle\": -42.98651909317854, \"translation_dx\": 114.49293313374625, \"translation_dy\": -39.53290228333596, \"scale\": 1.442019387031135}\nD: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_128_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_128_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}\nB: {\"rotation_angle\": -5.683971346231118, \"translation_dx\": -0.7123036436211407, \"translation_dy\": -23.660599152813326, \"scale\": 1.1241034499451734}\nC: {\"rotation_angle\": -42.98651909317854, \"translation_dx\": 114.49293313374625, \"translation_dy\": -39.53290228333596, \"scale\": 1.442019387031135}\nD: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -163.07830945514343, \"translation_dx\": 107.25371607945826, \"translation_dy\": 44.19319462200147, \"scale\": 1.0330497674624493}\nB: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}\nC: {\"rotation_angle\": 137.29869982747988, \"translation_dx\": 75.41375097241084, \"translation_dy\": 55.66358575693553, \"scale\": 1.1335508281242805}\nD: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_129_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_129_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -163.07830945514343, \"translation_dx\": 107.25371607945826, \"translation_dy\": 44.19319462200147, \"scale\": 1.0330497674624493}\nB: {\"rotation_angle\": 137.6047485759084, \"translation_dx\": -27.00857214512888, \"translation_dy\": -94.97246325619065, \"scale\": 1.1628545134465245}\nC: {\"rotation_angle\": 137.29869982747988, \"translation_dx\": 75.41375097241084, \"translation_dy\": 55.66358575693553, \"scale\": 1.1335508281242805}\nD: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}\nB: {\"rotation_angle\": 171.23105805984426, \"translation_dx\": 28.800906238980815, \"translation_dy\": 60.921924115709544, \"scale\": 1.4441070487112413}\nC: {\"rotation_angle\": -44.30781692045639, \"translation_dx\": -23.473696812537305, \"translation_dy\": -94.42952089946652, \"scale\": 1.4029179362735564}\nD: {\"rotation_angle\": 172.84173099768327, \"translation_dx\": -36.82796075364796, \"translation_dy\": -15.346257103503191, \"scale\": 0.8112655094699114}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_130_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_130_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 153.24034529323683, \"translation_dx\": -80.95083564593054, \"translation_dy\": 58.17854805068575, \"scale\": 0.8564275095577245}\nB: {\"rotation_angle\": 171.23105805984426, \"translation_dx\": 28.800906238980815, \"translation_dy\": 60.921924115709544, \"scale\": 1.4441070487112413}\nC: {\"rotation_angle\": -44.30781692045639, \"translation_dx\": -23.473696812537305, \"translation_dy\": -94.42952089946652, \"scale\": 1.4029179362735564}\nD: {\"rotation_angle\": 172.84173099768327, \"translation_dx\": -36.82796075364796, \"translation_dy\": -15.346257103503191, \"scale\": 0.8112655094699114}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -6.970858631484532, \"translation_dx\": -2.793256631611797, \"translation_dy\": 83.08133552847667, \"scale\": 1.4237697720578382}\nB: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nC: {\"rotation_angle\": 88.199522854527, \"translation_dx\": 18.814421533590917, \"translation_dy\": -27.135307313502466, \"scale\": 1.37855935527965}\nD: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_131_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_131_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -6.970858631484532, \"translation_dx\": -2.793256631611797, \"translation_dy\": 83.08133552847667, \"scale\": 1.4237697720578382}\nB: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nC: {\"rotation_angle\": 88.199522854527, \"translation_dx\": 18.814421533590917, \"translation_dy\": -27.135307313502466, \"scale\": 1.37855935527965}\nD: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nB: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nC: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nD: {\"rotation_angle\": 157.75388648393812, \"translation_dx\": 20.356281771878216, \"translation_dy\": 16.09866009065132, \"scale\": 0.523349135390574}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_132_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_132_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nB: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nC: {\"rotation_angle\": -31.020660516088725, \"translation_dx\": 105.99805178546191, \"translation_dy\": -82.8489656004858, \"scale\": 1.0703563169477137}\nD: {\"rotation_angle\": 157.75388648393812, \"translation_dx\": 20.356281771878216, \"translation_dy\": 16.09866009065132, \"scale\": 0.523349135390574}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nB: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nC: {\"rotation_angle\": -149.42147215379055, \"translation_dx\": 2.3444194857030283, \"translation_dy\": 35.92779325530762, \"scale\": 1.0223945055206394}\nD: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_133_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_133_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nB: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nC: {\"rotation_angle\": -149.42147215379055, \"translation_dx\": 2.3444194857030283, \"translation_dy\": 35.92779325530762, \"scale\": 1.0223945055206394}\nD: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 45.786611297437304, \"translation_dx\": 45.53183354666939, \"translation_dy\": -112.45880863798888, \"scale\": 0.5686394776423458}\nB: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nC: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nD: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_134_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_134_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 45.786611297437304, \"translation_dx\": 45.53183354666939, \"translation_dy\": -112.45880863798888, \"scale\": 0.5686394776423458}\nB: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nC: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nD: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nB: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nC: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}\nD: {\"rotation_angle\": 130.382151153576, \"translation_dx\": 48.77925626504499, \"translation_dy\": 54.89982459749416, \"scale\": 1.3647831130001666}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_135_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_135_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nB: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nC: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}\nD: {\"rotation_angle\": 130.382151153576, \"translation_dx\": 48.77925626504499, \"translation_dy\": 54.89982459749416, \"scale\": 1.3647831130001666}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 45.786611297437304, \"translation_dx\": 45.53183354666939, \"translation_dy\": -112.45880863798888, \"scale\": 0.5686394776423458}\nB: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}\nC: {\"rotation_angle\": -124.27587082376021, \"translation_dx\": -88.19288051455345, \"translation_dy\": 24.145134775980125, \"scale\": 1.4414104211047083}\nD: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_136_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_136_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 45.786611297437304, \"translation_dx\": 45.53183354666939, \"translation_dy\": -112.45880863798888, \"scale\": 0.5686394776423458}\nB: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}\nC: {\"rotation_angle\": -124.27587082376021, \"translation_dx\": -88.19288051455345, \"translation_dy\": 24.145134775980125, \"scale\": 1.4414104211047083}\nD: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nB: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nC: {\"rotation_angle\": -165.5576257925042, \"translation_dx\": 120.02978270991923, \"translation_dy\": -94.68626204020723, \"scale\": 1.377433782383828}\nD: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_137_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_137_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nB: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nC: {\"rotation_angle\": -165.5576257925042, \"translation_dx\": 120.02978270991923, \"translation_dy\": -94.68626204020723, \"scale\": 1.377433782383828}\nD: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -50.19218790392131, \"translation_dx\": -27.31734251737683, \"translation_dy\": 8.514724344494553, \"scale\": 1.0874517053433594}\nB: {\"rotation_angle\": -176.3085334768787, \"translation_dx\": -26.09189325642553, \"translation_dy\": 21.458056495366975, \"scale\": 0.7934334422653395}\nC: {\"rotation_angle\": -6.970858631484532, \"translation_dx\": -2.793256631611797, \"translation_dy\": 83.08133552847667, \"scale\": 1.4237697720578382}\nD: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_138_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_138_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -50.19218790392131, \"translation_dx\": -27.31734251737683, \"translation_dy\": 8.514724344494553, \"scale\": 1.0874517053433594}\nB: {\"rotation_angle\": -176.3085334768787, \"translation_dx\": -26.09189325642553, \"translation_dy\": 21.458056495366975, \"scale\": 0.7934334422653395}\nC: {\"rotation_angle\": -6.970858631484532, \"translation_dx\": -2.793256631611797, \"translation_dy\": 83.08133552847667, \"scale\": 1.4237697720578382}\nD: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nB: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nC: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nD: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_139_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_139_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nB: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nC: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nD: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 172.84173099768327, \"translation_dx\": -36.82796075364796, \"translation_dy\": -15.346257103503191, \"scale\": 0.8112655094699114}\nB: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nC: {\"rotation_angle\": -178.96154331790243, \"translation_dx\": -45.831117140591004, \"translation_dy\": 14.962223802901406, \"scale\": 1.4059876442036168}\nD: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_140_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_140_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 172.84173099768327, \"translation_dx\": -36.82796075364796, \"translation_dy\": -15.346257103503191, \"scale\": 0.8112655094699114}\nB: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nC: {\"rotation_angle\": -178.96154331790243, \"translation_dx\": -45.831117140591004, \"translation_dy\": 14.962223802901406, \"scale\": 1.4059876442036168}\nD: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nB: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nC: {\"rotation_angle\": 115.44035395260755, \"translation_dx\": 104.38539690843712, \"translation_dy\": -82.71757148170198, \"scale\": 0.6534862534786243}\nD: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_141_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_141_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nB: {\"rotation_angle\": -79.55706788063112, \"translation_dx\": -38.613403166877674, \"translation_dy\": 48.56888435185245, \"scale\": 1.368947012195521}\nC: {\"rotation_angle\": 115.44035395260755, \"translation_dx\": 104.38539690843712, \"translation_dy\": -82.71757148170198, \"scale\": 0.6534862534786243}\nD: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nB: {\"rotation_angle\": -178.96154331790243, \"translation_dx\": -45.831117140591004, \"translation_dy\": 14.962223802901406, \"scale\": 1.4059876442036168}\nC: {\"rotation_angle\": -70.97525301082955, \"translation_dx\": -28.380848037876873, \"translation_dy\": 54.37723426674512, \"scale\": 0.9024922197892329}\nD: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_142_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_142_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nB: {\"rotation_angle\": -178.96154331790243, \"translation_dx\": -45.831117140591004, \"translation_dy\": 14.962223802901406, \"scale\": 1.4059876442036168}\nC: {\"rotation_angle\": -70.97525301082955, \"translation_dx\": -28.380848037876873, \"translation_dy\": 54.37723426674512, \"scale\": 0.9024922197892329}\nD: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 12.872370969250312, \"translation_dx\": -43.1533458138392, \"translation_dy\": -64.88511529320917, \"scale\": 1.3092068537816153}\nB: {\"rotation_angle\": -123.92621597373325, \"translation_dx\": 115.25994331141689, \"translation_dy\": -45.13111299141354, \"scale\": 1.164470344420729}\nC: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}\nD: {\"rotation_angle\": 55.990963226006784, \"translation_dx\": 71.2358057599877, \"translation_dy\": 22.751866785772563, \"scale\": 1.4964705985201703}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_143_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_143_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 12.872370969250312, \"translation_dx\": -43.1533458138392, \"translation_dy\": -64.88511529320917, \"scale\": 1.3092068537816153}\nB: {\"rotation_angle\": -123.92621597373325, \"translation_dx\": 115.25994331141689, \"translation_dy\": -45.13111299141354, \"scale\": 1.164470344420729}\nC: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}\nD: {\"rotation_angle\": 55.990963226006784, \"translation_dx\": 71.2358057599877, \"translation_dy\": 22.751866785772563, \"scale\": 1.4964705985201703}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nB: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}\nC: {\"rotation_angle\": -5.816806483512181, \"translation_dx\": -70.40329792935935, \"translation_dy\": -21.418007440252175, \"scale\": 1.0041476956174793}\nD: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_144_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_144_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nB: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}\nC: {\"rotation_angle\": -5.816806483512181, \"translation_dx\": -70.40329792935935, \"translation_dy\": -21.418007440252175, \"scale\": 1.0041476956174793}\nD: {\"rotation_angle\": -95.56761680572791, \"translation_dx\": -92.07587430861633, \"translation_dy\": -64.18919222058364, \"scale\": 1.033728049154846}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -42.98651909317854, \"translation_dx\": 114.49293313374625, \"translation_dy\": -39.53290228333596, \"scale\": 1.442019387031135}\nB: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nC: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}\nD: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_145_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_145_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -42.98651909317854, \"translation_dx\": 114.49293313374625, \"translation_dy\": -39.53290228333596, \"scale\": 1.442019387031135}\nB: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nC: {\"rotation_angle\": 120.9888581359325, \"translation_dx\": 2.43720894071744, \"translation_dy\": -7.865691814940682, \"scale\": 0.5519813971136048}\nD: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -6.38420562293993, \"translation_dx\": -106.80670691302902, \"translation_dy\": -3.5935098985529663, \"scale\": 1.3037846299861797}\nB: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}\nC: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}\nD: {\"rotation_angle\": 137.29869982747988, \"translation_dx\": 75.41375097241084, \"translation_dy\": 55.66358575693553, \"scale\": 1.1335508281242805}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_146_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_146_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -6.38420562293993, \"translation_dx\": -106.80670691302902, \"translation_dy\": -3.5935098985529663, \"scale\": 1.3037846299861797}\nB: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}\nC: {\"rotation_angle\": 97.08459407481979, \"translation_dx\": 38.76418659488206, \"translation_dy\": 44.81166266995322, \"scale\": 1.27585958531192}\nD: {\"rotation_angle\": 137.29869982747988, \"translation_dx\": 75.41375097241084, \"translation_dy\": 55.66358575693553, \"scale\": 1.1335508281242805}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nB: {\"rotation_angle\": -8.756342422911757, \"translation_dx\": -120.12147874311805, \"translation_dy\": -16.659510954699698, \"scale\": 0.8471832394055047}\nC: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nD: {\"rotation_angle\": -4.364889011784271, \"translation_dx\": 74.89385338851659, \"translation_dy\": 29.259521498010997, \"scale\": 1.2877948451877137}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_147_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_147_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nB: {\"rotation_angle\": -8.756342422911757, \"translation_dx\": -120.12147874311805, \"translation_dy\": -16.659510954699698, \"scale\": 0.8471832394055047}\nC: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nD: {\"rotation_angle\": -4.364889011784271, \"translation_dx\": 74.89385338851659, \"translation_dy\": 29.259521498010997, \"scale\": 1.2877948451877137}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nB: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nC: {\"rotation_angle\": 161.7596265938729, \"translation_dx\": -9.170216354863072, \"translation_dy\": -19.23222492696047, \"scale\": 1.1821087248622173}\nD: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_148_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_148_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -127.03310490562403, \"translation_dx\": -44.497972498107885, \"translation_dy\": 53.252184804163164, \"scale\": 0.8807762361133948}\nB: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nC: {\"rotation_angle\": 161.7596265938729, \"translation_dx\": -9.170216354863072, \"translation_dy\": -19.23222492696047, \"scale\": 1.1821087248622173}\nD: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -32.96407209098831, \"translation_dx\": -27.518946535455143, \"translation_dy\": 2.5370159689679213, \"scale\": 1.259328459428434}\nB: {\"rotation_angle\": -148.06770236959966, \"translation_dx\": 76.71938731609727, \"translation_dy\": 125.67697929104389, \"scale\": 1.1600663307259453}\nC: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nD: {\"rotation_angle\": -6.258618837806779, \"translation_dx\": -117.56200624611057, \"translation_dy\": -84.92852320396813, \"scale\": 0.8703619649920769}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_149_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_149_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -32.96407209098831, \"translation_dx\": -27.518946535455143, \"translation_dy\": 2.5370159689679213, \"scale\": 1.259328459428434}\nB: {\"rotation_angle\": -148.06770236959966, \"translation_dx\": 76.71938731609727, \"translation_dy\": 125.67697929104389, \"scale\": 1.1600663307259453}\nC: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nD: {\"rotation_angle\": -6.258618837806779, \"translation_dx\": -117.56200624611057, \"translation_dy\": -84.92852320396813, \"scale\": 0.8703619649920769}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}\nB: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nC: {\"rotation_angle\": 156.4647723112265, \"translation_dx\": -66.53886800122852, \"translation_dy\": 64.98500274528308, \"scale\": 1.1427015309184732}\nD: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_150_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_150_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}\nB: {\"rotation_angle\": 134.59992138556464, \"translation_dx\": 5.908404103559974, \"translation_dy\": 47.60587687007518, \"scale\": 1.0105063493742612}\nC: {\"rotation_angle\": 156.4647723112265, \"translation_dx\": -66.53886800122852, \"translation_dy\": 64.98500274528308, \"scale\": 1.1427015309184732}\nD: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -51.98717119490195, \"translation_dx\": -83.93544420557635, \"translation_dy\": -17.359661719977098, \"scale\": 1.0858344969275349}\nB: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}\nC: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nD: {\"rotation_angle\": 96.727171962103, \"translation_dx\": 36.81177221178956, \"translation_dy\": 18.012374651364837, \"scale\": 0.7274955443317854}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_151_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_151_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -51.98717119490195, \"translation_dx\": -83.93544420557635, \"translation_dy\": -17.359661719977098, \"scale\": 1.0858344969275349}\nB: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}\nC: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nD: {\"rotation_angle\": 96.727171962103, \"translation_dx\": 36.81177221178956, \"translation_dy\": 18.012374651364837, \"scale\": 0.7274955443317854}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 18.52926347539298, \"translation_dx\": -26.155433185237058, \"translation_dy\": -39.799299198218556, \"scale\": 0.9355127285855813}\nB: {\"rotation_angle\": 74.4727172984789, \"translation_dx\": 83.0498783040965, \"translation_dy\": 24.573318419119772, \"scale\": 1.4775593630739356}\nC: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nD: {\"rotation_angle\": 95.56102360167273, \"translation_dx\": -57.629857243876444, \"translation_dy\": -95.34824117323305, \"scale\": 0.9533126568708786}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_152_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_152_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 18.52926347539298, \"translation_dx\": -26.155433185237058, \"translation_dy\": -39.799299198218556, \"scale\": 0.9355127285855813}\nB: {\"rotation_angle\": 74.4727172984789, \"translation_dx\": 83.0498783040965, \"translation_dy\": 24.573318419119772, \"scale\": 1.4775593630739356}\nC: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nD: {\"rotation_angle\": 95.56102360167273, \"translation_dx\": -57.629857243876444, \"translation_dy\": -95.34824117323305, \"scale\": 0.9533126568708786}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}\nB: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}\nC: {\"rotation_angle\": 133.22970053001933, \"translation_dx\": 30.83867253278636, \"translation_dy\": 9.987607615316023, \"scale\": 0.9746642566652708}\nD: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_153_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_153_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}\nB: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}\nC: {\"rotation_angle\": 133.22970053001933, \"translation_dx\": 30.83867253278636, \"translation_dy\": 9.987607615316023, \"scale\": 0.9746642566652708}\nD: {\"rotation_angle\": -38.58021171568234, \"translation_dx\": -80.14139661496048, \"translation_dy\": 7.985099889843255, \"scale\": 1.029545268033875}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 103.56580652114087, \"translation_dx\": -76.88940345297716, \"translation_dy\": -3.4544443607121593, \"scale\": 1.3949152683659345}\nB: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nC: {\"rotation_angle\": -99.80397961792426, \"translation_dx\": 113.2252387398062, \"translation_dy\": -61.846052830557056, \"scale\": 1.080357872583317}\nD: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_154_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_154_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 103.56580652114087, \"translation_dx\": -76.88940345297716, \"translation_dy\": -3.4544443607121593, \"scale\": 1.3949152683659345}\nB: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nC: {\"rotation_angle\": -99.80397961792426, \"translation_dx\": 113.2252387398062, \"translation_dy\": -61.846052830557056, \"scale\": 1.080357872583317}\nD: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}\nB: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}\nC: {\"rotation_angle\": -59.18065174130953, \"translation_dx\": -66.15733764198566, \"translation_dy\": -32.06450758946801, \"scale\": 1.1967157159259998}\nD: {\"rotation_angle\": -153.3687774434925, \"translation_dx\": 50.92336593606055, \"translation_dy\": -56.81603844715568, \"scale\": 1.398231264497651}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_155_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_155_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}\nB: {\"rotation_angle\": -15.445234303955033, \"translation_dx\": 52.656313993324545, \"translation_dy\": 4.243768644047549, \"scale\": 0.8747335302455691}\nC: {\"rotation_angle\": -59.18065174130953, \"translation_dx\": -66.15733764198566, \"translation_dy\": -32.06450758946801, \"scale\": 1.1967157159259998}\nD: {\"rotation_angle\": -153.3687774434925, \"translation_dx\": 50.92336593606055, \"translation_dy\": -56.81603844715568, \"scale\": 1.398231264497651}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -153.3687774434925, \"translation_dx\": 50.92336593606055, \"translation_dy\": -56.81603844715568, \"scale\": 1.398231264497651}\nB: {\"rotation_angle\": -124.74198080809023, \"translation_dx\": -48.23531115232953, \"translation_dy\": 52.62526617026404, \"scale\": 1.3484625774406969}\nC: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}\nD: {\"rotation_angle\": -32.057796286961064, \"translation_dx\": 119.50392135854452, \"translation_dy\": -17.786253698900993, \"scale\": 1.4583062003808291}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_156_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_156_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -153.3687774434925, \"translation_dx\": 50.92336593606055, \"translation_dy\": -56.81603844715568, \"scale\": 1.398231264497651}\nB: {\"rotation_angle\": -124.74198080809023, \"translation_dx\": -48.23531115232953, \"translation_dy\": 52.62526617026404, \"scale\": 1.3484625774406969}\nC: {\"rotation_angle\": -132.6730586187399, \"translation_dx\": -14.723128468316531, \"translation_dy\": -95.44210429834934, \"scale\": 1.0421065600095725}\nD: {\"rotation_angle\": -32.057796286961064, \"translation_dx\": 119.50392135854452, \"translation_dy\": -17.786253698900993, \"scale\": 1.4583062003808291}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 52.27392299801002, \"translation_dx\": -7.943242591889941, \"translation_dy\": -1.8318597711701017, \"scale\": 1.489664776133741}\nB: {\"rotation_angle\": -153.95647753312159, \"translation_dx\": 64.08546266437509, \"translation_dy\": -34.554486291313935, \"scale\": 1.423360690418288}\nC: {\"rotation_angle\": -126.15991399279281, \"translation_dx\": 24.895638463286446, \"translation_dy\": -35.71086816730676, \"scale\": 1.30648936857296}\nD: {\"rotation_angle\": 98.62110540120432, \"translation_dx\": 55.8324503005326, \"translation_dy\": -53.32963696213369, \"scale\": 1.3342375308232577}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_157_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_157_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 52.27392299801002, \"translation_dx\": -7.943242591889941, \"translation_dy\": -1.8318597711701017, \"scale\": 1.489664776133741}\nB: {\"rotation_angle\": -153.95647753312159, \"translation_dx\": 64.08546266437509, \"translation_dy\": -34.554486291313935, \"scale\": 1.423360690418288}\nC: {\"rotation_angle\": -126.15991399279281, \"translation_dx\": 24.895638463286446, \"translation_dy\": -35.71086816730676, \"scale\": 1.30648936857296}\nD: {\"rotation_angle\": 98.62110540120432, \"translation_dx\": 55.8324503005326, \"translation_dy\": -53.32963696213369, \"scale\": 1.3342375308232577}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -44.30781692045639, \"translation_dx\": -23.473696812537305, \"translation_dy\": -94.42952089946652, \"scale\": 1.4029179362735564}\nB: {\"rotation_angle\": 157.75388648393812, \"translation_dx\": 20.356281771878216, \"translation_dy\": 16.09866009065132, \"scale\": 0.523349135390574}\nC: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}\nD: {\"rotation_angle\": 168.86687879669455, \"translation_dx\": 30.327287286076626, \"translation_dy\": -73.84263373893171, \"scale\": 1.0887904122788439}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_158_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_158_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -44.30781692045639, \"translation_dx\": -23.473696812537305, \"translation_dy\": -94.42952089946652, \"scale\": 1.4029179362735564}\nB: {\"rotation_angle\": 157.75388648393812, \"translation_dx\": 20.356281771878216, \"translation_dy\": 16.09866009065132, \"scale\": 0.523349135390574}\nC: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}\nD: {\"rotation_angle\": 168.86687879669455, \"translation_dx\": 30.327287286076626, \"translation_dy\": -73.84263373893171, \"scale\": 1.0887904122788439}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nB: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}\nC: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}\nD: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_159_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_159_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nB: {\"rotation_angle\": -76.09611957445006, \"translation_dx\": -118.19634710213703, \"translation_dy\": 85.91610719889127, \"scale\": 1.371999627635525}\nC: {\"rotation_angle\": -16.878745814478265, \"translation_dx\": -68.86659110743665, \"translation_dy\": -98.54142762965468, \"scale\": 1.2648663928919022}\nD: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nB: {\"rotation_angle\": -162.34443008832744, \"translation_dx\": 11.222356042803995, \"translation_dy\": -20.913798214168963, \"scale\": 0.5876305148063811}\nC: {\"rotation_angle\": -41.748048059314925, \"translation_dx\": 84.2495675740148, \"translation_dy\": -81.02778113177463, \"scale\": 1.207158201764622}\nD: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_160_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_160_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nB: {\"rotation_angle\": -162.34443008832744, \"translation_dx\": 11.222356042803995, \"translation_dy\": -20.913798214168963, \"scale\": 0.5876305148063811}\nC: {\"rotation_angle\": -41.748048059314925, \"translation_dx\": 84.2495675740148, \"translation_dy\": -81.02778113177463, \"scale\": 1.207158201764622}\nD: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}\nB: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nC: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nD: {\"rotation_angle\": -22.98450105670534, \"translation_dx\": -24.343109907781525, \"translation_dy\": -75.50859401578859, \"scale\": 0.5077440368943875}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_161_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_161_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}\nB: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nC: {\"rotation_angle\": -49.11147497176091, \"translation_dx\": -21.61309921155923, \"translation_dy\": 41.841400081955015, \"scale\": 1.3374733710705384}\nD: {\"rotation_angle\": -22.98450105670534, \"translation_dx\": -24.343109907781525, \"translation_dy\": -75.50859401578859, \"scale\": 0.5077440368943875}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 4.601729825002167, \"translation_dx\": -92.34842360064926, \"translation_dy\": 78.34726427877602, \"scale\": 0.7620115680057987}\nB: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nC: {\"rotation_angle\": -110.46391589612124, \"translation_dx\": -77.96644542647721, \"translation_dy\": -50.23500265461973, \"scale\": 0.7651088884143488}\nD: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_162_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_162_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 4.601729825002167, \"translation_dx\": -92.34842360064926, \"translation_dy\": 78.34726427877602, \"scale\": 0.7620115680057987}\nB: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nC: {\"rotation_angle\": -110.46391589612124, \"translation_dx\": -77.96644542647721, \"translation_dy\": -50.23500265461973, \"scale\": 0.7651088884143488}\nD: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nB: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nC: {\"rotation_angle\": -162.34443008832744, \"translation_dx\": 11.222356042803995, \"translation_dy\": -20.913798214168963, \"scale\": 0.5876305148063811}\nD: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_163_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_163_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nB: {\"rotation_angle\": -137.58016126496426, \"translation_dx\": 45.631572391068715, \"translation_dy\": -54.72741054396442, \"scale\": 1.391656794638211}\nC: {\"rotation_angle\": -162.34443008832744, \"translation_dx\": 11.222356042803995, \"translation_dy\": -20.913798214168963, \"scale\": 0.5876305148063811}\nD: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}\nB: {\"rotation_angle\": -162.34443008832744, \"translation_dx\": 11.222356042803995, \"translation_dy\": -20.913798214168963, \"scale\": 0.5876305148063811}\nC: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}\nD: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_164_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_164_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -127.2688410750471, \"translation_dx\": 10.330064507300825, \"translation_dy\": -25.010404065134438, \"scale\": 1.1376215421095472}\nB: {\"rotation_angle\": -162.34443008832744, \"translation_dx\": 11.222356042803995, \"translation_dy\": -20.913798214168963, \"scale\": 0.5876305148063811}\nC: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}\nD: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}\nB: {\"rotation_angle\": 23.955007488404988, \"translation_dx\": 90.0018582930472, \"translation_dy\": 38.03553582875617, \"scale\": 1.3380437802347522}\nC: {\"rotation_angle\": 136.76946369368522, \"translation_dx\": 86.13615517916296, \"translation_dy\": 47.49597577737802, \"scale\": 1.1842967613683704}\nD: {\"rotation_angle\": 157.75388648393812, \"translation_dx\": 20.356281771878216, \"translation_dy\": 16.09866009065132, \"scale\": 0.523349135390574}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_165_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_165_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}\nB: {\"rotation_angle\": 23.955007488404988, \"translation_dx\": 90.0018582930472, \"translation_dy\": 38.03553582875617, \"scale\": 1.3380437802347522}\nC: {\"rotation_angle\": 136.76946369368522, \"translation_dx\": 86.13615517916296, \"translation_dy\": 47.49597577737802, \"scale\": 1.1842967613683704}\nD: {\"rotation_angle\": 157.75388648393812, \"translation_dx\": 20.356281771878216, \"translation_dy\": 16.09866009065132, \"scale\": 0.523349135390574}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nB: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}\nC: {\"rotation_angle\": -178.96154331790243, \"translation_dx\": -45.831117140591004, \"translation_dy\": 14.962223802901406, \"scale\": 1.4059876442036168}\nD: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_166_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_166_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -128.93587705152078, \"translation_dx\": 48.830662388872895, \"translation_dy\": 65.60255696435819, \"scale\": 0.5618983722639579}\nB: {\"rotation_angle\": 159.25105466068987, \"translation_dx\": -126.35420360425098, \"translation_dy\": -17.54721978726404, \"scale\": 1.4952435062275256}\nC: {\"rotation_angle\": -178.96154331790243, \"translation_dx\": -45.831117140591004, \"translation_dy\": 14.962223802901406, \"scale\": 1.4059876442036168}\nD: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}\nB: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nC: {\"rotation_angle\": 28.728757892682808, \"translation_dx\": 12.065384659700086, \"translation_dy\": -119.64549643343977, \"scale\": 1.126100132224236}\nD: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_167_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_167_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}\nB: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nC: {\"rotation_angle\": 28.728757892682808, \"translation_dx\": 12.065384659700086, \"translation_dy\": -119.64549643343977, \"scale\": 1.126100132224236}\nD: {\"rotation_angle\": 173.6372649335733, \"translation_dx\": -7.357207392874017, \"translation_dy\": -51.70776156994498, \"scale\": 1.09720142096939}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}\nB: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}\nC: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nD: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_168_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_168_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}\nB: {\"rotation_angle\": 64.33574528550244, \"translation_dx\": -83.09111528364858, \"translation_dy\": 12.26726314152404, \"scale\": 0.7845370507816389}\nC: {\"rotation_angle\": -97.38730278840897, \"translation_dx\": 79.58431404822528, \"translation_dy\": -65.17570525641105, \"scale\": 0.8501057849742453}\nD: {\"rotation_angle\": -4.956802948250129, \"translation_dx\": -46.115491929325685, \"translation_dy\": 39.01349173096322, \"scale\": 1.02280257064298}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -44.902472769484746, \"translation_dx\": -36.85475324083902, \"translation_dy\": 36.81692000181951, \"scale\": 1.0769710077370194}\nB: {\"rotation_angle\": -161.22593365548192, \"translation_dx\": -119.73961882572601, \"translation_dy\": -93.50838821854722, \"scale\": 1.4476413063179399}\nC: {\"rotation_angle\": 32.25033099080062, \"translation_dx\": -33.246475706714875, \"translation_dy\": -9.848772328845214, \"scale\": 0.986502265576198}\nD: {\"rotation_angle\": 55.990963226006784, \"translation_dx\": 71.2358057599877, \"translation_dy\": 22.751866785772563, \"scale\": 1.4964705985201703}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_169_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_169_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -44.902472769484746, \"translation_dx\": -36.85475324083902, \"translation_dy\": 36.81692000181951, \"scale\": 1.0769710077370194}\nB: {\"rotation_angle\": -161.22593365548192, \"translation_dx\": -119.73961882572601, \"translation_dy\": -93.50838821854722, \"scale\": 1.4476413063179399}\nC: {\"rotation_angle\": 32.25033099080062, \"translation_dx\": -33.246475706714875, \"translation_dy\": -9.848772328845214, \"scale\": 0.986502265576198}\nD: {\"rotation_angle\": 55.990963226006784, \"translation_dx\": 71.2358057599877, \"translation_dy\": 22.751866785772563, \"scale\": 1.4964705985201703}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -79.27003163090343, \"translation_dx\": 8.207736130313549, \"translation_dy\": 6.670417118750038, \"scale\": 1.3327657238113826}\nB: {\"rotation_angle\": -32.057796286961064, \"translation_dx\": 119.50392135854452, \"translation_dy\": -17.786253698900993, \"scale\": 1.4583062003808291}\nC: {\"rotation_angle\": 172.84173099768327, \"translation_dx\": -36.82796075364796, \"translation_dy\": -15.346257103503191, \"scale\": 0.8112655094699114}\nD: {\"rotation_angle\": 138.15953129001275, \"translation_dx\": 108.29077351507729, \"translation_dy\": 11.25207260435026, \"scale\": 1.2682750116992958}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_170_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_170_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -79.27003163090343, \"translation_dx\": 8.207736130313549, \"translation_dy\": 6.670417118750038, \"scale\": 1.3327657238113826}\nB: {\"rotation_angle\": -32.057796286961064, \"translation_dx\": 119.50392135854452, \"translation_dy\": -17.786253698900993, \"scale\": 1.4583062003808291}\nC: {\"rotation_angle\": 172.84173099768327, \"translation_dx\": -36.82796075364796, \"translation_dy\": -15.346257103503191, \"scale\": 0.8112655094699114}\nD: {\"rotation_angle\": 138.15953129001275, \"translation_dx\": 108.29077351507729, \"translation_dy\": 11.25207260435026, \"scale\": 1.2682750116992958}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nB: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nC: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nD: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_171_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_171_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nB: {\"rotation_angle\": -152.40502323992493, \"translation_dx\": -0.6096313646742146, \"translation_dy\": 26.2224872549711, \"scale\": 0.6008305458537412}\nC: {\"rotation_angle\": 136.2943203908062, \"translation_dx\": 59.15508525636656, \"translation_dy\": -38.46099161723379, \"scale\": 0.6414776081953896}\nD: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}\nB: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nC: {\"rotation_angle\": -35.37165300247324, \"translation_dx\": -51.674784510203665, \"translation_dy\": 35.0550301640573, \"scale\": 1.181842779166554}\nD: {\"rotation_angle\": -147.17742740700606, \"translation_dx\": 99.79022385553455, \"translation_dy\": -46.32888217161055, \"scale\": 1.2561938294527635}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_172_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_172_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}\nB: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nC: {\"rotation_angle\": -35.37165300247324, \"translation_dx\": -51.674784510203665, \"translation_dy\": 35.0550301640573, \"scale\": 1.181842779166554}\nD: {\"rotation_angle\": -147.17742740700606, \"translation_dx\": 99.79022385553455, \"translation_dy\": -46.32888217161055, \"scale\": 1.2561938294527635}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}\nB: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}\nC: {\"rotation_angle\": -165.5576257925042, \"translation_dx\": 120.02978270991923, \"translation_dy\": -94.68626204020723, \"scale\": 1.377433782383828}\nD: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_173_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_173_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 139.13421797404374, \"translation_dx\": -107.62188977651758, \"translation_dy\": -65.35657968686931, \"scale\": 0.569575564082204}\nB: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}\nC: {\"rotation_angle\": -165.5576257925042, \"translation_dx\": 120.02978270991923, \"translation_dy\": -94.68626204020723, \"scale\": 1.377433782383828}\nD: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}\nB: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nC: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}\nD: {\"rotation_angle\": 51.652651058291696, \"translation_dx\": -79.60059266318888, \"translation_dy\": 40.24223939512936, \"scale\": 1.045377495061187}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_174_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_174_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -53.475823147809436, \"translation_dx\": -52.11444637245131, \"translation_dy\": -7.974464084606126, \"scale\": 1.302004904680502}\nB: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nC: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}\nD: {\"rotation_angle\": 51.652651058291696, \"translation_dx\": -79.60059266318888, \"translation_dy\": 40.24223939512936, \"scale\": 1.045377495061187}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 160.04018122869564, \"translation_dx\": -10.031879581871024, \"translation_dy\": 74.10075881851205, \"scale\": 0.8976020445815951}\nB: {\"rotation_angle\": 134.22497079750707, \"translation_dx\": -56.33244292094708, \"translation_dy\": 12.15417280277697, \"scale\": 1.260404381889235}\nC: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nD: {\"rotation_angle\": 162.98131081099467, \"translation_dx\": -80.19473687776261, \"translation_dy\": -17.70282064458462, \"scale\": 1.2855975600149028}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_175_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_175_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 160.04018122869564, \"translation_dx\": -10.031879581871024, \"translation_dy\": 74.10075881851205, \"scale\": 0.8976020445815951}\nB: {\"rotation_angle\": 134.22497079750707, \"translation_dx\": -56.33244292094708, \"translation_dy\": 12.15417280277697, \"scale\": 1.260404381889235}\nC: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nD: {\"rotation_angle\": 162.98131081099467, \"translation_dx\": -80.19473687776261, \"translation_dy\": -17.70282064458462, \"scale\": 1.2855975600149028}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}\nB: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nC: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nD: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_176_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_176_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 170.5673161572617, \"translation_dx\": -54.14309140946517, \"translation_dy\": -20.9067824061149, \"scale\": 0.74080987054586}\nB: {\"rotation_angle\": 98.88222011850513, \"translation_dx\": 98.58699088344886, \"translation_dy\": 52.424259863835346, \"scale\": 0.8670994673205047}\nC: {\"rotation_angle\": 107.15748471049534, \"translation_dx\": -112.04520804841785, \"translation_dy\": 107.36899853350675, \"scale\": 0.784106447062462}\nD: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -75.97132980340905, \"translation_dx\": 6.960702322199779, \"translation_dy\": 90.08754109424518, \"scale\": 1.363389071715864}\nB: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}\nC: {\"rotation_angle\": 106.62912259997893, \"translation_dx\": -62.19399566166837, \"translation_dy\": -63.078041204745844, \"scale\": 1.4577244189370733}\nD: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_177_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_177_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -75.97132980340905, \"translation_dx\": 6.960702322199779, \"translation_dy\": 90.08754109424518, \"scale\": 1.363389071715864}\nB: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}\nC: {\"rotation_angle\": 106.62912259997893, \"translation_dx\": -62.19399566166837, \"translation_dy\": -63.078041204745844, \"scale\": 1.4577244189370733}\nD: {\"rotation_angle\": 26.06413776863195, \"translation_dx\": 104.54441011530889, \"translation_dy\": -2.802993361858995, \"scale\": 0.6919535578881184}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -149.42147215379055, \"translation_dx\": 2.3444194857030283, \"translation_dy\": 35.92779325530762, \"scale\": 1.0223945055206394}\nB: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}\nC: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}\nD: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_178_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_178_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -149.42147215379055, \"translation_dx\": 2.3444194857030283, \"translation_dy\": 35.92779325530762, \"scale\": 1.0223945055206394}\nB: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}\nC: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}\nD: {\"rotation_angle\": 14.369437993555863, \"translation_dx\": -23.54312301695805, \"translation_dy\": 55.41046511147678, \"scale\": 1.115345902394854}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nB: {\"rotation_angle\": -138.01409324857718, \"translation_dx\": -15.316687484355015, \"translation_dy\": 65.85955726482798, \"scale\": 0.7544815678306976}\nC: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nD: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_179_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_179_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 104.66960596229086, \"translation_dx\": 122.9579606372167, \"translation_dy\": -32.21502556645471, \"scale\": 0.5791563638149022}\nB: {\"rotation_angle\": -138.01409324857718, \"translation_dx\": -15.316687484355015, \"translation_dy\": 65.85955726482798, \"scale\": 0.7544815678306976}\nC: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nD: {\"rotation_angle\": -106.99875725121946, \"translation_dx\": 87.96881157950656, \"translation_dy\": -34.70529343588741, \"scale\": 1.407305489874207}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -103.5561502427767, \"translation_dx\": -75.76940431238745, \"translation_dy\": -48.3479107136017, \"scale\": 1.0522987713432983}\nB: {\"rotation_angle\": 136.76946369368522, \"translation_dx\": 86.13615517916296, \"translation_dy\": 47.49597577737802, \"scale\": 1.1842967613683704}\nC: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nD: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_180_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_180_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -103.5561502427767, \"translation_dx\": -75.76940431238745, \"translation_dy\": -48.3479107136017, \"scale\": 1.0522987713432983}\nB: {\"rotation_angle\": 136.76946369368522, \"translation_dx\": 86.13615517916296, \"translation_dy\": 47.49597577737802, \"scale\": 1.1842967613683704}\nC: {\"rotation_angle\": 159.74516071456964, \"translation_dx\": 18.36539372865252, \"translation_dy\": -32.68583255299669, \"scale\": 0.6283421405871866}\nD: {\"rotation_angle\": 84.88997243843744, \"translation_dx\": 19.30269357274682, \"translation_dy\": 9.929350250110147, \"scale\": 1.0595552381550672}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -8.756342422911757, \"translation_dx\": -120.12147874311805, \"translation_dy\": -16.659510954699698, \"scale\": 0.8471832394055047}\nB: {\"rotation_angle\": 134.22497079750707, \"translation_dx\": -56.33244292094708, \"translation_dy\": 12.15417280277697, \"scale\": 1.260404381889235}\nC: {\"rotation_angle\": -173.49565975712173, \"translation_dx\": 30.5303454517925, \"translation_dy\": 77.86216107455405, \"scale\": 1.067173806992701}\nD: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_181_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_181_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -8.756342422911757, \"translation_dx\": -120.12147874311805, \"translation_dy\": -16.659510954699698, \"scale\": 0.8471832394055047}\nB: {\"rotation_angle\": 134.22497079750707, \"translation_dx\": -56.33244292094708, \"translation_dy\": 12.15417280277697, \"scale\": 1.260404381889235}\nC: {\"rotation_angle\": -173.49565975712173, \"translation_dx\": 30.5303454517925, \"translation_dy\": 77.86216107455405, \"scale\": 1.067173806992701}\nD: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": -6.38420562293993, \"translation_dx\": -106.80670691302902, \"translation_dy\": -3.5935098985529663, \"scale\": 1.3037846299861797}\nC: {\"rotation_angle\": 134.66606893121838, \"translation_dx\": 30.71289427748178, \"translation_dy\": 31.00111281943242, \"scale\": 0.9716368665085688}\nD: {\"rotation_angle\": 171.23105805984426, \"translation_dx\": 28.800906238980815, \"translation_dy\": 60.921924115709544, \"scale\": 1.4441070487112413}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_182_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_182_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -169.57691070181107, \"translation_dx\": 67.3776951722352, \"translation_dy\": 6.393739311338578, \"scale\": 0.8283042543093307}\nB: {\"rotation_angle\": -6.38420562293993, \"translation_dx\": -106.80670691302902, \"translation_dy\": -3.5935098985529663, \"scale\": 1.3037846299861797}\nC: {\"rotation_angle\": 134.66606893121838, \"translation_dx\": 30.71289427748178, \"translation_dy\": 31.00111281943242, \"scale\": 0.9716368665085688}\nD: {\"rotation_angle\": 171.23105805984426, \"translation_dx\": 28.800906238980815, \"translation_dy\": 60.921924115709544, \"scale\": 1.4441070487112413}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 18.52926347539298, \"translation_dx\": -26.155433185237058, \"translation_dy\": -39.799299198218556, \"scale\": 0.9355127285855813}\nB: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}\nC: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}\nD: {\"rotation_angle\": -75.97132980340905, \"translation_dx\": 6.960702322199779, \"translation_dy\": 90.08754109424518, \"scale\": 1.363389071715864}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_183_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_183_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 18.52926347539298, \"translation_dx\": -26.155433185237058, \"translation_dy\": -39.799299198218556, \"scale\": 0.9355127285855813}\nB: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}\nC: {\"rotation_angle\": 72.25092677282458, \"translation_dx\": 61.389740502873025, \"translation_dy\": -36.86538640455047, \"scale\": 1.0748600769835353}\nD: {\"rotation_angle\": -75.97132980340905, \"translation_dx\": 6.960702322199779, \"translation_dy\": 90.08754109424518, \"scale\": 1.363389071715864}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -75.97132980340905, \"translation_dx\": 6.960702322199779, \"translation_dy\": 90.08754109424518, \"scale\": 1.363389071715864}\nB: {\"rotation_angle\": 44.2601421515034, \"translation_dx\": -84.9832744911761, \"translation_dy\": -78.07982572554322, \"scale\": 0.5612120736859965}\nC: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nD: {\"rotation_angle\": -101.64893396855386, \"translation_dx\": -96.08306753711838, \"translation_dy\": 14.852477797043775, \"scale\": 1.3017377870800058}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_184_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_184_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -75.97132980340905, \"translation_dx\": 6.960702322199779, \"translation_dy\": 90.08754109424518, \"scale\": 1.363389071715864}\nB: {\"rotation_angle\": 44.2601421515034, \"translation_dx\": -84.9832744911761, \"translation_dy\": -78.07982572554322, \"scale\": 0.5612120736859965}\nC: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nD: {\"rotation_angle\": -101.64893396855386, \"translation_dx\": -96.08306753711838, \"translation_dy\": 14.852477797043775, \"scale\": 1.3017377870800058}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -178.96154331790243, \"translation_dx\": -45.831117140591004, \"translation_dy\": 14.962223802901406, \"scale\": 1.4059876442036168}\nB: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}\nC: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nD: {\"rotation_angle\": -149.34069149386406, \"translation_dx\": 81.63420911320063, \"translation_dy\": -26.073567429384056, \"scale\": 1.427947630130646}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_185_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_185_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -178.96154331790243, \"translation_dx\": -45.831117140591004, \"translation_dy\": 14.962223802901406, \"scale\": 1.4059876442036168}\nB: {\"rotation_angle\": 115.4472434811122, \"translation_dx\": 69.00896887231048, \"translation_dy\": -26.016218629159226, \"scale\": 0.9339901852292719}\nC: {\"rotation_angle\": 2.6800660606496933, \"translation_dx\": 8.805898944242955, \"translation_dy\": -61.557448223727356, \"scale\": 0.7338009245004858}\nD: {\"rotation_angle\": -149.34069149386406, \"translation_dx\": 81.63420911320063, \"translation_dy\": -26.073567429384056, \"scale\": 1.427947630130646}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -61.308258156024195, \"translation_dx\": -92.42627707406731, \"translation_dy\": -21.076199203141364, \"scale\": 1.1133621977071444}\nB: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nC: {\"rotation_angle\": -0.45613579718829556, \"translation_dx\": 98.71619714866841, \"translation_dy\": 70.1100439641223, \"scale\": 0.6491919010173006}\nD: {\"rotation_angle\": -124.27587082376021, \"translation_dx\": -88.19288051455345, \"translation_dy\": 24.145134775980125, \"scale\": 1.4414104211047083}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_186_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_186_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -61.308258156024195, \"translation_dx\": -92.42627707406731, \"translation_dy\": -21.076199203141364, \"scale\": 1.1133621977071444}\nB: {\"rotation_angle\": 83.49682873903629, \"translation_dx\": -127.2042493945246, \"translation_dy\": 2.6616959584396938, \"scale\": 0.9488759478249397}\nC: {\"rotation_angle\": -0.45613579718829556, \"translation_dx\": 98.71619714866841, \"translation_dy\": 70.1100439641223, \"scale\": 0.6491919010173006}\nD: {\"rotation_angle\": -124.27587082376021, \"translation_dx\": -88.19288051455345, \"translation_dy\": 24.145134775980125, \"scale\": 1.4414104211047083}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -163.07830945514343, \"translation_dx\": 107.25371607945826, \"translation_dy\": 44.19319462200147, \"scale\": 1.0330497674624493}\nB: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}\nC: {\"rotation_angle\": -148.06770236959966, \"translation_dx\": 76.71938731609727, \"translation_dy\": 125.67697929104389, \"scale\": 1.1600663307259453}\nD: {\"rotation_angle\": -98.17490649350026, \"translation_dx\": 5.744855173473269, \"translation_dy\": -10.705504600001973, \"scale\": 1.1182428392253487}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_187_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_187_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -163.07830945514343, \"translation_dx\": 107.25371607945826, \"translation_dy\": 44.19319462200147, \"scale\": 1.0330497674624493}\nB: {\"rotation_angle\": 115.16030768984217, \"translation_dx\": -1.9669547188467504, \"translation_dy\": 38.42152609256746, \"scale\": 1.3403221872922475}\nC: {\"rotation_angle\": -148.06770236959966, \"translation_dx\": 76.71938731609727, \"translation_dy\": 125.67697929104389, \"scale\": 1.1600663307259453}\nD: {\"rotation_angle\": -98.17490649350026, \"translation_dx\": 5.744855173473269, \"translation_dy\": -10.705504600001973, \"scale\": 1.1182428392253487}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nB: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}\nC: {\"rotation_angle\": 44.2601421515034, \"translation_dx\": -84.9832744911761, \"translation_dy\": -78.07982572554322, \"scale\": 0.5612120736859965}\nD: {\"rotation_angle\": 127.1396993936072, \"translation_dx\": -29.08894824101361, \"translation_dy\": -80.84475014775404, \"scale\": 1.2834497894588772}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_188_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_188_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 143.38145335973087, \"translation_dx\": 86.67970142496799, \"translation_dy\": -33.57640317277091, \"scale\": 0.6114655384261714}\nB: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}\nC: {\"rotation_angle\": 44.2601421515034, \"translation_dx\": -84.9832744911761, \"translation_dy\": -78.07982572554322, \"scale\": 0.5612120736859965}\nD: {\"rotation_angle\": 127.1396993936072, \"translation_dx\": -29.08894824101361, \"translation_dy\": -80.84475014775404, \"scale\": 1.2834497894588772}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -6.258618837806779, \"translation_dx\": -117.56200624611057, \"translation_dy\": -84.92852320396813, \"scale\": 0.8703619649920769}\nB: {\"rotation_angle\": -92.49508697379828, \"translation_dx\": 63.09853740086383, \"translation_dy\": 99.47995409556995, \"scale\": 0.9495145406508286}\nC: {\"rotation_angle\": -46.75272698463425, \"translation_dx\": 16.424107524155175, \"translation_dy\": -60.683488552754085, \"scale\": 1.375025476214386}\nD: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_189_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_189_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -6.258618837806779, \"translation_dx\": -117.56200624611057, \"translation_dy\": -84.92852320396813, \"scale\": 0.8703619649920769}\nB: {\"rotation_angle\": -92.49508697379828, \"translation_dx\": 63.09853740086383, \"translation_dy\": 99.47995409556995, \"scale\": 0.9495145406508286}\nC: {\"rotation_angle\": -46.75272698463425, \"translation_dx\": 16.424107524155175, \"translation_dy\": -60.683488552754085, \"scale\": 1.375025476214386}\nD: {\"rotation_angle\": 99.38174871704592, \"translation_dx\": 57.870588734166205, \"translation_dy\": 17.413162007690403, \"scale\": 1.4113398114931053}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -42.98651909317854, \"translation_dx\": 114.49293313374625, \"translation_dy\": -39.53290228333596, \"scale\": 1.442019387031135}\nB: {\"rotation_angle\": 49.896013394485834, \"translation_dx\": -25.763756683237403, \"translation_dy\": -26.432232271484168, \"scale\": 1.1619310734744932}\nC: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}\nD: {\"rotation_angle\": -79.27003163090343, \"translation_dx\": 8.207736130313549, \"translation_dy\": 6.670417118750038, \"scale\": 1.3327657238113826}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_190_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_190_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -42.98651909317854, \"translation_dx\": 114.49293313374625, \"translation_dy\": -39.53290228333596, \"scale\": 1.442019387031135}\nB: {\"rotation_angle\": 49.896013394485834, \"translation_dx\": -25.763756683237403, \"translation_dy\": -26.432232271484168, \"scale\": 1.1619310734744932}\nC: {\"rotation_angle\": 97.63348280388993, \"translation_dx\": 59.62332527691919, \"translation_dy\": 12.549462794922746, \"scale\": 0.6927080624806098}\nD: {\"rotation_angle\": -79.27003163090343, \"translation_dx\": 8.207736130313549, \"translation_dy\": 6.670417118750038, \"scale\": 1.3327657238113826}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nB: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}\nC: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nD: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_191_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_191_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nB: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}\nC: {\"rotation_angle\": 123.61853421760617, \"translation_dx\": -93.63136806510369, \"translation_dy\": -15.65687765252683, \"scale\": 0.9834422929774667}\nD: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}\nB: {\"rotation_angle\": -99.80397961792426, \"translation_dx\": 113.2252387398062, \"translation_dy\": -61.846052830557056, \"scale\": 1.080357872583317}\nC: {\"rotation_angle\": -84.90425841207441, \"translation_dx\": -96.22975116611923, \"translation_dy\": -54.13037688992304, \"scale\": 1.161476925450186}\nD: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_192_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_192_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -78.36766094840773, \"translation_dx\": -86.41466180609471, \"translation_dy\": 63.19530077419013, \"scale\": 0.608403973907593}\nB: {\"rotation_angle\": -99.80397961792426, \"translation_dx\": 113.2252387398062, \"translation_dy\": -61.846052830557056, \"scale\": 1.080357872583317}\nC: {\"rotation_angle\": -84.90425841207441, \"translation_dx\": -96.22975116611923, \"translation_dy\": -54.13037688992304, \"scale\": 1.161476925450186}\nD: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 99.4759866737457, \"translation_dx\": -117.67383777244245, \"translation_dy\": -44.645046657688624, \"scale\": 1.4332006009229632}\nB: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nC: {\"rotation_angle\": 12.872370969250312, \"translation_dx\": -43.1533458138392, \"translation_dy\": -64.88511529320917, \"scale\": 1.3092068537816153}\nD: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_193_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_193_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 99.4759866737457, \"translation_dx\": -117.67383777244245, \"translation_dy\": -44.645046657688624, \"scale\": 1.4332006009229632}\nB: {\"rotation_angle\": -162.31682909306286, \"translation_dx\": 94.60975693720637, \"translation_dy\": -28.569332128995313, \"scale\": 1.1251281587345527}\nC: {\"rotation_angle\": 12.872370969250312, \"translation_dx\": -43.1533458138392, \"translation_dy\": -64.88511529320917, \"scale\": 1.3092068537816153}\nD: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}"}, "output": {"output_text": "C"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 137.29869982747988, \"translation_dx\": 75.41375097241084, \"translation_dy\": 55.66358575693553, \"scale\": 1.1335508281242805}\nB: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}\nC: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}\nD: {\"rotation_angle\": -6.970858631484532, \"translation_dx\": -2.793256631611797, \"translation_dy\": 83.08133552847667, \"scale\": 1.4237697720578382}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_194_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_194_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 137.29869982747988, \"translation_dx\": 75.41375097241084, \"translation_dy\": 55.66358575693553, \"scale\": 1.1335508281242805}\nB: {\"rotation_angle\": 83.8873422171626, \"translation_dx\": -89.51171417178318, \"translation_dy\": 44.525876215713694, \"scale\": 0.7096671999666376}\nC: {\"rotation_angle\": -13.219279868292688, \"translation_dx\": -95.87022677446828, \"translation_dy\": -58.31347876468597, \"scale\": 1.3722022398508045}\nD: {\"rotation_angle\": -6.970858631484532, \"translation_dx\": -2.793256631611797, \"translation_dy\": 83.08133552847667, \"scale\": 1.4237697720578382}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nB: {\"rotation_angle\": -44.30781692045639, \"translation_dx\": -23.473696812537305, \"translation_dy\": -94.42952089946652, \"scale\": 1.4029179362735564}\nC: {\"rotation_angle\": -92.49508697379828, \"translation_dx\": 63.09853740086383, \"translation_dy\": 99.47995409556995, \"scale\": 0.9495145406508286}\nD: {\"rotation_angle\": -61.308258156024195, \"translation_dx\": -92.42627707406731, \"translation_dy\": -21.076199203141364, \"scale\": 1.1133621977071444}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_195_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_195_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -100.94596249363259, \"translation_dx\": 18.493532966543597, \"translation_dy\": -4.904135882610319, \"scale\": 1.1575890826518318}\nB: {\"rotation_angle\": -44.30781692045639, \"translation_dx\": -23.473696812537305, \"translation_dy\": -94.42952089946652, \"scale\": 1.4029179362735564}\nC: {\"rotation_angle\": -92.49508697379828, \"translation_dx\": 63.09853740086383, \"translation_dy\": 99.47995409556995, \"scale\": 0.9495145406508286}\nD: {\"rotation_angle\": -61.308258156024195, \"translation_dx\": -92.42627707406731, \"translation_dy\": -21.076199203141364, \"scale\": 1.1133621977071444}"}, "output": {"output_text": "B"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}\nB: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nC: {\"rotation_angle\": 48.71833122181758, \"translation_dx\": -105.22683210092106, \"translation_dy\": -63.34096559919908, \"scale\": 0.7204478932238769}\nD: {\"rotation_angle\": 88.199522854527, \"translation_dx\": 18.814421533590917, \"translation_dy\": -27.135307313502466, \"scale\": 1.37855935527965}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_196_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_196_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}\nB: {\"rotation_angle\": 111.11665430921613, \"translation_dx\": -45.526232266105865, \"translation_dy\": -71.56835409165808, \"scale\": 0.5234271564227445}\nC: {\"rotation_angle\": 48.71833122181758, \"translation_dx\": -105.22683210092106, \"translation_dy\": -63.34096559919908, \"scale\": 0.7204478932238769}\nD: {\"rotation_angle\": 88.199522854527, \"translation_dx\": 18.814421533590917, \"translation_dy\": -27.135307313502466, \"scale\": 1.37855935527965}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}\nB: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nC: {\"rotation_angle\": 179.8013352752547, \"translation_dx\": -90.5548533247824, \"translation_dy\": 17.23782922418306, \"scale\": 0.9885365626195518}\nD: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_197_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_197_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": 142.66976946716716, \"translation_dx\": 29.963541003119957, \"translation_dy\": 66.07065092305665, \"scale\": 1.42144068359999}\nB: {\"rotation_angle\": 53.86809011441332, \"translation_dx\": -15.131168518097624, \"translation_dy\": -31.300037391593577, \"scale\": 1.3154620606808156}\nC: {\"rotation_angle\": 179.8013352752547, \"translation_dx\": -90.5548533247824, \"translation_dy\": 17.23782922418306, \"scale\": 0.9885365626195518}\nD: {\"rotation_angle\": 148.22875373623708, \"translation_dx\": 53.75338658972072, \"translation_dy\": -63.78583022927253, \"scale\": 0.9304836306567924}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}\nB: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nC: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}\nD: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_198_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_198_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -23.247975965134003, \"translation_dx\": 108.97564353658032, \"translation_dy\": 27.267413374938258, \"scale\": 1.2292170424899498}\nB: {\"rotation_angle\": 46.42160956908356, \"translation_dx\": -90.04619228512212, \"translation_dy\": -15.749486436572411, \"scale\": 1.005156310055277}\nC: {\"rotation_angle\": 26.051749493295517, \"translation_dx\": 8.674153667650117, \"translation_dy\": 81.98381249796742, \"scale\": 1.4721363798843865}\nD: {\"rotation_angle\": -160.6395227566207, \"translation_dx\": 53.66643366551958, \"translation_dy\": -27.712376159428388, \"scale\": 1.1084051689599654}"}, "output": {"output_text": "D"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "COCO_spatial", "options": "A: {\"rotation_angle\": -70.97525301082955, \"translation_dx\": -28.380848037876873, \"translation_dy\": 54.37723426674512, \"scale\": 0.9024922197892329}\nB: {\"rotation_angle\": -5.816806483512181, \"translation_dx\": -70.40329792935935, \"translation_dy\": -21.418007440252175, \"scale\": 1.0041476956174793}\nC: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}\nD: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}", "visual_input_component": "natural image", "input": {"input_image_path": ["2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_199_0.jpg", "2D-spatial/Image_Spatial_Transformation_Estimation/Image_Spatial_Transformation_Estimation_199_1.jpg"], "question": "Please compute the type and parameters of the spatial transformation between these two images.", "context": "Given pairs of images depicting scenes before and after a spatial transformation (e.g., rotation, translation), your task is to predict the type and magnitude of the transformation that occurred. \nSelect from the following choices.\nA: {\"rotation_angle\": -70.97525301082955, \"translation_dx\": -28.380848037876873, \"translation_dy\": 54.37723426674512, \"scale\": 0.9024922197892329}\nB: {\"rotation_angle\": -5.816806483512181, \"translation_dx\": -70.40329792935935, \"translation_dy\": -21.418007440252175, \"scale\": 1.0041476956174793}\nC: {\"rotation_angle\": 163.34031080178892, \"translation_dx\": -21.567151354845635, \"translation_dy\": -30.72615389540148, \"scale\": 1.2439888416024685}\nD: {\"rotation_angle\": -14.958482221349612, \"translation_dx\": 49.62118662103501, \"translation_dy\": -13.943537967490855, \"scale\": 1.489574484959727}"}, "output": {"output_text": "A"}, "task": "Image_Spatial_Transformation_Estimation"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 1, 4, 2]\nB: [2, 3, 4, 1]\nC: [2, 1, 3, 4]\nD: [4, 2, 1, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_0_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_0_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_0_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_0_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_0_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 4, 2]\nB: [2, 3, 4, 1]\nC: [2, 1, 3, 4]\nD: [4, 2, 1, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 3, 4, 1]\nB: [3, 1, 2, 4]\nC: [4, 3, 1, 2]\nD: [2, 4, 3, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_1_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_1_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_1_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_1_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_1_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [3, 1, 2, 4]\nC: [4, 3, 1, 2]\nD: [2, 4, 3, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 1, 2]\nB: [3, 4, 2, 1]\nC: [4, 3, 2, 1]\nD: [1, 2, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_2_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_2_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_2_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_2_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_2_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [3, 4, 2, 1]\nC: [4, 3, 2, 1]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 1, 2]\nB: [4, 1, 3, 2]\nC: [1, 3, 4, 2]\nD: [3, 2, 4, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_3_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_3_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_3_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_3_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_3_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [4, 1, 3, 2]\nC: [1, 3, 4, 2]\nD: [3, 2, 4, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [3, 1, 2, 4]\nC: [4, 3, 1, 2]\nD: [1, 4, 3, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_4_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_4_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_4_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_4_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_4_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [3, 1, 2, 4]\nC: [4, 3, 1, 2]\nD: [1, 4, 3, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 2, 4, 1]\nB: [3, 4, 2, 1]\nC: [1, 3, 4, 2]\nD: [3, 1, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_5_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_5_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_5_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_5_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_5_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 4, 1]\nB: [3, 4, 2, 1]\nC: [1, 3, 4, 2]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 2, 4, 1]\nB: [3, 4, 1, 2]\nC: [2, 1, 3, 4]\nD: [4, 2, 3, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_6_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_6_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_6_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_6_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_6_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 4, 1]\nB: [3, 4, 1, 2]\nC: [2, 1, 3, 4]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 3, 2]\nB: [4, 1, 2, 3]\nC: [2, 3, 4, 1]\nD: [2, 4, 3, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_7_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_7_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_7_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_7_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_7_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [4, 1, 2, 3]\nC: [2, 3, 4, 1]\nD: [2, 4, 3, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 3, 1]\nB: [2, 3, 4, 1]\nC: [1, 3, 4, 2]\nD: [4, 3, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_8_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_8_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_8_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_8_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_8_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [2, 3, 4, 1]\nC: [1, 3, 4, 2]\nD: [4, 3, 2, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 2, 3]\nB: [1, 3, 2, 4]\nC: [3, 1, 2, 4]\nD: [3, 4, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_9_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_9_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_9_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_9_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_9_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [1, 3, 2, 4]\nC: [3, 1, 2, 4]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 2, 4, 1]\nB: [2, 1, 3, 4]\nC: [1, 3, 2, 4]\nD: [2, 3, 1, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_10_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_10_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_10_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_10_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_10_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 4, 1]\nB: [2, 1, 3, 4]\nC: [1, 3, 2, 4]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 3, 2]\nB: [3, 4, 2, 1]\nC: [4, 1, 3, 2]\nD: [2, 4, 1, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_11_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_11_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_11_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_11_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_11_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [3, 4, 2, 1]\nC: [4, 1, 3, 2]\nD: [2, 4, 1, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 1, 2]\nB: [2, 1, 3, 4]\nC: [1, 2, 4, 3]\nD: [1, 3, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_12_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_12_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_12_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_12_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_12_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [2, 1, 3, 4]\nC: [1, 2, 4, 3]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 3, 2]\nB: [4, 3, 1, 2]\nC: [3, 2, 1, 4]\nD: [3, 1, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_13_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_13_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_13_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_13_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_13_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [4, 3, 1, 2]\nC: [3, 2, 1, 4]\nD: [3, 1, 2, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 2, 1]\nB: [3, 4, 1, 2]\nC: [1, 4, 2, 3]\nD: [4, 3, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_14_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_14_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_14_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_14_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_14_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [3, 4, 1, 2]\nC: [1, 4, 2, 3]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 1, 4, 2]\nB: [4, 2, 3, 1]\nC: [1, 4, 2, 3]\nD: [3, 4, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_15_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_15_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_15_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_15_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_15_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 4, 2]\nB: [4, 2, 3, 1]\nC: [1, 4, 2, 3]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 2, 1]\nB: [4, 2, 3, 1]\nC: [1, 3, 2, 4]\nD: [3, 2, 1, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_16_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_16_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_16_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_16_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_16_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [4, 2, 3, 1]\nC: [1, 3, 2, 4]\nD: [3, 2, 1, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [4, 2, 1, 3]\nC: [4, 2, 3, 1]\nD: [3, 4, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_17_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_17_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_17_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_17_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_17_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [4, 2, 1, 3]\nC: [4, 2, 3, 1]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 3, 1]\nB: [3, 1, 2, 4]\nC: [1, 3, 2, 4]\nD: [4, 2, 1, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_18_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_18_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_18_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_18_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_18_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [3, 1, 2, 4]\nC: [1, 3, 2, 4]\nD: [4, 2, 1, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 1, 4, 2]\nB: [3, 2, 1, 4]\nC: [2, 4, 3, 1]\nD: [1, 2, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_19_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_19_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_19_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_19_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_19_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 4, 2]\nB: [3, 2, 1, 4]\nC: [2, 4, 3, 1]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [4, 1, 2, 3]\nC: [4, 3, 1, 2]\nD: [1, 2, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_20_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_20_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_20_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_20_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_20_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [4, 1, 2, 3]\nC: [4, 3, 1, 2]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 3, 1]\nB: [3, 2, 1, 4]\nC: [3, 2, 4, 1]\nD: [1, 2, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_21_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_21_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_21_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_21_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_21_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [3, 2, 1, 4]\nC: [3, 2, 4, 1]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [2, 3, 4, 1]\nC: [1, 4, 2, 3]\nD: [2, 1, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_22_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_22_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_22_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_22_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_22_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [2, 3, 4, 1]\nC: [1, 4, 2, 3]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 1, 3]\nB: [4, 2, 1, 3]\nC: [2, 1, 4, 3]\nD: [1, 3, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_23_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_23_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_23_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_23_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_23_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 1, 3]\nB: [4, 2, 1, 3]\nC: [2, 1, 4, 3]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 1, 2, 4]\nB: [1, 3, 2, 4]\nC: [2, 1, 4, 3]\nD: [4, 2, 3, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_24_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_24_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_24_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_24_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_24_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 2, 4]\nB: [1, 3, 2, 4]\nC: [2, 1, 4, 3]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 2, 3, 1]\nB: [1, 4, 3, 2]\nC: [4, 2, 1, 3]\nD: [2, 3, 1, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_25_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_25_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_25_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_25_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_25_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [1, 4, 3, 2]\nC: [4, 2, 1, 3]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 1, 2]\nB: [2, 4, 3, 1]\nC: [4, 3, 1, 2]\nD: [3, 1, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_26_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_26_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_26_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_26_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_26_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [2, 4, 3, 1]\nC: [4, 3, 1, 2]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 3, 2, 4]\nB: [2, 4, 3, 1]\nC: [1, 4, 2, 3]\nD: [1, 2, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_27_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_27_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_27_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_27_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_27_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 2, 4]\nB: [2, 4, 3, 1]\nC: [1, 4, 2, 3]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 3, 2]\nB: [4, 1, 3, 2]\nC: [2, 1, 3, 4]\nD: [4, 3, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_28_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_28_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_28_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_28_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_28_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [4, 1, 3, 2]\nC: [2, 1, 3, 4]\nD: [4, 3, 2, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 2, 1, 4]\nB: [3, 1, 2, 4]\nC: [4, 1, 2, 3]\nD: [1, 4, 3, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_29_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_29_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_29_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_29_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_29_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 1, 4]\nB: [3, 1, 2, 4]\nC: [4, 1, 2, 3]\nD: [1, 4, 3, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 2, 4, 3]\nB: [3, 2, 1, 4]\nC: [1, 3, 2, 4]\nD: [4, 3, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_30_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_30_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_30_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_30_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_30_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 4, 3]\nB: [3, 2, 1, 4]\nC: [1, 3, 2, 4]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [1, 4, 3, 2]\nC: [1, 3, 4, 2]\nD: [1, 2, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_31_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_31_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_31_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_31_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_31_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [1, 4, 3, 2]\nC: [1, 3, 4, 2]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 3, 1, 4]\nB: [4, 3, 1, 2]\nC: [2, 3, 4, 1]\nD: [2, 1, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_32_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_32_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_32_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_32_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_32_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 1, 4]\nB: [4, 3, 1, 2]\nC: [2, 3, 4, 1]\nD: [2, 1, 4, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [3, 2, 4, 1]\nC: [3, 4, 1, 2]\nD: [1, 2, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_33_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_33_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_33_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_33_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_33_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [3, 2, 4, 1]\nC: [3, 4, 1, 2]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 2, 4, 1]\nB: [3, 1, 2, 4]\nC: [2, 3, 4, 1]\nD: [1, 3, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_34_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_34_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_34_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_34_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_34_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 4, 1]\nB: [3, 1, 2, 4]\nC: [2, 3, 4, 1]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 2, 1]\nB: [1, 4, 2, 3]\nC: [2, 1, 3, 4]\nD: [2, 3, 1, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_35_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_35_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_35_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_35_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_35_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [1, 4, 2, 3]\nC: [2, 1, 3, 4]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 2, 1]\nB: [4, 1, 2, 3]\nC: [3, 2, 1, 4]\nD: [1, 3, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_36_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_36_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_36_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_36_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_36_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [4, 1, 2, 3]\nC: [3, 2, 1, 4]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 3, 4, 1]\nB: [4, 1, 2, 3]\nC: [3, 4, 2, 1]\nD: [3, 4, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_37_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_37_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_37_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_37_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_37_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [4, 1, 2, 3]\nC: [3, 4, 2, 1]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 1, 2]\nB: [4, 2, 1, 3]\nC: [1, 3, 4, 2]\nD: [3, 1, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_38_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_38_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_38_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_38_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_38_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [4, 2, 1, 3]\nC: [1, 3, 4, 2]\nD: [3, 1, 2, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 3, 2]\nB: [1, 3, 4, 2]\nC: [2, 3, 4, 1]\nD: [3, 1, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_39_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_39_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_39_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_39_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_39_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [1, 3, 4, 2]\nC: [2, 3, 4, 1]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [3, 4, 1, 2]\nC: [3, 2, 4, 1]\nD: [1, 2, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_40_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_40_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_40_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_40_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_40_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [3, 4, 1, 2]\nC: [3, 2, 4, 1]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 1, 2]\nB: [1, 2, 3, 4]\nC: [2, 4, 3, 1]\nD: [2, 3, 1, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_41_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_41_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_41_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_41_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_41_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [1, 2, 3, 4]\nC: [2, 4, 3, 1]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 2, 1]\nB: [1, 2, 4, 3]\nC: [2, 1, 3, 4]\nD: [4, 2, 3, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_42_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_42_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_42_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_42_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_42_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [1, 2, 4, 3]\nC: [2, 1, 3, 4]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 2, 1, 3]\nB: [3, 1, 4, 2]\nC: [1, 2, 3, 4]\nD: [3, 4, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_43_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_43_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_43_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_43_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_43_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 1, 3]\nB: [3, 1, 4, 2]\nC: [1, 2, 3, 4]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 3, 4, 2]\nB: [1, 4, 3, 2]\nC: [2, 3, 4, 1]\nD: [2, 1, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_44_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_44_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_44_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_44_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_44_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 4, 2]\nB: [1, 4, 3, 2]\nC: [2, 3, 4, 1]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 2, 1, 3]\nB: [2, 3, 4, 1]\nC: [4, 3, 2, 1]\nD: [2, 1, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_45_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_45_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_45_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_45_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_45_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 1, 3]\nB: [2, 3, 4, 1]\nC: [4, 3, 2, 1]\nD: [2, 1, 4, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 2, 3]\nB: [1, 3, 2, 4]\nC: [2, 4, 1, 3]\nD: [2, 1, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_46_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_46_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_46_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_46_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_46_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [1, 3, 2, 4]\nC: [2, 4, 1, 3]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [4, 2, 3, 1]\nC: [1, 3, 2, 4]\nD: [3, 1, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_47_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_47_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_47_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_47_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_47_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [4, 2, 3, 1]\nC: [1, 3, 2, 4]\nD: [3, 1, 2, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 3, 4]\nB: [1, 2, 4, 3]\nC: [3, 1, 2, 4]\nD: [3, 1, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_48_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_48_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_48_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_48_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_48_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 3, 4]\nB: [1, 2, 4, 3]\nC: [3, 1, 2, 4]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [1, 4, 2, 3]\nC: [1, 3, 2, 4]\nD: [1, 2, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_49_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_49_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_49_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_49_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_49_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [1, 4, 2, 3]\nC: [1, 3, 2, 4]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 2, 4, 3]\nB: [3, 1, 2, 4]\nC: [2, 4, 1, 3]\nD: [3, 4, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_50_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_50_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_50_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_50_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_50_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 4, 3]\nB: [3, 1, 2, 4]\nC: [2, 4, 1, 3]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 2, 1]\nB: [1, 4, 2, 3]\nC: [4, 2, 3, 1]\nD: [1, 3, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_51_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_51_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_51_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_51_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_51_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [1, 4, 2, 3]\nC: [4, 2, 3, 1]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 1, 2]\nB: [3, 2, 4, 1]\nC: [1, 3, 4, 2]\nD: [4, 1, 2, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_52_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_52_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_52_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_52_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_52_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [3, 2, 4, 1]\nC: [1, 3, 4, 2]\nD: [4, 1, 2, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 2, 3, 1]\nB: [1, 3, 4, 2]\nC: [4, 3, 2, 1]\nD: [3, 4, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_53_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_53_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_53_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_53_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_53_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [1, 3, 4, 2]\nC: [4, 3, 2, 1]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 2, 3, 4]\nB: [1, 3, 4, 2]\nC: [4, 3, 1, 2]\nD: [4, 3, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_54_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_54_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_54_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_54_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_54_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [1, 3, 4, 2]\nC: [4, 3, 1, 2]\nD: [4, 3, 2, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 2, 4, 3]\nB: [2, 1, 4, 3]\nC: [3, 1, 4, 2]\nD: [4, 2, 3, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_55_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_55_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_55_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_55_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_55_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 4, 3]\nB: [2, 1, 4, 3]\nC: [3, 1, 4, 2]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 3, 2]\nB: [1, 4, 2, 3]\nC: [4, 3, 2, 1]\nD: [1, 3, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_56_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_56_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_56_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_56_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_56_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [1, 4, 2, 3]\nC: [4, 3, 2, 1]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 2, 3]\nB: [3, 2, 1, 4]\nC: [4, 2, 1, 3]\nD: [4, 3, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_57_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_57_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_57_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_57_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_57_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [3, 2, 1, 4]\nC: [4, 2, 1, 3]\nD: [4, 3, 2, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 2, 1]\nB: [2, 1, 3, 4]\nC: [4, 1, 2, 3]\nD: [2, 1, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_58_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_58_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_58_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_58_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_58_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [2, 1, 3, 4]\nC: [4, 1, 2, 3]\nD: [2, 1, 4, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 1, 4, 2]\nB: [2, 3, 4, 1]\nC: [1, 4, 3, 2]\nD: [1, 4, 2, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_59_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_59_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_59_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_59_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_59_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 4, 2]\nB: [2, 3, 4, 1]\nC: [1, 4, 3, 2]\nD: [1, 4, 2, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [3, 1, 4, 2]\nC: [2, 3, 1, 4]\nD: [3, 1, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_60_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_60_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_60_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_60_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_60_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [3, 1, 4, 2]\nC: [2, 3, 1, 4]\nD: [3, 1, 2, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 1, 2]\nB: [4, 3, 1, 2]\nC: [2, 4, 1, 3]\nD: [1, 2, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_61_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_61_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_61_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_61_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_61_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [4, 3, 1, 2]\nC: [2, 4, 1, 3]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 2, 3]\nB: [4, 3, 1, 2]\nC: [3, 1, 4, 2]\nD: [2, 3, 4, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_62_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_62_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_62_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_62_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_62_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [4, 3, 1, 2]\nC: [3, 1, 4, 2]\nD: [2, 3, 4, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 2, 3, 4]\nB: [3, 1, 4, 2]\nC: [4, 3, 2, 1]\nD: [4, 2, 3, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_63_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_63_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_63_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_63_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_63_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [3, 1, 4, 2]\nC: [4, 3, 2, 1]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 3, 4, 2]\nB: [4, 1, 2, 3]\nC: [2, 3, 1, 4]\nD: [3, 4, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_64_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_64_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_64_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_64_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_64_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 4, 2]\nB: [4, 1, 2, 3]\nC: [2, 3, 1, 4]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 2, 1]\nB: [4, 2, 3, 1]\nC: [2, 1, 4, 3]\nD: [3, 2, 4, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_65_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_65_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_65_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_65_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_65_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [4, 2, 3, 1]\nC: [2, 1, 4, 3]\nD: [3, 2, 4, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 1, 2]\nB: [4, 3, 1, 2]\nC: [1, 3, 2, 4]\nD: [1, 3, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_66_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_66_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_66_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_66_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_66_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [4, 3, 1, 2]\nC: [1, 3, 2, 4]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 1, 3]\nB: [4, 3, 1, 2]\nC: [4, 2, 1, 3]\nD: [3, 4, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_67_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_67_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_67_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_67_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_67_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 1, 3]\nB: [4, 3, 1, 2]\nC: [4, 2, 1, 3]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 2, 1]\nB: [1, 3, 2, 4]\nC: [4, 2, 1, 3]\nD: [3, 2, 4, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_68_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_68_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_68_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_68_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_68_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [1, 3, 2, 4]\nC: [4, 2, 1, 3]\nD: [3, 2, 4, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 1, 4, 2]\nB: [4, 2, 1, 3]\nC: [3, 2, 4, 1]\nD: [2, 1, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_69_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_69_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_69_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_69_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_69_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 4, 2]\nB: [4, 2, 1, 3]\nC: [3, 2, 4, 1]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 1, 2]\nB: [1, 4, 2, 3]\nC: [1, 3, 4, 2]\nD: [4, 1, 2, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_70_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_70_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_70_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_70_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_70_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [1, 4, 2, 3]\nC: [1, 3, 4, 2]\nD: [4, 1, 2, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 2, 3, 4]\nB: [2, 4, 1, 3]\nC: [3, 4, 2, 1]\nD: [3, 2, 4, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_71_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_71_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_71_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_71_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_71_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [2, 4, 1, 3]\nC: [3, 4, 2, 1]\nD: [3, 2, 4, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 3, 1]\nB: [4, 2, 3, 1]\nC: [2, 1, 3, 4]\nD: [3, 4, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_72_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_72_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_72_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_72_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_72_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [4, 2, 3, 1]\nC: [2, 1, 3, 4]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 4, 3]\nB: [1, 4, 2, 3]\nC: [3, 1, 4, 2]\nD: [1, 3, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_73_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_73_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_73_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_73_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_73_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [1, 4, 2, 3]\nC: [3, 1, 4, 2]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 2, 3, 4]\nB: [4, 3, 1, 2]\nC: [1, 4, 2, 3]\nD: [4, 3, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_74_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_74_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_74_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_74_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_74_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [4, 3, 1, 2]\nC: [1, 4, 2, 3]\nD: [4, 3, 2, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 3, 2]\nB: [2, 3, 4, 1]\nC: [4, 3, 1, 2]\nD: [2, 1, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_75_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_75_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_75_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_75_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_75_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [2, 3, 4, 1]\nC: [4, 3, 1, 2]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 2, 1]\nB: [4, 2, 1, 3]\nC: [3, 2, 1, 4]\nD: [1, 3, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_76_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_76_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_76_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_76_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_76_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [4, 2, 1, 3]\nC: [3, 2, 1, 4]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 1, 2]\nB: [3, 1, 2, 4]\nC: [4, 1, 2, 3]\nD: [4, 3, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_77_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_77_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_77_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_77_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_77_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [3, 1, 2, 4]\nC: [4, 1, 2, 3]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 2, 3]\nB: [4, 3, 1, 2]\nC: [4, 1, 3, 2]\nD: [2, 3, 4, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_78_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_78_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_78_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_78_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_78_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [4, 3, 1, 2]\nC: [4, 1, 3, 2]\nD: [2, 3, 4, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 2, 3]\nB: [1, 3, 4, 2]\nC: [2, 1, 3, 4]\nD: [1, 4, 2, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_79_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_79_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_79_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_79_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_79_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [1, 3, 4, 2]\nC: [2, 1, 3, 4]\nD: [1, 4, 2, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 2, 3]\nB: [4, 2, 1, 3]\nC: [1, 3, 4, 2]\nD: [2, 3, 1, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_80_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_80_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_80_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_80_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_80_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [4, 2, 1, 3]\nC: [1, 3, 4, 2]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 3, 4, 1]\nB: [2, 3, 1, 4]\nC: [4, 2, 1, 3]\nD: [4, 1, 2, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_81_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_81_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_81_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_81_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_81_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [2, 3, 1, 4]\nC: [4, 2, 1, 3]\nD: [4, 1, 2, 3]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 3, 2]\nB: [2, 1, 4, 3]\nC: [2, 3, 1, 4]\nD: [3, 1, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_82_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_82_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_82_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_82_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_82_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [2, 1, 4, 3]\nC: [2, 3, 1, 4]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 3, 1]\nB: [1, 3, 2, 4]\nC: [4, 2, 1, 3]\nD: [4, 3, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_83_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_83_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_83_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_83_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_83_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [1, 3, 2, 4]\nC: [4, 2, 1, 3]\nD: [4, 3, 2, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 1, 3, 4]\nB: [3, 2, 4, 1]\nC: [1, 3, 4, 2]\nD: [4, 3, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_84_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_84_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_84_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_84_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_84_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 3, 4]\nB: [3, 2, 4, 1]\nC: [1, 3, 4, 2]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 3, 1]\nB: [1, 3, 2, 4]\nC: [3, 4, 1, 2]\nD: [2, 1, 3, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_85_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_85_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_85_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_85_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_85_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [1, 3, 2, 4]\nC: [3, 4, 1, 2]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 1, 3]\nB: [4, 2, 1, 3]\nC: [1, 3, 4, 2]\nD: [3, 2, 1, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_86_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_86_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_86_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_86_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_86_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 1, 3]\nB: [4, 2, 1, 3]\nC: [1, 3, 4, 2]\nD: [3, 2, 1, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 3, 1]\nB: [1, 3, 2, 4]\nC: [1, 4, 3, 2]\nD: [1, 2, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_87_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_87_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_87_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_87_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_87_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [1, 3, 2, 4]\nC: [1, 4, 3, 2]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 2, 3, 1]\nB: [3, 2, 1, 4]\nC: [4, 3, 2, 1]\nD: [1, 4, 3, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_88_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_88_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_88_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_88_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_88_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [3, 2, 1, 4]\nC: [4, 3, 2, 1]\nD: [1, 4, 3, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 4, 2, 1]\nB: [3, 2, 4, 1]\nC: [4, 2, 1, 3]\nD: [4, 1, 3, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_89_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_89_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_89_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_89_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_89_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [3, 2, 4, 1]\nC: [4, 2, 1, 3]\nD: [4, 1, 3, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 3, 1, 4]\nB: [2, 1, 3, 4]\nC: [3, 4, 2, 1]\nD: [3, 4, 1, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_90_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_90_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_90_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_90_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_90_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 1, 4]\nB: [2, 1, 3, 4]\nC: [3, 4, 2, 1]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 2, 1, 3]\nB: [4, 1, 3, 2]\nC: [4, 3, 2, 1]\nD: [2, 1, 4, 3]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_91_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_91_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_91_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_91_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_91_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 1, 3]\nB: [4, 1, 3, 2]\nC: [4, 3, 2, 1]\nD: [2, 1, 4, 3]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 1, 2]\nB: [2, 1, 3, 4]\nC: [4, 1, 2, 3]\nD: [1, 3, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_92_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_92_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_92_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_92_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_92_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [2, 1, 3, 4]\nC: [4, 1, 2, 3]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [1, 4, 3, 2]\nB: [1, 3, 4, 2]\nC: [2, 4, 1, 3]\nD: [4, 2, 3, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_93_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_93_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_93_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_93_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_93_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [1, 3, 4, 2]\nC: [2, 4, 1, 3]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 3, 1, 2]\nB: [1, 4, 3, 2]\nC: [1, 3, 4, 2]\nD: [1, 3, 2, 4]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_94_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_94_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_94_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_94_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_94_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [1, 4, 3, 2]\nC: [1, 3, 4, 2]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 1, 2, 4]\nB: [1, 3, 4, 2]\nC: [2, 4, 3, 1]\nD: [3, 2, 4, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_95_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_95_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_95_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_95_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_95_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 2, 4]\nB: [1, 3, 4, 2]\nC: [2, 4, 3, 1]\nD: [3, 2, 4, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 3, 2]\nB: [1, 2, 3, 4]\nC: [2, 4, 3, 1]\nD: [3, 4, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_96_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_96_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_96_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_96_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_96_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [1, 2, 3, 4]\nC: [2, 4, 3, 1]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [4, 1, 3, 2]\nB: [2, 4, 1, 3]\nC: [3, 2, 4, 1]\nD: [4, 3, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_97_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_97_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_97_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_97_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_97_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [2, 4, 1, 3]\nC: [3, 2, 4, 1]\nD: [4, 3, 2, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [3, 2, 4, 1]\nB: [1, 4, 2, 3]\nC: [3, 1, 2, 4]\nD: [4, 3, 2, 1]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_98_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_98_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_98_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_98_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_98_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 4, 1]\nB: [1, 4, 2, 3]\nC: [3, 1, 2, 4]\nD: [4, 3, 2, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_natural", "options": "A: [2, 4, 3, 1]\nB: [3, 4, 2, 1]\nC: [4, 1, 2, 3]\nD: [1, 3, 4, 2]", "visual_input_component": ["natural_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_99_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_99_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_99_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_99_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_99_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [3, 4, 2, 1]\nC: [4, 1, 2, 3]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 4, 3, 1]\nB: [2, 1, 4, 3]\nC: [3, 2, 1, 4]\nD: [1, 2, 4, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_100_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_100_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_100_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_100_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_100_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [2, 1, 4, 3]\nC: [3, 2, 1, 4]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 3, 2]\nB: [1, 3, 4, 2]\nC: [3, 1, 2, 4]\nD: [2, 4, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_101_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_101_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_101_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_101_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_101_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [1, 3, 4, 2]\nC: [3, 1, 2, 4]\nD: [2, 4, 1, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 2, 3]\nB: [2, 4, 1, 3]\nC: [2, 1, 4, 3]\nD: [3, 4, 2, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_102_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_102_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_102_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_102_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_102_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [2, 4, 1, 3]\nC: [2, 1, 4, 3]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 3, 2, 4]\nB: [1, 3, 4, 2]\nC: [2, 4, 3, 1]\nD: [4, 3, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_103_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_103_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_103_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_103_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_103_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 2, 4]\nB: [1, 3, 4, 2]\nC: [2, 4, 3, 1]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 2, 3]\nB: [3, 4, 1, 2]\nC: [4, 1, 3, 2]\nD: [4, 3, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_104_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_104_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_104_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_104_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_104_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [3, 4, 1, 2]\nC: [4, 1, 3, 2]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 3, 4, 1]\nB: [1, 4, 2, 3]\nC: [2, 3, 1, 4]\nD: [1, 3, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_105_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_105_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_105_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_105_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_105_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [1, 4, 2, 3]\nC: [2, 3, 1, 4]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 3, 4, 1]\nB: [3, 4, 1, 2]\nC: [2, 1, 4, 3]\nD: [2, 3, 1, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_106_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_106_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_106_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_106_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_106_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [3, 4, 1, 2]\nC: [2, 1, 4, 3]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 4, 1, 2]\nB: [1, 4, 2, 3]\nC: [3, 2, 4, 1]\nD: [4, 2, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_107_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_107_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_107_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_107_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_107_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 1, 2]\nB: [1, 4, 2, 3]\nC: [3, 2, 4, 1]\nD: [4, 2, 1, 3]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 1, 2]\nB: [3, 4, 2, 1]\nC: [3, 2, 4, 1]\nD: [4, 2, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_108_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_108_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_108_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_108_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_108_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [3, 4, 2, 1]\nC: [3, 2, 4, 1]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 3, 4, 2]\nB: [3, 1, 2, 4]\nC: [3, 1, 4, 2]\nD: [2, 3, 1, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_109_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_109_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_109_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_109_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_109_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 4, 2]\nB: [3, 1, 2, 4]\nC: [3, 1, 4, 2]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 2, 1]\nB: [3, 1, 2, 4]\nC: [2, 4, 3, 1]\nD: [1, 4, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_110_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_110_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_110_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_110_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_110_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [3, 1, 2, 4]\nC: [2, 4, 3, 1]\nD: [1, 4, 3, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 3, 4]\nB: [1, 4, 3, 2]\nC: [2, 1, 3, 4]\nD: [3, 1, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_111_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_111_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_111_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_111_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_111_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [1, 4, 3, 2]\nC: [2, 1, 3, 4]\nD: [3, 1, 2, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 3, 2, 4]\nB: [4, 2, 1, 3]\nC: [1, 4, 3, 2]\nD: [3, 4, 2, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_112_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_112_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_112_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_112_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_112_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 2, 4]\nB: [4, 2, 1, 3]\nC: [1, 4, 3, 2]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 3, 2, 4]\nB: [1, 4, 3, 2]\nC: [2, 4, 3, 1]\nD: [1, 4, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_113_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_113_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_113_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_113_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_113_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 2, 4]\nB: [1, 4, 3, 2]\nC: [2, 4, 3, 1]\nD: [1, 4, 2, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 3, 2, 4]\nB: [2, 3, 1, 4]\nC: [3, 4, 2, 1]\nD: [3, 1, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_114_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_114_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_114_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_114_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_114_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 2, 4]\nB: [2, 3, 1, 4]\nC: [3, 4, 2, 1]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 2, 1]\nB: [1, 3, 4, 2]\nC: [3, 1, 2, 4]\nD: [4, 3, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_115_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_115_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_115_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_115_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_115_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [1, 3, 4, 2]\nC: [3, 1, 2, 4]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 2, 1, 4]\nB: [3, 1, 4, 2]\nC: [4, 3, 2, 1]\nD: [1, 3, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_116_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_116_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_116_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_116_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_116_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 1, 4]\nB: [3, 1, 4, 2]\nC: [4, 3, 2, 1]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 2, 3]\nB: [1, 2, 4, 3]\nC: [3, 2, 1, 4]\nD: [1, 3, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_117_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_117_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_117_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_117_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_117_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [1, 2, 4, 3]\nC: [3, 2, 1, 4]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 3, 4, 2]\nB: [4, 3, 2, 1]\nC: [3, 4, 2, 1]\nD: [1, 4, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_118_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_118_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_118_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_118_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_118_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 4, 2]\nB: [4, 3, 2, 1]\nC: [3, 4, 2, 1]\nD: [1, 4, 3, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 1, 2]\nB: [2, 3, 4, 1]\nC: [1, 3, 2, 4]\nD: [1, 3, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_119_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_119_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_119_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_119_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_119_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [2, 3, 4, 1]\nC: [1, 3, 2, 4]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 2, 3]\nB: [1, 4, 2, 3]\nC: [3, 1, 2, 4]\nD: [3, 1, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_120_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_120_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_120_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_120_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_120_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [1, 4, 2, 3]\nC: [3, 1, 2, 4]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 3, 4]\nB: [4, 3, 2, 1]\nC: [1, 4, 3, 2]\nD: [2, 3, 1, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_121_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_121_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_121_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_121_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_121_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [4, 3, 2, 1]\nC: [1, 4, 3, 2]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 3, 2]\nB: [1, 2, 3, 4]\nC: [4, 1, 3, 2]\nD: [1, 4, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_122_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_122_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_122_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_122_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_122_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [1, 2, 3, 4]\nC: [4, 1, 3, 2]\nD: [1, 4, 2, 3]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 2, 3]\nB: [1, 2, 3, 4]\nC: [1, 3, 4, 2]\nD: [2, 4, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_123_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_123_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_123_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_123_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_123_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [1, 2, 3, 4]\nC: [1, 3, 4, 2]\nD: [2, 4, 3, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 1, 4, 3]\nB: [2, 4, 3, 1]\nC: [1, 4, 3, 2]\nD: [1, 2, 4, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_124_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_124_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_124_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_124_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_124_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [2, 4, 3, 1]\nC: [1, 4, 3, 2]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 2, 3]\nB: [2, 4, 1, 3]\nC: [1, 2, 4, 3]\nD: [4, 1, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_125_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_125_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_125_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_125_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_125_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [2, 4, 1, 3]\nC: [1, 2, 4, 3]\nD: [4, 1, 3, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 1, 2, 4]\nB: [4, 2, 1, 3]\nC: [3, 4, 1, 2]\nD: [2, 1, 3, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_126_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_126_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_126_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_126_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_126_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 2, 4]\nB: [4, 2, 1, 3]\nC: [3, 4, 1, 2]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 2, 3]\nB: [3, 2, 4, 1]\nC: [2, 4, 1, 3]\nD: [1, 4, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_127_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_127_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_127_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_127_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_127_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [3, 2, 4, 1]\nC: [2, 4, 1, 3]\nD: [1, 4, 3, 2]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 4, 3, 1]\nB: [1, 3, 4, 2]\nC: [3, 1, 2, 4]\nD: [1, 2, 3, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_128_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_128_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_128_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_128_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_128_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [1, 3, 4, 2]\nC: [3, 1, 2, 4]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 3, 2]\nB: [2, 4, 1, 3]\nC: [2, 4, 3, 1]\nD: [4, 3, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_129_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_129_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_129_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_129_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_129_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [2, 4, 1, 3]\nC: [2, 4, 3, 1]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 2, 4, 1]\nB: [2, 4, 3, 1]\nC: [2, 1, 3, 4]\nD: [1, 2, 4, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_130_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_130_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_130_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_130_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_130_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 4, 1]\nB: [2, 4, 3, 1]\nC: [2, 1, 3, 4]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 1, 2, 4]\nB: [4, 1, 3, 2]\nC: [3, 4, 1, 2]\nD: [4, 3, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_131_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_131_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_131_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_131_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_131_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 2, 4]\nB: [4, 1, 3, 2]\nC: [3, 4, 1, 2]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 4, 1, 3]\nB: [4, 3, 2, 1]\nC: [4, 1, 3, 2]\nD: [2, 3, 4, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_132_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_132_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_132_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_132_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_132_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 1, 3]\nB: [4, 3, 2, 1]\nC: [4, 1, 3, 2]\nD: [2, 3, 4, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 2, 3]\nB: [2, 3, 1, 4]\nC: [4, 1, 3, 2]\nD: [1, 3, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_133_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_133_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_133_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_133_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_133_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [2, 3, 1, 4]\nC: [4, 1, 3, 2]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 1, 4, 3]\nB: [2, 4, 3, 1]\nC: [4, 3, 1, 2]\nD: [1, 2, 3, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_134_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_134_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_134_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_134_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_134_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [2, 4, 3, 1]\nC: [4, 3, 1, 2]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 3, 2]\nB: [4, 3, 2, 1]\nC: [4, 1, 2, 3]\nD: [3, 1, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_135_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_135_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_135_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_135_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_135_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [4, 3, 2, 1]\nC: [4, 1, 2, 3]\nD: [3, 1, 2, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 1, 2, 4]\nB: [4, 2, 3, 1]\nC: [1, 2, 4, 3]\nD: [3, 1, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_136_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_136_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_136_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_136_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_136_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 2, 4]\nB: [4, 2, 3, 1]\nC: [1, 2, 4, 3]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 2, 3]\nB: [1, 3, 2, 4]\nC: [2, 4, 1, 3]\nD: [1, 4, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_137_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_137_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_137_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_137_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_137_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [1, 3, 2, 4]\nC: [2, 4, 1, 3]\nD: [1, 4, 3, 2]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 1, 2]\nB: [4, 3, 2, 1]\nC: [4, 2, 3, 1]\nD: [4, 1, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_138_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_138_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_138_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_138_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_138_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [4, 3, 2, 1]\nC: [4, 2, 3, 1]\nD: [4, 1, 2, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 1, 4, 3]\nB: [3, 1, 2, 4]\nC: [4, 2, 3, 1]\nD: [4, 3, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_139_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_139_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_139_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_139_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_139_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [3, 1, 2, 4]\nC: [4, 2, 3, 1]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 3, 2]\nB: [2, 1, 4, 3]\nC: [1, 4, 2, 3]\nD: [2, 3, 4, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_140_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_140_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_140_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_140_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_140_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [2, 1, 4, 3]\nC: [1, 4, 2, 3]\nD: [2, 3, 4, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 1, 2]\nB: [1, 4, 3, 2]\nC: [2, 4, 3, 1]\nD: [3, 2, 1, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_141_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_141_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_141_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_141_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_141_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [1, 4, 3, 2]\nC: [2, 4, 3, 1]\nD: [3, 2, 1, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 4, 1, 3]\nB: [1, 4, 2, 3]\nC: [2, 1, 4, 3]\nD: [4, 1, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_142_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_142_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_142_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_142_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_142_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 1, 3]\nB: [1, 4, 2, 3]\nC: [2, 1, 4, 3]\nD: [4, 1, 3, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 2, 3, 1]\nB: [4, 3, 2, 1]\nC: [3, 1, 2, 4]\nD: [4, 2, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_143_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_143_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_143_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_143_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_143_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [4, 3, 2, 1]\nC: [3, 1, 2, 4]\nD: [4, 2, 1, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 1, 2, 4]\nB: [3, 1, 4, 2]\nC: [3, 2, 1, 4]\nD: [2, 4, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_144_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_144_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_144_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_144_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_144_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 2, 4]\nB: [3, 1, 4, 2]\nC: [3, 2, 1, 4]\nD: [2, 4, 1, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 1, 4, 2]\nB: [2, 4, 1, 3]\nC: [4, 1, 3, 2]\nD: [1, 4, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_145_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_145_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_145_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_145_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_145_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 4, 2]\nB: [2, 4, 1, 3]\nC: [4, 1, 3, 2]\nD: [1, 4, 2, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 1, 2]\nB: [4, 2, 3, 1]\nC: [1, 4, 2, 3]\nD: [2, 4, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_146_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_146_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_146_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_146_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_146_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [4, 2, 3, 1]\nC: [1, 4, 2, 3]\nD: [2, 4, 3, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 3, 1, 4]\nB: [2, 4, 1, 3]\nC: [4, 1, 3, 2]\nD: [1, 2, 3, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_147_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_147_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_147_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_147_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_147_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 1, 4]\nB: [2, 4, 1, 3]\nC: [4, 1, 3, 2]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 1, 4, 3]\nB: [2, 3, 1, 4]\nC: [3, 1, 4, 2]\nD: [2, 4, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_148_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_148_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_148_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_148_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_148_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [2, 3, 1, 4]\nC: [3, 1, 4, 2]\nD: [2, 4, 1, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 4, 3]\nB: [4, 1, 3, 2]\nC: [2, 3, 1, 4]\nD: [4, 2, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_149_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_149_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_149_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_149_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_149_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 4, 3]\nB: [4, 1, 3, 2]\nC: [2, 3, 1, 4]\nD: [4, 2, 1, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 3, 4]\nB: [2, 3, 4, 1]\nC: [4, 1, 3, 2]\nD: [3, 4, 2, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_150_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_150_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_150_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_150_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_150_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [2, 3, 4, 1]\nC: [4, 1, 3, 2]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 2, 4, 1]\nB: [3, 2, 1, 4]\nC: [3, 1, 2, 4]\nD: [4, 2, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_151_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_151_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_151_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_151_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_151_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 4, 1]\nB: [3, 2, 1, 4]\nC: [3, 1, 2, 4]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 1, 2]\nB: [3, 1, 4, 2]\nC: [3, 2, 1, 4]\nD: [4, 1, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_152_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_152_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_152_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_152_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_152_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 1, 2]\nB: [3, 1, 4, 2]\nC: [3, 2, 1, 4]\nD: [4, 1, 3, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 2, 1, 4]\nB: [1, 3, 4, 2]\nC: [2, 3, 1, 4]\nD: [4, 2, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_153_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_153_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_153_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_153_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_153_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 1, 4]\nB: [1, 3, 4, 2]\nC: [2, 3, 1, 4]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 2, 1]\nB: [4, 3, 1, 2]\nC: [4, 1, 2, 3]\nD: [3, 4, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_154_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_154_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_154_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_154_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_154_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [4, 3, 1, 2]\nC: [4, 1, 2, 3]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 2, 1]\nB: [1, 4, 3, 2]\nC: [2, 1, 3, 4]\nD: [1, 3, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_155_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_155_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_155_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_155_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_155_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [1, 4, 3, 2]\nC: [2, 1, 3, 4]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 1, 4, 2]\nB: [3, 2, 4, 1]\nC: [4, 2, 3, 1]\nD: [2, 4, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_156_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_156_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_156_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_156_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_156_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 4, 2]\nB: [3, 2, 4, 1]\nC: [4, 2, 3, 1]\nD: [2, 4, 1, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 2, 3, 1]\nB: [1, 3, 2, 4]\nC: [3, 4, 1, 2]\nD: [2, 3, 4, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_157_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_157_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_157_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_157_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_157_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [1, 3, 2, 4]\nC: [3, 4, 1, 2]\nD: [2, 3, 4, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 3, 4, 1]\nB: [1, 3, 2, 4]\nC: [2, 3, 1, 4]\nD: [3, 4, 2, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_158_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_158_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_158_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_158_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_158_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [1, 3, 2, 4]\nC: [2, 3, 1, 4]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 3, 2, 4]\nB: [2, 1, 3, 4]\nC: [3, 4, 2, 1]\nD: [2, 1, 4, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_159_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_159_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_159_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_159_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_159_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 2, 4]\nB: [2, 1, 3, 4]\nC: [3, 4, 2, 1]\nD: [2, 1, 4, 3]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 4, 2, 1]\nB: [4, 3, 1, 2]\nC: [4, 3, 2, 1]\nD: [2, 1, 3, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_160_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_160_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_160_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_160_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_160_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [4, 3, 1, 2]\nC: [4, 3, 2, 1]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 1, 3, 4]\nB: [4, 2, 1, 3]\nC: [1, 4, 2, 3]\nD: [1, 2, 4, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_161_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_161_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_161_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_161_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_161_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 3, 4]\nB: [4, 2, 1, 3]\nC: [1, 4, 2, 3]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 2, 3]\nB: [4, 2, 1, 3]\nC: [1, 4, 2, 3]\nD: [3, 4, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_162_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_162_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_162_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_162_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_162_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [4, 2, 1, 3]\nC: [1, 4, 2, 3]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 1, 4, 3]\nB: [4, 2, 1, 3]\nC: [1, 2, 3, 4]\nD: [2, 3, 1, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_163_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_163_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_163_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_163_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_163_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 4, 3]\nB: [4, 2, 1, 3]\nC: [1, 2, 3, 4]\nD: [2, 3, 1, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 4, 3, 1]\nB: [3, 1, 4, 2]\nC: [3, 2, 1, 4]\nD: [2, 1, 4, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_164_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_164_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_164_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_164_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_164_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [3, 1, 4, 2]\nC: [3, 2, 1, 4]\nD: [2, 1, 4, 3]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 2, 4, 1]\nB: [2, 4, 3, 1]\nC: [1, 2, 4, 3]\nD: [2, 4, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_165_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_165_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_165_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_165_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_165_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 4, 1]\nB: [2, 4, 3, 1]\nC: [1, 2, 4, 3]\nD: [2, 4, 1, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 4, 3]\nB: [1, 4, 2, 3]\nC: [4, 3, 1, 2]\nD: [3, 1, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_166_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_166_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_166_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_166_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_166_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 4, 3]\nB: [1, 4, 2, 3]\nC: [4, 3, 1, 2]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 4, 1, 3]\nB: [2, 1, 4, 3]\nC: [1, 3, 2, 4]\nD: [3, 1, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_167_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_167_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_167_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_167_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_167_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 1, 3]\nB: [2, 1, 4, 3]\nC: [1, 3, 2, 4]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 4, 3, 1]\nB: [4, 3, 1, 2]\nC: [4, 1, 3, 2]\nD: [3, 1, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_168_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_168_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_168_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_168_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_168_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 4, 3, 1]\nB: [4, 3, 1, 2]\nC: [4, 1, 3, 2]\nD: [3, 1, 2, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 4, 3]\nB: [3, 2, 4, 1]\nC: [4, 1, 2, 3]\nD: [4, 2, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_169_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_169_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_169_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_169_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_169_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 4, 3]\nB: [3, 2, 4, 1]\nC: [4, 1, 2, 3]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 2, 1, 4]\nB: [1, 4, 2, 3]\nC: [1, 4, 3, 2]\nD: [1, 2, 4, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_170_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_170_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_170_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_170_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_170_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 1, 4]\nB: [1, 4, 2, 3]\nC: [1, 4, 3, 2]\nD: [1, 2, 4, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 3, 2]\nB: [3, 1, 2, 4]\nC: [2, 4, 3, 1]\nD: [4, 2, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_171_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_171_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_171_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_171_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_171_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [3, 1, 2, 4]\nC: [2, 4, 3, 1]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 2, 3]\nB: [4, 2, 1, 3]\nC: [3, 1, 4, 2]\nD: [3, 2, 1, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_172_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_172_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_172_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_172_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_172_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [4, 2, 1, 3]\nC: [3, 1, 4, 2]\nD: [3, 2, 1, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 3, 4, 1]\nB: [1, 4, 3, 2]\nC: [4, 2, 3, 1]\nD: [4, 1, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_173_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_173_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_173_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_173_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_173_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [1, 4, 3, 2]\nC: [4, 2, 3, 1]\nD: [4, 1, 3, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 1, 3, 4]\nB: [2, 3, 4, 1]\nC: [3, 1, 4, 2]\nD: [4, 1, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_174_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_174_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_174_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_174_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_174_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 3, 4]\nB: [2, 3, 4, 1]\nC: [3, 1, 4, 2]\nD: [4, 1, 2, 3]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 3, 2]\nB: [1, 4, 2, 3]\nC: [3, 1, 4, 2]\nD: [4, 2, 1, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_175_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_175_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_175_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_175_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_175_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [1, 4, 2, 3]\nC: [3, 1, 4, 2]\nD: [4, 2, 1, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 2, 1, 4]\nB: [3, 4, 2, 1]\nC: [4, 1, 2, 3]\nD: [2, 1, 3, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_176_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_176_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_176_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_176_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_176_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 1, 4]\nB: [3, 4, 2, 1]\nC: [4, 1, 2, 3]\nD: [2, 1, 3, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 2, 3]\nB: [1, 4, 3, 2]\nC: [2, 1, 4, 3]\nD: [3, 4, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_177_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_177_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_177_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_177_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_177_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [1, 4, 3, 2]\nC: [2, 1, 4, 3]\nD: [3, 4, 1, 2]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 2, 1, 3]\nB: [1, 4, 3, 2]\nC: [2, 1, 3, 4]\nD: [4, 3, 1, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_178_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_178_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_178_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_178_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_178_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 1, 3]\nB: [1, 4, 3, 2]\nC: [2, 1, 3, 4]\nD: [4, 3, 1, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 2, 3]\nB: [3, 1, 4, 2]\nC: [1, 2, 4, 3]\nD: [1, 2, 3, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_179_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_179_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_179_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_179_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_179_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 2, 3]\nB: [3, 1, 4, 2]\nC: [1, 2, 4, 3]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 2, 1]\nB: [3, 4, 2, 1]\nC: [3, 1, 4, 2]\nD: [1, 4, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_180_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_180_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_180_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_180_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_180_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [3, 4, 2, 1]\nC: [3, 1, 4, 2]\nD: [1, 4, 2, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 1, 3, 4]\nB: [4, 1, 3, 2]\nC: [1, 4, 3, 2]\nD: [3, 4, 2, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_181_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_181_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_181_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_181_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_181_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 1, 3, 4]\nB: [4, 1, 3, 2]\nC: [1, 4, 3, 2]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 3, 2]\nB: [3, 4, 1, 2]\nC: [2, 3, 1, 4]\nD: [3, 2, 4, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_182_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_182_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_182_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_182_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_182_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [3, 4, 1, 2]\nC: [2, 3, 1, 4]\nD: [3, 2, 4, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 2, 3, 1]\nB: [4, 1, 3, 2]\nC: [2, 1, 4, 3]\nD: [1, 4, 3, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_183_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_183_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_183_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_183_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_183_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [4, 1, 3, 2]\nC: [2, 1, 4, 3]\nD: [1, 4, 3, 2]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 4, 2, 1]\nB: [3, 2, 4, 1]\nC: [4, 1, 2, 3]\nD: [4, 2, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_184_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_184_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_184_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_184_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_184_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 4, 2, 1]\nB: [3, 2, 4, 1]\nC: [4, 1, 2, 3]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 3, 2, 4]\nB: [4, 2, 3, 1]\nC: [3, 2, 1, 4]\nD: [3, 1, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_185_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_185_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_185_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_185_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_185_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 3, 2, 4]\nB: [4, 2, 3, 1]\nC: [3, 2, 1, 4]\nD: [3, 1, 4, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 2, 3]\nB: [3, 1, 4, 2]\nC: [2, 1, 4, 3]\nD: [4, 2, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_186_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_186_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_186_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_186_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_186_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 2, 3]\nB: [3, 1, 4, 2]\nC: [2, 1, 4, 3]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 3, 2, 1]\nB: [4, 3, 1, 2]\nC: [1, 2, 4, 3]\nD: [1, 2, 3, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_187_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_187_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_187_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_187_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_187_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 3, 2, 1]\nB: [4, 3, 1, 2]\nC: [1, 2, 4, 3]\nD: [1, 2, 3, 4]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 2, 3, 1]\nB: [4, 3, 2, 1]\nC: [1, 3, 4, 2]\nD: [1, 3, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_188_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_188_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_188_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_188_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_188_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [4, 3, 2, 1]\nC: [1, 3, 4, 2]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 1, 4, 2]\nB: [3, 2, 4, 1]\nC: [1, 2, 4, 3]\nD: [1, 4, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_189_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_189_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_189_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_189_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_189_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 1, 4, 2]\nB: [3, 2, 4, 1]\nC: [1, 2, 4, 3]\nD: [1, 4, 2, 3]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 2, 3, 1]\nB: [1, 2, 3, 4]\nC: [1, 2, 4, 3]\nD: [1, 3, 4, 2]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_190_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_190_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_190_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_190_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_190_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [1, 2, 3, 4]\nC: [1, 2, 4, 3]\nD: [1, 3, 4, 2]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 3, 4, 1]\nB: [3, 1, 4, 2]\nC: [4, 3, 2, 1]\nD: [4, 1, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_191_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_191_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_191_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_191_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_191_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [3, 1, 4, 2]\nC: [4, 3, 2, 1]\nD: [4, 1, 2, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [2, 3, 4, 1]\nB: [2, 4, 1, 3]\nC: [1, 4, 2, 3]\nD: [4, 2, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_192_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_192_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_192_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_192_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_192_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [2, 3, 4, 1]\nB: [2, 4, 1, 3]\nC: [1, 4, 2, 3]\nD: [4, 2, 3, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 3, 4]\nB: [4, 2, 1, 3]\nC: [1, 3, 4, 2]\nD: [1, 4, 2, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_193_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_193_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_193_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_193_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_193_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [4, 2, 1, 3]\nC: [1, 3, 4, 2]\nD: [1, 4, 2, 3]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 1, 3, 2]\nB: [4, 2, 1, 3]\nC: [2, 4, 1, 3]\nD: [1, 3, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_194_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_194_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_194_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_194_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_194_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 1, 3, 2]\nB: [4, 2, 1, 3]\nC: [2, 4, 1, 3]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 4, 3, 2]\nB: [1, 2, 3, 4]\nC: [4, 1, 2, 3]\nD: [1, 3, 2, 4]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_195_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_195_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_195_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_195_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_195_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 4, 3, 2]\nB: [1, 2, 3, 4]\nC: [4, 1, 2, 3]\nD: [1, 3, 2, 4]"}, "output": {"output_text": "C"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 4, 3]\nB: [2, 4, 3, 1]\nC: [3, 1, 4, 2]\nD: [3, 4, 2, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_196_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_196_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_196_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_196_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_196_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 4, 3]\nB: [2, 4, 3, 1]\nC: [3, 1, 4, 2]\nD: [3, 4, 2, 1]"}, "output": {"output_text": "D"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [3, 2, 1, 4]\nB: [3, 4, 2, 1]\nC: [4, 2, 3, 1]\nD: [2, 4, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_197_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_197_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_197_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_197_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_197_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [3, 2, 1, 4]\nB: [3, 4, 2, 1]\nC: [4, 2, 3, 1]\nD: [2, 4, 3, 1]"}, "output": {"output_text": "A"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [4, 2, 3, 1]\nB: [1, 4, 2, 3]\nC: [3, 1, 4, 2]\nD: [2, 4, 3, 1]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_198_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_198_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_198_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_198_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_198_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [4, 2, 3, 1]\nB: [1, 4, 2, 3]\nC: [3, 1, 4, 2]\nD: [2, 4, 3, 1]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "jigsaw_puzzle_solving_painting", "options": "A: [1, 2, 3, 4]\nB: [2, 4, 1, 3]\nC: [4, 1, 3, 2]\nD: [2, 1, 4, 3]", "visual_input_component": ["painting_image", "visual_mark"], "input": {"input_image_path": ["2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_199_0.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_199_1.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_199_2.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_199_3.jpg", "2D-spatial/jigsaw_puzzle_solving/jigsaw_puzzle_solving_199_4.jpg"], "question": "The patches in the middle of the image might be disordered. Please state the correct order of the number indexes based on the given patches, following the sequence: top left, top right, bottom left, bottom right.", "context": "Your task is give a order of these given images\nSelect from the following choices.\nA: [1, 2, 3, 4]\nB: [2, 4, 1, 3]\nC: [4, 1, 3, 2]\nD: [2, 1, 4, 3]"}, "output": {"output_text": "B"}, "task": "jigsaw_puzzle_solving"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_0_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_1_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_2_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_3_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_4_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_5_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_5_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_5_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_5_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_5_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_5_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_5_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_5_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_6_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_7_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_7_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_7_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_7_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_7_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_7_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_7_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_7_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_8_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_9_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_10_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_11_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_11_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_11_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_11_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_11_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_11_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_11_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_11_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_12_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_13_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_14_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_14_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_14_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_14_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_14_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_14_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_14_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_14_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_15_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_16_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_17_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_18_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_19_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_20_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_20_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_20_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_20_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_20_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_20_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_20_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_20_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_21_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_22_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_23_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_23_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_23_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_23_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_23_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_23_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_23_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_23_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_24_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_24_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_24_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_24_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_24_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_24_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_24_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_24_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_25_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_26_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_27_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_27_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_27_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_27_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_27_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_27_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_27_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_27_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_28_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_29_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_30_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_31_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_32_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_33_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_33_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_33_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_33_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_33_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_33_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_33_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_33_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_34_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_35_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_36_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_37_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_37_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_37_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_37_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_37_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_37_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_37_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_37_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_38_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_39_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_39_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_39_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_39_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_39_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_39_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_39_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_39_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_40_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_41_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_42_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_42_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_42_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_42_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_42_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_42_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_42_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_42_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_43_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_43_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_43_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_43_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_43_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_43_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_43_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_43_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_44_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_45_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_46_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_46_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_46_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_46_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_46_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_46_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_46_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_46_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_47_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_48_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_49_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_50_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_51_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_52_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_53_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_53_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_53_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_53_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_53_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_53_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_53_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_53_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_54_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_55_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_56_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_57_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_58_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_58_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_58_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_58_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_58_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_58_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_58_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_58_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_59_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_60_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_61_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_62_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_63_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_64_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_65_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_65_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_65_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_65_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_65_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_65_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_65_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_65_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_66_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_66_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_66_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_66_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_66_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_66_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_66_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_66_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_67_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_68_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_69_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_70_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_70_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_70_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_70_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_70_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_70_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_70_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_70_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_71_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_71_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_71_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_71_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_71_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_71_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_71_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_71_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_72_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_73_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_74_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_75_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_75_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_75_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_75_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_75_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_75_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_75_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_75_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_76_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_77_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_77_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_77_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_77_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_77_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_77_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_77_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_77_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_78_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_78_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_78_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_78_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_78_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_78_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_78_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_78_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_79_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_80_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_81_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_81_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_81_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_81_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_81_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_81_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_81_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_81_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_82_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_83_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_84_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_85_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_86_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_87_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_88_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_89_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_90_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_91_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_92_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_93_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_94_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_95_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_95_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_95_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_95_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_95_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_95_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_95_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_95_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_96_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_97_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_98_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_99_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_99_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_99_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_99_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_99_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_99_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_99_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_99_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_100_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_101_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_101_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_101_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_101_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_101_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_101_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_101_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_101_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_102_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_103_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_103_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_103_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_103_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_103_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_103_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_103_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_103_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_104_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_104_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_104_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_104_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_104_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_104_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_104_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_104_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_105_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_106_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_107_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_108_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_109_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_110_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_111_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_111_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_111_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_111_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_111_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_111_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_111_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_111_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_112_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_113_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_114_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_115_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_116_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_117_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_117_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_117_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_117_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_117_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_117_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_117_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_117_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_118_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_119_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_120_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_121_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_122_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_123_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_124_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_125_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_126_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_127_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_128_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_129_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_130_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_131_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_131_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_131_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_131_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_131_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_131_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_131_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_131_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_132_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_133_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_134_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_134_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_134_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_134_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_134_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_134_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_134_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_134_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_135_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_136_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_137_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_138_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_139_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_140_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_141_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_142_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_143_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_144_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_145_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_146_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_147_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_148_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_148_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_148_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_148_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_148_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_148_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_148_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_148_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_149_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_150_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_151_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_152_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_152_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_152_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_152_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_152_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_152_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_152_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_152_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_153_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_154_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_155_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "I"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_156_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_157_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_157_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_157_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_157_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_157_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_157_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_157_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_157_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_158_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_159_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_159_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_159_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_159_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_159_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_159_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_159_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_159_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_160_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_160_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_160_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_160_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_160_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_160_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_160_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_160_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_161_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_162_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_163_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_164_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_165_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_166_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_166_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_166_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_166_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_166_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_166_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_166_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_166_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_167_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_168_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_169_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_170_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_171_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_171_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_171_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_171_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_171_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_171_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_171_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_171_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_172_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_173_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_174_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_175_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "G"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_176_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_176_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_176_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_176_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_176_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_176_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_176_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_176_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_177_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_178_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_179_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_179_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_179_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_179_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_179_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_179_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_179_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_179_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_180_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_181_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_182_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_182_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_182_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_182_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_182_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_182_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_182_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_182_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_183_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_184_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_184_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_184_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_184_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_184_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_184_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_184_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_184_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_185_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_185_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_185_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_185_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_185_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_185_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_185_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_185_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_186_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_187_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_188_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "H"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_189_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_190_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_191_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_192_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_192_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_192_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_192_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_192_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_192_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_192_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_192_7.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_193_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_194_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "C"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_195_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "D"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_196_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "E"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_197_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "B"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_198_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "F"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "SPEC", "options": "A: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_0.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_1.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_2.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_3.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_4.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_5.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_6.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_7.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_8.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_9.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_10.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_11.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_12.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_13.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_14.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_15.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_16.jpg", "2D-spatial/Image_text_retrieval_with_Spatial_Context/Image_text_retrieval_with_Spatial_Context_199_17.jpg"], "question": "Please retrieve the matching image to the query text in the candidate images.", "context": "Your task is : Given a text addressing spatial context, identify the matched image within candidates. The input images are the first 9 images\nSelect from the following choices.\nA: The 10th image\nB: The 11th image\nC: The 12th image\nD: The 13th image\nE: The 14th image\nF: The 15th image\nG: The 16th image\nH: The 17th image\nI: The 18th image"}, "output": {"output_text": "A"}, "task": "Image_text_retrieval_with_Spatial_Context"}
{"source": "tapvid_davis", "options": "A: [0.627, 0.2]\nB: [0.166, 0.657]\nC: [0.95, 0.907]\nD: [0.328, 0.477]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_0_0.jpg", "2D-spatial/point_tracking/point_tracking_0_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.627, 0.2]\nB: [0.166, 0.657]\nC: [0.95, 0.907]\nD: [0.328, 0.477]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.368, 0.265]\nB: [0.925, 0.128]\nC: [0.133, 0.261]\nD: [0.488, 0.101]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_1_0.jpg", "2D-spatial/point_tracking/point_tracking_1_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.366, 0.265]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.368, 0.265]\nB: [0.925, 0.128]\nC: [0.133, 0.261]\nD: [0.488, 0.101]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.398, 0.165]\nB: [0.606, 0.999]\nC: [0.955, 0.756]\nD: [0.976, 0.964]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_2_0.jpg", "2D-spatial/point_tracking/point_tracking_2_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.488, -0.073]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.398, 0.165]\nB: [0.606, 0.999]\nC: [0.955, 0.756]\nD: [0.976, 0.964]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.411, 0.483]\nB: [0.624, 0.13]\nC: [0.256, 0.845]\nD: [0.393, 0.328]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_3_0.jpg", "2D-spatial/point_tracking/point_tracking_3_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.411, 0.483]\nB: [0.624, 0.13]\nC: [0.256, 0.845]\nD: [0.393, 0.328]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.851, 0.69]\nB: [0.112, 0.164]\nC: [0.561, 0.3]\nD: [0.69, 0.205]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_4_0.jpg", "2D-spatial/point_tracking/point_tracking_4_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.572, 0.294]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.851, 0.69]\nB: [0.112, 0.164]\nC: [0.561, 0.3]\nD: [0.69, 0.205]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.71, 0.765]\nB: [0.039, 0.565]\nC: [0.599, 0.897]\nD: [0.077, 0.037]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_5_0.jpg", "2D-spatial/point_tracking/point_tracking_5_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.127, 0.205]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.71, 0.765]\nB: [0.039, 0.565]\nC: [0.599, 0.897]\nD: [0.077, 0.037]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.75, 0.266]\nC: [0.658, 0.765]\nD: [0.825, 0.377]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_6_0.jpg", "2D-spatial/point_tracking/point_tracking_6_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.75, 0.266]\nC: [0.658, 0.765]\nD: [0.825, 0.377]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.458, 0.112]\nB: [0.522, 0.216]\nC: [0.672, 0.493]\nD: [0.435, 0.891]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_7_0.jpg", "2D-spatial/point_tracking/point_tracking_7_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.392, 0.15]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.458, 0.112]\nB: [0.522, 0.216]\nC: [0.672, 0.493]\nD: [0.435, 0.891]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.055, 0.212]\nC: [0.926, 0.897]\nD: [0.088, 0.69]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_8_0.jpg", "2D-spatial/point_tracking/point_tracking_8_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.055, 0.212]\nC: [0.926, 0.897]\nD: [0.088, 0.69]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.572, 0.347]\nB: [0.822, 0.524]\nC: [0.668, 0.975]\nD: [0.228, 0.421]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_9_0.jpg", "2D-spatial/point_tracking/point_tracking_9_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.84, 0.359]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.572, 0.347]\nB: [0.822, 0.524]\nC: [0.668, 0.975]\nD: [0.228, 0.421]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.55, 0.157]\nB: [0.225, 0.407]\nC: [0.428, 0.202]\nD: [0.848, 0.045]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_10_0.jpg", "2D-spatial/point_tracking/point_tracking_10_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.195, 0.402]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.55, 0.157]\nB: [0.225, 0.407]\nC: [0.428, 0.202]\nD: [0.848, 0.045]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.606, 0.811]\nB: [0.463, 0.023]\nC: [0.307, 0.429]\nD: [0.789, 0.214]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_11_0.jpg", "2D-spatial/point_tracking/point_tracking_11_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.793, 0.216]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.606, 0.811]\nB: [0.463, 0.023]\nC: [0.307, 0.429]\nD: [0.789, 0.214]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.276, 0.532]\nB: [0.401, 0.534]\nC: [0.28, 0.157]\nD: [0.0, 0.0]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_12_0.jpg", "2D-spatial/point_tracking/point_tracking_12_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.276, 0.532]\nB: [0.401, 0.534]\nC: [0.28, 0.157]\nD: [0.0, 0.0]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.447, 0.29]\nB: [0.574, 0.304]\nC: [0.111, 0.034]\nD: [0.966, 0.262]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_13_0.jpg", "2D-spatial/point_tracking/point_tracking_13_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.574, 0.304]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.447, 0.29]\nB: [0.574, 0.304]\nC: [0.111, 0.034]\nD: [0.966, 0.262]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.769, 0.374]\nB: [0.69, -0.054]\nC: [0.182, 0.457]\nD: [0.423, 0.809]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_14_0.jpg", "2D-spatial/point_tracking/point_tracking_14_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.723, -0.019]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.769, 0.374]\nB: [0.69, -0.054]\nC: [0.182, 0.457]\nD: [0.423, 0.809]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.861, 0.924]\nB: [0.976, 0.801]\nC: [0.63, 0.946]\nD: [0.457, 0.566]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_15_0.jpg", "2D-spatial/point_tracking/point_tracking_15_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.491, 0.572]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.861, 0.924]\nB: [0.976, 0.801]\nC: [0.63, 0.946]\nD: [0.457, 0.566]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.737, 0.324]\nB: [0.346, 0.386]\nC: [0.464, 0.662]\nD: [0.24, 0.833]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_16_0.jpg", "2D-spatial/point_tracking/point_tracking_16_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.24, 0.833]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.737, 0.324]\nB: [0.346, 0.386]\nC: [0.464, 0.662]\nD: [0.24, 0.833]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.902, 0.889]\nB: [0.734, 0.179]\nC: [0.695, 0.313]\nD: [0.552, 0.586]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_17_0.jpg", "2D-spatial/point_tracking/point_tracking_17_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.552, 0.586]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.902, 0.889]\nB: [0.734, 0.179]\nC: [0.695, 0.313]\nD: [0.552, 0.586]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.015, 0.757]\nB: [0.493, 0.371]\nC: [0.002, 0.142]\nD: [0.438, 0.698]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_18_0.jpg", "2D-spatial/point_tracking/point_tracking_18_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.496, 0.371]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.015, 0.757]\nB: [0.493, 0.371]\nC: [0.002, 0.142]\nD: [0.438, 0.698]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.629, 0.185]\nB: [0.357, 0.413]\nC: [0.521, 0.95]\nD: [0.591, 0.415]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_19_0.jpg", "2D-spatial/point_tracking/point_tracking_19_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.598, 0.417]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.629, 0.185]\nB: [0.357, 0.413]\nC: [0.521, 0.95]\nD: [0.591, 0.415]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.244, 0.498]\nB: [0.749, 0.317]\nC: [0.76, 0.581]\nD: [0.806, 0.63]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_20_0.jpg", "2D-spatial/point_tracking/point_tracking_20_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.678, 0.324]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.244, 0.498]\nB: [0.749, 0.317]\nC: [0.76, 0.581]\nD: [0.806, 0.63]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.786, 0.763]\nB: [0.139, 0.661]\nC: [0.549, 0.391]\nD: [0.901, 0.478]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_21_0.jpg", "2D-spatial/point_tracking/point_tracking_21_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.577, 0.479]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.786, 0.763]\nB: [0.139, 0.661]\nC: [0.549, 0.391]\nD: [0.901, 0.478]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.84, 0.364]\nB: [0.664, 0.326]\nC: [0.643, 0.579]\nD: [0.486, 0.458]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_22_0.jpg", "2D-spatial/point_tracking/point_tracking_22_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.836, 0.364]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.84, 0.364]\nB: [0.664, 0.326]\nC: [0.643, 0.579]\nD: [0.486, 0.458]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.667, 0.104]\nB: [0.801, 0.792]\nC: [0.271, 0.317]\nD: [0.699, 0.539]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_23_0.jpg", "2D-spatial/point_tracking/point_tracking_23_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.631, 0.551]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.667, 0.104]\nB: [0.801, 0.792]\nC: [0.271, 0.317]\nD: [0.699, 0.539]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.433, 0.435]\nB: [0.509, 0.298]\nC: [0.517, 0.969]\nD: [0.096, 0.626]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_24_0.jpg", "2D-spatial/point_tracking/point_tracking_24_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.517, 0.969]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.433, 0.435]\nB: [0.509, 0.298]\nC: [0.517, 0.969]\nD: [0.096, 0.626]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.572, 0.447]\nB: [0.317, 0.394]\nC: [0.276, 0.148]\nD: [0.404, 0.225]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_25_0.jpg", "2D-spatial/point_tracking/point_tracking_25_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.571, 0.446]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.572, 0.447]\nB: [0.317, 0.394]\nC: [0.276, 0.148]\nD: [0.404, 0.225]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.082, 0.932]\nB: [0.086, 0.159]\nC: [0.711, 0.457]\nD: [0.056, 0.373]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_26_0.jpg", "2D-spatial/point_tracking/point_tracking_26_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.082, 0.932]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.082, 0.932]\nB: [0.086, 0.159]\nC: [0.711, 0.457]\nD: [0.056, 0.373]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.151, 0.743]\nB: [0.746, 0.222]\nC: [0.439, 0.384]\nD: [0.367, 0.888]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_27_0.jpg", "2D-spatial/point_tracking/point_tracking_27_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.717, 0.34]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.151, 0.743]\nB: [0.746, 0.222]\nC: [0.439, 0.384]\nD: [0.367, 0.888]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.215, 0.968]\nB: [0.558, 0.522]\nC: [0.967, 0.723]\nD: [0.212, 0.809]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_28_0.jpg", "2D-spatial/point_tracking/point_tracking_28_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.584, 0.596]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.215, 0.968]\nB: [0.558, 0.522]\nC: [0.967, 0.723]\nD: [0.212, 0.809]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.413, 0.07]\nB: [0.437, 0.318]\nC: [0.155, 0.833]\nD: [0.607, 0.498]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_29_0.jpg", "2D-spatial/point_tracking/point_tracking_29_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.525, 0.482]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.413, 0.07]\nB: [0.437, 0.318]\nC: [0.155, 0.833]\nD: [0.607, 0.498]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.438, 0.097]\nB: [0.631, 0.018]\nC: [0.215, 0.313]\nD: [0.263, 0.723]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_30_0.jpg", "2D-spatial/point_tracking/point_tracking_30_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.438, 0.097]\nB: [0.631, 0.018]\nC: [0.215, 0.313]\nD: [0.263, 0.723]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.896, 0.061]\nB: [0.167, 0.451]\nC: [0.216, 0.513]\nD: [0.57, 0.361]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_31_0.jpg", "2D-spatial/point_tracking/point_tracking_31_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.569, 0.361]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.896, 0.061]\nB: [0.167, 0.451]\nC: [0.216, 0.513]\nD: [0.57, 0.361]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.917, 0.582]\nB: [0.858, 0.833]\nC: [0.962, 0.955]\nD: [0.285, 0.385]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_32_0.jpg", "2D-spatial/point_tracking/point_tracking_32_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.285, 0.385]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.917, 0.582]\nB: [0.858, 0.833]\nC: [0.962, 0.955]\nD: [0.285, 0.385]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.65, 0.016]\nB: [0.761, 0.985]\nC: [0.538, 0.359]\nD: [0.842, 0.025]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_33_0.jpg", "2D-spatial/point_tracking/point_tracking_33_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.537, 0.35]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.65, 0.016]\nB: [0.761, 0.985]\nC: [0.538, 0.359]\nD: [0.842, 0.025]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.9, 0.904]\nB: [0.664, 0.466]\nC: [0.273, 0.03]\nD: [0.393, 0.275]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_34_0.jpg", "2D-spatial/point_tracking/point_tracking_34_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.427, 0.335]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.9, 0.904]\nB: [0.664, 0.466]\nC: [0.273, 0.03]\nD: [0.393, 0.275]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.051, 0.768]\nB: [0.363, 0.364]\nC: [0.376, 0.685]\nD: [0.454, 0.177]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_35_0.jpg", "2D-spatial/point_tracking/point_tracking_35_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.051, 0.768]\nB: [0.363, 0.364]\nC: [0.376, 0.685]\nD: [0.454, 0.177]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.086, 0.538]\nB: [0.209, 0.589]\nC: [0.727, 0.366]\nD: [0.529, 0.299]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_36_0.jpg", "2D-spatial/point_tracking/point_tracking_36_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.789, 0.359]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.086, 0.538]\nB: [0.209, 0.589]\nC: [0.727, 0.366]\nD: [0.529, 0.299]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.5, 0.007]\nB: [0.731, 0.113]\nC: [0.636, 0.642]\nD: [0.325, 0.315]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_37_0.jpg", "2D-spatial/point_tracking/point_tracking_37_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.5, 0.007]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.5, 0.007]\nB: [0.731, 0.113]\nC: [0.636, 0.642]\nD: [0.325, 0.315]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.032, 0.829]\nC: [0.507, 0.48]\nD: [0.697, 0.839]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_38_0.jpg", "2D-spatial/point_tracking/point_tracking_38_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.296, 0.358]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.032, 0.829]\nC: [0.507, 0.48]\nD: [0.697, 0.839]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.773, 0.291]\nB: [0.256, 0.091]\nC: [0.561, 0.908]\nD: [0.572, 0.294]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_39_0.jpg", "2D-spatial/point_tracking/point_tracking_39_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.572, 0.294]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.773, 0.291]\nB: [0.256, 0.091]\nC: [0.561, 0.908]\nD: [0.572, 0.294]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.278, 0.564]\nB: [0.995, 0.367]\nC: [0.923, 0.335]\nD: [0.942, 0.46]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_40_0.jpg", "2D-spatial/point_tracking/point_tracking_40_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.995, 0.367]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.278, 0.564]\nB: [0.995, 0.367]\nC: [0.923, 0.335]\nD: [0.942, 0.46]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.791, 0.587]\nB: [0.006, 0.092]\nC: [0.454, 0.459]\nD: [0.339, 0.211]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_41_0.jpg", "2D-spatial/point_tracking/point_tracking_41_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.339, 0.211]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.791, 0.587]\nB: [0.006, 0.092]\nC: [0.454, 0.459]\nD: [0.339, 0.211]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.834, 0.042]\nB: [0.0, 0.0]\nC: [0.657, 0.031]\nD: [0.366, 0.215]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_42_0.jpg", "2D-spatial/point_tracking/point_tracking_42_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.834, 0.042]\nB: [0.0, 0.0]\nC: [0.657, 0.031]\nD: [0.366, 0.215]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.522, 0.936]\nB: [0.431, 0.505]\nC: [0.056, 0.43]\nD: [0.445, 0.055]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_43_0.jpg", "2D-spatial/point_tracking/point_tracking_43_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.465, 0.516]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.522, 0.936]\nB: [0.431, 0.505]\nC: [0.056, 0.43]\nD: [0.445, 0.055]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.441, 0.076]\nB: [0.638, 0.275]\nC: [0.844, 0.793]\nD: [0.485, 0.944]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_44_0.jpg", "2D-spatial/point_tracking/point_tracking_44_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.638, 0.276]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.441, 0.076]\nB: [0.638, 0.275]\nC: [0.844, 0.793]\nD: [0.485, 0.944]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.464, 0.801]\nB: [0.453, 0.251]\nC: [0.254, 0.642]\nD: [0.099, 0.252]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_45_0.jpg", "2D-spatial/point_tracking/point_tracking_45_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.254, 0.642]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.464, 0.801]\nB: [0.453, 0.251]\nC: [0.254, 0.642]\nD: [0.099, 0.252]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.281, 0.178]\nB: [0.162, 0.715]\nC: [0.761, 0.046]\nD: [0.557, 0.001]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_46_0.jpg", "2D-spatial/point_tracking/point_tracking_46_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.571, 0.033]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.281, 0.178]\nB: [0.162, 0.715]\nC: [0.761, 0.046]\nD: [0.557, 0.001]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [1.0, 0.279]\nB: [0.584, 0.204]\nC: [0.191, 0.877]\nD: [0.563, 0.267]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_47_0.jpg", "2D-spatial/point_tracking/point_tracking_47_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.582, 0.204]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [1.0, 0.279]\nB: [0.584, 0.204]\nC: [0.191, 0.877]\nD: [0.563, 0.267]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.002, 0.108]\nB: [0.549, 0.37]\nC: [0.846, 0.072]\nD: [0.502, 0.698]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_48_0.jpg", "2D-spatial/point_tracking/point_tracking_48_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.552, 0.368]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.002, 0.108]\nB: [0.549, 0.37]\nC: [0.846, 0.072]\nD: [0.502, 0.698]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.509, 0.858]\nB: [0.643, 0.572]\nC: [0.432, 0.735]\nD: [0.542, 0.338]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_49_0.jpg", "2D-spatial/point_tracking/point_tracking_49_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.542, 0.339]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.509, 0.858]\nB: [0.643, 0.572]\nC: [0.432, 0.735]\nD: [0.542, 0.338]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.048, 0.391]\nB: [0.787, 0.747]\nC: [0.518, 0.517]\nD: [0.507, 0.833]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_50_0.jpg", "2D-spatial/point_tracking/point_tracking_50_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.515, 0.514]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.048, 0.391]\nB: [0.787, 0.747]\nC: [0.518, 0.517]\nD: [0.507, 0.833]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.528, 0.45]\nB: [0.74, 0.315]\nC: [0.482, 0.584]\nD: [0.088, 0.042]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_51_0.jpg", "2D-spatial/point_tracking/point_tracking_51_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.723, 0.386]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.528, 0.45]\nB: [0.74, 0.315]\nC: [0.482, 0.584]\nD: [0.088, 0.042]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.04, 0.904]\nB: [0.4, 0.187]\nC: [0.134, 0.465]\nD: [0.294, 0.45]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_52_0.jpg", "2D-spatial/point_tracking/point_tracking_52_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.056, 0.907]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.04, 0.904]\nB: [0.4, 0.187]\nC: [0.134, 0.465]\nD: [0.294, 0.45]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.232, 0.071]\nB: [0.57, 0.335]\nC: [0.206, 0.4]\nD: [0.554, 0.081]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_53_0.jpg", "2D-spatial/point_tracking/point_tracking_53_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.585, 0.342]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.232, 0.071]\nB: [0.57, 0.335]\nC: [0.206, 0.4]\nD: [0.554, 0.081]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.043, 0.026]\nB: [0.536, 0.287]\nC: [0.878, 0.179]\nD: [0.519, 0.466]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_54_0.jpg", "2D-spatial/point_tracking/point_tracking_54_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.537, 0.483]) in Image 1 within the Image 2? Note that the width of the input RGB image is 910 and the height is 480.", "context": "Select from the following choices.\nA: [0.043, 0.026]\nB: [0.536, 0.287]\nC: [0.878, 0.179]\nD: [0.519, 0.466]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.712, 0.402]\nB: [0.937, 0.199]\nC: [0.286, 0.017]\nD: [0.843, 0.865]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_55_0.jpg", "2D-spatial/point_tracking/point_tracking_55_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.309, -0.011]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.712, 0.402]\nB: [0.937, 0.199]\nC: [0.286, 0.017]\nD: [0.843, 0.865]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.014, 0.882]\nB: [0.728, 0.689]\nC: [0.088, 0.375]\nD: [0.554, 0.511]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_56_0.jpg", "2D-spatial/point_tracking/point_tracking_56_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.6, 0.808]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.014, 0.882]\nB: [0.728, 0.689]\nC: [0.088, 0.375]\nD: [0.554, 0.511]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.109, 0.199]\nB: [0.021, 0.741]\nC: [0.0, 0.0]\nD: [0.405, 0.69]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_57_0.jpg", "2D-spatial/point_tracking/point_tracking_57_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.572, 0.171]) in Image 1 within the Image 2? Note that the width of the input RGB image is 910 and the height is 480.", "context": "Select from the following choices.\nA: [0.109, 0.199]\nB: [0.021, 0.741]\nC: [0.0, 0.0]\nD: [0.405, 0.69]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.38, 0.932]\nB: [0.47, 0.409]\nC: [0.528, 0.936]\nD: [0.533, 0.686]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_58_0.jpg", "2D-spatial/point_tracking/point_tracking_58_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.459, 0.412]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.38, 0.932]\nB: [0.47, 0.409]\nC: [0.528, 0.936]\nD: [0.533, 0.686]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.092, 0.225]\nB: [0.605, 0.232]\nC: [0.39, 0.458]\nD: [0.377, 0.065]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_59_0.jpg", "2D-spatial/point_tracking/point_tracking_59_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.064, 0.24]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.092, 0.225]\nB: [0.605, 0.232]\nC: [0.39, 0.458]\nD: [0.377, 0.065]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.307, 0.516]\nB: [0.508, 0.388]\nC: [0.368, 0.937]\nD: [0.527, 0.106]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_60_0.jpg", "2D-spatial/point_tracking/point_tracking_60_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.513, 0.478]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.307, 0.516]\nB: [0.508, 0.388]\nC: [0.368, 0.937]\nD: [0.527, 0.106]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.621, 0.322]\nB: [0.757, 0.909]\nC: [0.765, 0.887]\nD: [0.485, 0.282]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_61_0.jpg", "2D-spatial/point_tracking/point_tracking_61_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.543, 0.573]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.621, 0.322]\nB: [0.757, 0.909]\nC: [0.765, 0.887]\nD: [0.485, 0.282]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.148, 0.593]\nB: [0.867, 0.594]\nC: [0.363, 0.725]\nD: [0.988, 0.381]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_62_0.jpg", "2D-spatial/point_tracking/point_tracking_62_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.363, 0.725]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.148, 0.593]\nB: [0.867, 0.594]\nC: [0.363, 0.725]\nD: [0.988, 0.381]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.538, 0.67]\nB: [0.113, 0.312]\nC: [0.781, 0.017]\nD: [0.78, 0.124]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_63_0.jpg", "2D-spatial/point_tracking/point_tracking_63_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.113, 0.312]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.538, 0.67]\nB: [0.113, 0.312]\nC: [0.781, 0.017]\nD: [0.78, 0.124]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.409, 0.184]\nB: [0.327, 0.555]\nC: [0.304, 0.166]\nD: [0.398, 0.141]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_64_0.jpg", "2D-spatial/point_tracking/point_tracking_64_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.318, 0.204]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.409, 0.184]\nB: [0.327, 0.555]\nC: [0.304, 0.166]\nD: [0.398, 0.141]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.663, 0.685]\nB: [0.5, 0.562]\nC: [0.628, 0.094]\nD: [0.876, 0.492]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_65_0.jpg", "2D-spatial/point_tracking/point_tracking_65_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.5, 0.546]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.663, 0.685]\nB: [0.5, 0.562]\nC: [0.628, 0.094]\nD: [0.876, 0.492]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.148, 0.822]\nB: [0.654, 0.462]\nC: [0.274, 0.087]\nD: [0.294, 0.87]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_66_0.jpg", "2D-spatial/point_tracking/point_tracking_66_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.225, -0.034]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.148, 0.822]\nB: [0.654, 0.462]\nC: [0.274, 0.087]\nD: [0.294, 0.87]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.443, 0.32]\nB: [0.907, 0.404]\nC: [0.451, 0.543]\nD: [0.775, 0.465]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_67_0.jpg", "2D-spatial/point_tracking/point_tracking_67_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.775, 0.465]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.443, 0.32]\nB: [0.907, 0.404]\nC: [0.451, 0.543]\nD: [0.775, 0.465]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.912, 0.423]\nB: [0.477, 0.187]\nC: [0.439, 0.609]\nD: [0.127, 0.162]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_68_0.jpg", "2D-spatial/point_tracking/point_tracking_68_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.475, 0.188]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.912, 0.423]\nB: [0.477, 0.187]\nC: [0.439, 0.609]\nD: [0.127, 0.162]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.348, 0.247]\nB: [0.53, 0.395]\nC: [0.894, 0.004]\nD: [0.561, 0.958]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_69_0.jpg", "2D-spatial/point_tracking/point_tracking_69_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.528, 0.394]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.348, 0.247]\nB: [0.53, 0.395]\nC: [0.894, 0.004]\nD: [0.561, 0.958]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.451, 0.01]\nB: [0.588, 0.525]\nC: [0.542, 0.784]\nD: [0.271, 0.069]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_70_0.jpg", "2D-spatial/point_tracking/point_tracking_70_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.21, 0.024]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.451, 0.01]\nB: [0.588, 0.525]\nC: [0.542, 0.784]\nD: [0.271, 0.069]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.545, 0.343]\nB: [0.872, 0.767]\nC: [0.848, 0.331]\nD: [0.082, 0.655]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_71_0.jpg", "2D-spatial/point_tracking/point_tracking_71_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.546, 0.344]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.545, 0.343]\nB: [0.872, 0.767]\nC: [0.848, 0.331]\nD: [0.082, 0.655]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.246, 0.558]\nB: [0.213, 0.365]\nC: [0.605, 0.491]\nD: [0.56, -0.031]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_72_0.jpg", "2D-spatial/point_tracking/point_tracking_72_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.527, 0.136]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.246, 0.558]\nB: [0.213, 0.365]\nC: [0.605, 0.491]\nD: [0.56, -0.031]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.497, 0.448]\nB: [0.783, 0.271]\nC: [0.406, 0.738]\nD: [0.416, 0.886]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_73_0.jpg", "2D-spatial/point_tracking/point_tracking_73_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.783, 0.271]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.497, 0.448]\nB: [0.783, 0.271]\nC: [0.406, 0.738]\nD: [0.416, 0.886]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.939, 0.844]\nB: [0.453, 0.842]\nC: [0.019, 0.701]\nD: [0.33, 0.019]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_74_0.jpg", "2D-spatial/point_tracking/point_tracking_74_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.382, 0.074]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.939, 0.844]\nB: [0.453, 0.842]\nC: [0.019, 0.701]\nD: [0.33, 0.019]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.712, 0.468]\nB: [0.757, 0.203]\nC: [0.602, 0.149]\nD: [0.624, 0.442]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_75_0.jpg", "2D-spatial/point_tracking/point_tracking_75_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.625, 0.442]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.712, 0.468]\nB: [0.757, 0.203]\nC: [0.602, 0.149]\nD: [0.624, 0.442]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.775, 0.586]\nB: [0.403, 0.947]\nC: [0.0, 0.0]\nD: [0.095, 0.525]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_76_0.jpg", "2D-spatial/point_tracking/point_tracking_76_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 910 and the height is 480.", "context": "Select from the following choices.\nA: [0.775, 0.586]\nB: [0.403, 0.947]\nC: [0.0, 0.0]\nD: [0.095, 0.525]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.306, 0.517]\nB: [0.404, 0.704]\nC: [0.0, 0.0]\nD: [0.389, 0.429]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_77_0.jpg", "2D-spatial/point_tracking/point_tracking_77_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.306, 0.517]\nB: [0.404, 0.704]\nC: [0.0, 0.0]\nD: [0.389, 0.429]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.421, 0.202]\nB: [0.936, 0.193]\nC: [0.836, 0.093]\nD: [0.892, 0.905]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_78_0.jpg", "2D-spatial/point_tracking/point_tracking_78_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.603, 0.295]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.421, 0.202]\nB: [0.936, 0.193]\nC: [0.836, 0.093]\nD: [0.892, 0.905]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.415, 0.336]\nB: [0.147, 0.444]\nC: [0.469, 0.996]\nD: [0.759, 0.125]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_79_0.jpg", "2D-spatial/point_tracking/point_tracking_79_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.415, 0.336]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.415, 0.336]\nB: [0.147, 0.444]\nC: [0.469, 0.996]\nD: [0.759, 0.125]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.001, 0.519]\nC: [0.21, 0.901]\nD: [0.72, 0.872]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_80_0.jpg", "2D-spatial/point_tracking/point_tracking_80_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.001, 0.519]\nC: [0.21, 0.901]\nD: [0.72, 0.872]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.188, 0.294]\nB: [0.08, 0.837]\nC: [0.878, 0.923]\nD: [0.39, 0.215]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_81_0.jpg", "2D-spatial/point_tracking/point_tracking_81_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.39, 0.215]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.188, 0.294]\nB: [0.08, 0.837]\nC: [0.878, 0.923]\nD: [0.39, 0.215]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.725, 0.505]\nB: [0.825, 0.634]\nC: [0.772, 0.85]\nD: [0.521, 0.137]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_82_0.jpg", "2D-spatial/point_tracking/point_tracking_82_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.423, 0.126]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.725, 0.505]\nB: [0.825, 0.634]\nC: [0.772, 0.85]\nD: [0.521, 0.137]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.937, 0.437]\nB: [0.932, 0.955]\nC: [0.443, 0.473]\nD: [0.57, -0.021]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_83_0.jpg", "2D-spatial/point_tracking/point_tracking_83_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.379, -0.029]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.937, 0.437]\nB: [0.932, 0.955]\nC: [0.443, 0.473]\nD: [0.57, -0.021]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.482, 0.199]\nB: [0.043, 0.981]\nC: [0.419, 0.373]\nD: [0.325, 0.861]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_84_0.jpg", "2D-spatial/point_tracking/point_tracking_84_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.482, 0.199]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.482, 0.199]\nB: [0.043, 0.981]\nC: [0.419, 0.373]\nD: [0.325, 0.861]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.27, 0.844]\nB: [0.082, -0.197]\nC: [0.942, 0.56]\nD: [0.625, 0.212]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_85_0.jpg", "2D-spatial/point_tracking/point_tracking_85_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.314, -0.235]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.27, 0.844]\nB: [0.082, -0.197]\nC: [0.942, 0.56]\nD: [0.625, 0.212]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.493, 0.952]\nB: [0.403, 0.455]\nC: [0.764, 0.389]\nD: [0.3, 0.08]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_86_0.jpg", "2D-spatial/point_tracking/point_tracking_86_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.442, 0.361]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.493, 0.952]\nB: [0.403, 0.455]\nC: [0.764, 0.389]\nD: [0.3, 0.08]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [-0.038, 0.253]\nB: [0.77, 0.338]\nC: [0.766, 0.061]\nD: [0.958, 0.882]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_87_0.jpg", "2D-spatial/point_tracking/point_tracking_87_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.028, 0.3]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [-0.038, 0.253]\nB: [0.77, 0.338]\nC: [0.766, 0.061]\nD: [0.958, 0.882]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.005, 0.571]\nB: [0.168, 0.518]\nC: [0.523, 0.466]\nD: [0.784, 0.541]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_88_0.jpg", "2D-spatial/point_tracking/point_tracking_88_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.553, 0.401]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.005, 0.571]\nB: [0.168, 0.518]\nC: [0.523, 0.466]\nD: [0.784, 0.541]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.256, 0.018]\nB: [0.492, 0.583]\nC: [0.579, 0.753]\nD: [0.756, 0.803]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_89_0.jpg", "2D-spatial/point_tracking/point_tracking_89_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.536, 0.384]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.256, 0.018]\nB: [0.492, 0.583]\nC: [0.579, 0.753]\nD: [0.756, 0.803]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.4, 0.819]\nB: [0.315, 0.418]\nC: [0.695, 0.574]\nD: [0.934, 0.028]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_90_0.jpg", "2D-spatial/point_tracking/point_tracking_90_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.315, 0.418]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.4, 0.819]\nB: [0.315, 0.418]\nC: [0.695, 0.574]\nD: [0.934, 0.028]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.161, 0.915]\nB: [0.55, -0.109]\nC: [0.025, 0.306]\nD: [0.859, 0.383]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_91_0.jpg", "2D-spatial/point_tracking/point_tracking_91_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.556, -0.112]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.161, 0.915]\nB: [0.55, -0.109]\nC: [0.025, 0.306]\nD: [0.859, 0.383]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.796, 0.095]\nB: [0.902, 0.871]\nC: [0.454, 0.805]\nD: [0.399, 0.254]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_92_0.jpg", "2D-spatial/point_tracking/point_tracking_92_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.336, 0.127]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.796, 0.095]\nB: [0.902, 0.871]\nC: [0.454, 0.805]\nD: [0.399, 0.254]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.78, 0.578]\nB: [0.586, 0.492]\nC: [0.362, 0.862]\nD: [0.308, 0.418]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_93_0.jpg", "2D-spatial/point_tracking/point_tracking_93_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.516, 0.501]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.78, 0.578]\nB: [0.586, 0.492]\nC: [0.362, 0.862]\nD: [0.308, 0.418]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.771, 0.142]\nB: [0.516, 0.41]\nC: [0.068, 0.844]\nD: [0.331, 0.532]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_94_0.jpg", "2D-spatial/point_tracking/point_tracking_94_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.771, 0.142]\nB: [0.516, 0.41]\nC: [0.068, 0.844]\nD: [0.331, 0.532]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.53, 0.297]\nC: [0.365, 0.027]\nD: [0.781, 0.768]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_95_0.jpg", "2D-spatial/point_tracking/point_tracking_95_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.372, 0.327]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.53, 0.297]\nC: [0.365, 0.027]\nD: [0.781, 0.768]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.198, 0.526]\nB: [0.435, 0.603]\nC: [0.508, 0.551]\nD: [0.55, 0.363]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_96_0.jpg", "2D-spatial/point_tracking/point_tracking_96_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.508, 0.551]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.198, 0.526]\nB: [0.435, 0.603]\nC: [0.508, 0.551]\nD: [0.55, 0.363]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.12, 0.762]\nB: [0.674, 0.29]\nC: [0.557, 0.641]\nD: [0.055, 0.586]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_97_0.jpg", "2D-spatial/point_tracking/point_tracking_97_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.597, 0.28]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.12, 0.762]\nB: [0.674, 0.29]\nC: [0.557, 0.641]\nD: [0.055, 0.586]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.064, 0.785]\nB: [0.378, 0.667]\nC: [0.522, 0.235]\nD: [0.437, 0.118]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_98_0.jpg", "2D-spatial/point_tracking/point_tracking_98_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.378, 0.667]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.064, 0.785]\nB: [0.378, 0.667]\nC: [0.522, 0.235]\nD: [0.437, 0.118]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.875, 0.931]\nB: [0.087, 0.702]\nC: [0.508, 0.69]\nD: [0.046, 0.524]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_99_0.jpg", "2D-spatial/point_tracking/point_tracking_99_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.251, 0.5]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.875, 0.931]\nB: [0.087, 0.702]\nC: [0.508, 0.69]\nD: [0.046, 0.524]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.449, 0.349]\nB: [0.497, 0.606]\nC: [0.545, 0.303]\nD: [0.125, 0.458]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_100_0.jpg", "2D-spatial/point_tracking/point_tracking_100_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.527, 0.379]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.449, 0.349]\nB: [0.497, 0.606]\nC: [0.545, 0.303]\nD: [0.125, 0.458]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.276, 0.406]\nB: [0.94, 0.417]\nC: [0.807, 0.617]\nD: [0.151, 0.326]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_101_0.jpg", "2D-spatial/point_tracking/point_tracking_101_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.266, 0.607]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.276, 0.406]\nB: [0.94, 0.417]\nC: [0.807, 0.617]\nD: [0.151, 0.326]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.851, 0.365]\nB: [0.558, 0.074]\nC: [0.378, 0.002]\nD: [0.075, 0.676]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_102_0.jpg", "2D-spatial/point_tracking/point_tracking_102_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.852, 0.365]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.851, 0.365]\nB: [0.558, 0.074]\nC: [0.378, 0.002]\nD: [0.075, 0.676]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.515, 0.178]\nC: [0.197, 0.534]\nD: [0.536, 0.497]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_103_0.jpg", "2D-spatial/point_tracking/point_tracking_103_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 910 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.515, 0.178]\nC: [0.197, 0.534]\nD: [0.536, 0.497]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.133, 0.966]\nB: [0.167, 0.473]\nC: [0.808, 0.497]\nD: [0.597, 0.39]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_104_0.jpg", "2D-spatial/point_tracking/point_tracking_104_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.502, 0.304]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.133, 0.966]\nB: [0.167, 0.473]\nC: [0.808, 0.497]\nD: [0.597, 0.39]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.635, 0.971]\nB: [0.243, 0.351]\nC: [0.0, 0.0]\nD: [0.995, 0.403]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_105_0.jpg", "2D-spatial/point_tracking/point_tracking_105_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.635, 0.971]\nB: [0.243, 0.351]\nC: [0.0, 0.0]\nD: [0.995, 0.403]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.766, 0.625]\nC: [0.702, 0.537]\nD: [0.4, 0.901]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_106_0.jpg", "2D-spatial/point_tracking/point_tracking_106_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.766, 0.625]\nC: [0.702, 0.537]\nD: [0.4, 0.901]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.04, 0.521]\nB: [0.013, 0.863]\nC: [0.041, 0.677]\nD: [0.471, 0.865]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_107_0.jpg", "2D-spatial/point_tracking/point_tracking_107_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.554, 0.401]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.04, 0.521]\nB: [0.013, 0.863]\nC: [0.041, 0.677]\nD: [0.471, 0.865]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.422, 0.094]\nB: [0.036, 0.241]\nC: [0.832, 0.759]\nD: [0.084, 0.371]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_108_0.jpg", "2D-spatial/point_tracking/point_tracking_108_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.016, 0.253]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.422, 0.094]\nB: [0.036, 0.241]\nC: [0.832, 0.759]\nD: [0.084, 0.371]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.435, 0.035]\nB: [0.758, 0.725]\nC: [0.428, 0.944]\nD: [0.191, 0.586]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_109_0.jpg", "2D-spatial/point_tracking/point_tracking_109_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.758, 0.725]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.435, 0.035]\nB: [0.758, 0.725]\nC: [0.428, 0.944]\nD: [0.191, 0.586]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.486, 0.472]\nB: [0.0, 0.409]\nC: [0.679, 0.71]\nD: [0.474, 0.443]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_110_0.jpg", "2D-spatial/point_tracking/point_tracking_110_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.383, 0.481]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.486, 0.472]\nB: [0.0, 0.409]\nC: [0.679, 0.71]\nD: [0.474, 0.443]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.832, 0.16]\nB: [0.767, 0.295]\nC: [0.238, 0.998]\nD: [0.231, 0.345]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_111_0.jpg", "2D-spatial/point_tracking/point_tracking_111_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.278, 0.312]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.832, 0.16]\nB: [0.767, 0.295]\nC: [0.238, 0.998]\nD: [0.231, 0.345]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.98, 0.565]\nB: [0.053, 0.674]\nC: [0.564, 0.876]\nD: [0.452, 0.539]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_112_0.jpg", "2D-spatial/point_tracking/point_tracking_112_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.98, 0.565]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.98, 0.565]\nB: [0.053, 0.674]\nC: [0.564, 0.876]\nD: [0.452, 0.539]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.335, 0.563]\nB: [0.88, 0.001]\nC: [0.119, 0.693]\nD: [0.484, 0.412]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_113_0.jpg", "2D-spatial/point_tracking/point_tracking_113_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.521, 0.426]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.335, 0.563]\nB: [0.88, 0.001]\nC: [0.119, 0.693]\nD: [0.484, 0.412]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.626, 0.275]\nB: [0.815, 0.877]\nC: [0.004, 0.083]\nD: [0.871, 0.172]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_114_0.jpg", "2D-spatial/point_tracking/point_tracking_114_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.631, 0.278]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.626, 0.275]\nB: [0.815, 0.877]\nC: [0.004, 0.083]\nD: [0.871, 0.172]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.017, 0.757]\nB: [0.637, 0.134]\nC: [0.823, 0.303]\nD: [0.415, 0.038]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_115_0.jpg", "2D-spatial/point_tracking/point_tracking_115_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.475, 0.03]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.017, 0.757]\nB: [0.637, 0.134]\nC: [0.823, 0.303]\nD: [0.415, 0.038]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.278, 0.07]\nB: [0.287, 0.704]\nC: [0.387, 0.197]\nD: [0.443, 0.105]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_116_0.jpg", "2D-spatial/point_tracking/point_tracking_116_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.414, -0.045]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.278, 0.07]\nB: [0.287, 0.704]\nC: [0.387, 0.197]\nD: [0.443, 0.105]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.243, 0.44]\nB: [0.089, 0.367]\nC: [0.322, 0.069]\nD: [0.126, 0.424]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_117_0.jpg", "2D-spatial/point_tracking/point_tracking_117_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.126, 0.424]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.243, 0.44]\nB: [0.089, 0.367]\nC: [0.322, 0.069]\nD: [0.126, 0.424]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.878, 0.91]\nB: [0.509, 0.022]\nC: [0.259, 0.162]\nD: [0.213, 0.977]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_118_0.jpg", "2D-spatial/point_tracking/point_tracking_118_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.265, 0.16]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.878, 0.91]\nB: [0.509, 0.022]\nC: [0.259, 0.162]\nD: [0.213, 0.977]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.451, 0.674]\nB: [0.529, 0.336]\nC: [0.137, 0.847]\nD: [0.081, 0.187]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_119_0.jpg", "2D-spatial/point_tracking/point_tracking_119_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.529, 0.336]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.451, 0.674]\nB: [0.529, 0.336]\nC: [0.137, 0.847]\nD: [0.081, 0.187]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.354, 0.137]\nB: [0.831, 0.926]\nC: [0.473, 0.743]\nD: [0.228, 0.73]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_120_0.jpg", "2D-spatial/point_tracking/point_tracking_120_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.473, 0.743]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.354, 0.137]\nB: [0.831, 0.926]\nC: [0.473, 0.743]\nD: [0.228, 0.73]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.703, 0.241]\nB: [0.985, 0.235]\nC: [0.439, 0.494]\nD: [0.614, 0.184]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_121_0.jpg", "2D-spatial/point_tracking/point_tracking_121_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.703, 0.241]\nB: [0.985, 0.235]\nC: [0.439, 0.494]\nD: [0.614, 0.184]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.035, 0.992]\nB: [0.994, 0.321]\nC: [0.839, 0.258]\nD: [0.414, 0.367]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_122_0.jpg", "2D-spatial/point_tracking/point_tracking_122_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.306, 0.334]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.035, 0.992]\nB: [0.994, 0.321]\nC: [0.839, 0.258]\nD: [0.414, 0.367]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.613, 0.309]\nB: [0.457, 0.931]\nC: [0.669, 0.383]\nD: [0.938, 0.837]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_123_0.jpg", "2D-spatial/point_tracking/point_tracking_123_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.602, 0.319]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.613, 0.309]\nB: [0.457, 0.931]\nC: [0.669, 0.383]\nD: [0.938, 0.837]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.533, 0.568]\nB: [0.394, 0.545]\nC: [0.429, 0.604]\nD: [0.299, 0.66]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_124_0.jpg", "2D-spatial/point_tracking/point_tracking_124_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.533, 0.568]\nB: [0.394, 0.545]\nC: [0.429, 0.604]\nD: [0.299, 0.66]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.418, 0.337]\nB: [0.703, 0.614]\nC: [0.256, 0.811]\nD: [0.753, 0.192]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_125_0.jpg", "2D-spatial/point_tracking/point_tracking_125_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.419, 0.337]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.418, 0.337]\nB: [0.703, 0.614]\nC: [0.256, 0.811]\nD: [0.753, 0.192]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.451, 0.63]\nB: [0.161, 0.672]\nC: [0.117, 0.38]\nD: [0.918, 0.717]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_126_0.jpg", "2D-spatial/point_tracking/point_tracking_126_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.161, 0.672]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.451, 0.63]\nB: [0.161, 0.672]\nC: [0.117, 0.38]\nD: [0.918, 0.717]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.487, 0.784]\nB: [0.155, 0.004]\nC: [0.336, 0.564]\nD: [0.045, 0.917]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_127_0.jpg", "2D-spatial/point_tracking/point_tracking_127_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.616, 0.544]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.487, 0.784]\nB: [0.155, 0.004]\nC: [0.336, 0.564]\nD: [0.045, 0.917]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.352, 0.409]\nB: [0.959, 0.481]\nC: [0.373, 0.245]\nD: [0.977, 0.091]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_128_0.jpg", "2D-spatial/point_tracking/point_tracking_128_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.352, 0.409]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.352, 0.409]\nB: [0.959, 0.481]\nC: [0.373, 0.245]\nD: [0.977, 0.091]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.859, 0.311]\nB: [0.589, 0.682]\nC: [0.306, 0.308]\nD: [0.219, 0.979]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_129_0.jpg", "2D-spatial/point_tracking/point_tracking_129_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.304, 0.307]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.859, 0.311]\nB: [0.589, 0.682]\nC: [0.306, 0.308]\nD: [0.219, 0.979]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.472, 0.938]\nB: [0.873, 0.948]\nC: [0.511, 0.28]\nD: [0.829, 0.346]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_130_0.jpg", "2D-spatial/point_tracking/point_tracking_130_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.472, 0.938]\nB: [0.873, 0.948]\nC: [0.511, 0.28]\nD: [0.829, 0.346]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.446, 0.638]\nB: [0.628, 0.456]\nC: [0.455, 0.627]\nD: [0.379, 0.405]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_131_0.jpg", "2D-spatial/point_tracking/point_tracking_131_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.56, 0.467]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.446, 0.638]\nB: [0.628, 0.456]\nC: [0.455, 0.627]\nD: [0.379, 0.405]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.322, 0.321]\nB: [0.15, 0.133]\nC: [0.989, 0.972]\nD: [0.16, 0.862]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_132_0.jpg", "2D-spatial/point_tracking/point_tracking_132_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.232, 0.188]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.322, 0.321]\nB: [0.15, 0.133]\nC: [0.989, 0.972]\nD: [0.16, 0.862]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.931, 0.959]\nB: [0.506, 0.286]\nC: [0.391, 0.531]\nD: [0.469, 0.383]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_133_0.jpg", "2D-spatial/point_tracking/point_tracking_133_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.465, 0.381]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.931, 0.959]\nB: [0.506, 0.286]\nC: [0.391, 0.531]\nD: [0.469, 0.383]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.109, 0.806]\nB: [0.197, 0.457]\nC: [0.203, 0.114]\nD: [0.135, 0.938]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_134_0.jpg", "2D-spatial/point_tracking/point_tracking_134_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.2, 0.047]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.109, 0.806]\nB: [0.197, 0.457]\nC: [0.203, 0.114]\nD: [0.135, 0.938]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.24, 0.833]\nB: [0.308, 0.425]\nC: [0.339, 0.639]\nD: [0.077, 0.998]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_135_0.jpg", "2D-spatial/point_tracking/point_tracking_135_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.24, 0.833]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.24, 0.833]\nB: [0.308, 0.425]\nC: [0.339, 0.639]\nD: [0.077, 0.998]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.721, 0.606]\nC: [0.121, 0.428]\nD: [0.252, 0.486]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_136_0.jpg", "2D-spatial/point_tracking/point_tracking_136_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.496, 0.539]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.721, 0.606]\nC: [0.121, 0.428]\nD: [0.252, 0.486]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.675, 0.84]\nB: [0.087, 0.791]\nC: [0.736, 0.705]\nD: [0.092, 0.465]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_137_0.jpg", "2D-spatial/point_tracking/point_tracking_137_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.131, 0.527]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.675, 0.84]\nB: [0.087, 0.791]\nC: [0.736, 0.705]\nD: [0.092, 0.465]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.188, 0.925]\nB: [0.922, 0.115]\nC: [0.894, 0.22]\nD: [0.022, 0.091]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_138_0.jpg", "2D-spatial/point_tracking/point_tracking_138_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.195, 0.928]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.188, 0.925]\nB: [0.922, 0.115]\nC: [0.894, 0.22]\nD: [0.022, 0.091]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.962, 0.897]\nB: [0.754, 0.628]\nC: [0.384, 0.96]\nD: [0.784, 0.178]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_139_0.jpg", "2D-spatial/point_tracking/point_tracking_139_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.384, 0.96]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.962, 0.897]\nB: [0.754, 0.628]\nC: [0.384, 0.96]\nD: [0.784, 0.178]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.021, 0.739]\nB: [0.0, 0.0]\nC: [0.701, 0.818]\nD: [0.335, 0.057]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_140_0.jpg", "2D-spatial/point_tracking/point_tracking_140_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.021, 0.739]\nB: [0.0, 0.0]\nC: [0.701, 0.818]\nD: [0.335, 0.057]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.167, 0.345]\nB: [0.618, 0.201]\nC: [0.805, 0.514]\nD: [0.027, 0.731]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_141_0.jpg", "2D-spatial/point_tracking/point_tracking_141_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.609, 0.209]) in Image 1 within the Image 2? Note that the width of the input RGB image is 910 and the height is 480.", "context": "Select from the following choices.\nA: [0.167, 0.345]\nB: [0.618, 0.201]\nC: [0.805, 0.514]\nD: [0.027, 0.731]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.373, 0.459]\nB: [0.416, 0.278]\nC: [0.662, 0.648]\nD: [0.304, 0.781]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_142_0.jpg", "2D-spatial/point_tracking/point_tracking_142_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.443, 0.285]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.373, 0.459]\nB: [0.416, 0.278]\nC: [0.662, 0.648]\nD: [0.304, 0.781]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.53, 0.42]\nB: [0.638, 0.766]\nC: [0.517, 0.984]\nD: [0.344, 0.268]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_143_0.jpg", "2D-spatial/point_tracking/point_tracking_143_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.345, 0.268]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.53, 0.42]\nB: [0.638, 0.766]\nC: [0.517, 0.984]\nD: [0.344, 0.268]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.603, 0.414]\nB: [0.61, 0.464]\nC: [0.292, 0.626]\nD: [0.062, 0.813]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_144_0.jpg", "2D-spatial/point_tracking/point_tracking_144_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.602, 0.412]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.603, 0.414]\nB: [0.61, 0.464]\nC: [0.292, 0.626]\nD: [0.062, 0.813]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.493, 0.798]\nB: [0.963, 0.818]\nC: [0.245, 0.105]\nD: [0.982, 0.515]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_145_0.jpg", "2D-spatial/point_tracking/point_tracking_145_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.169, 0.075]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.493, 0.798]\nB: [0.963, 0.818]\nC: [0.245, 0.105]\nD: [0.982, 0.515]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.84, 0.083]\nB: [0.114, 0.077]\nC: [0.273, 0.23]\nD: [0.485, 0.534]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_146_0.jpg", "2D-spatial/point_tracking/point_tracking_146_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.443, 0.524]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.84, 0.083]\nB: [0.114, 0.077]\nC: [0.273, 0.23]\nD: [0.485, 0.534]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.345, 0.262]\nB: [0.512, 0.224]\nC: [0.657, 0.276]\nD: [0.166, 0.841]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_147_0.jpg", "2D-spatial/point_tracking/point_tracking_147_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.501, 0.22]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.345, 0.262]\nB: [0.512, 0.224]\nC: [0.657, 0.276]\nD: [0.166, 0.841]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.357, 0.196]\nB: [0.42, 0.234]\nC: [0.718, 0.336]\nD: [0.573, 0.896]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_148_0.jpg", "2D-spatial/point_tracking/point_tracking_148_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.357, 0.196]\nB: [0.42, 0.234]\nC: [0.718, 0.336]\nD: [0.573, 0.896]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.793, 0.03]\nB: [0.879, 0.871]\nC: [0.781, 0.418]\nD: [0.549, 0.338]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_149_0.jpg", "2D-spatial/point_tracking/point_tracking_149_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.53, 0.332]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.793, 0.03]\nB: [0.879, 0.871]\nC: [0.781, 0.418]\nD: [0.549, 0.338]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.342, 0.072]\nB: [0.574, 0.028]\nC: [0.795, 0.301]\nD: [0.752, 0.99]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_150_0.jpg", "2D-spatial/point_tracking/point_tracking_150_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.342, 0.072]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.342, 0.072]\nB: [0.574, 0.028]\nC: [0.795, 0.301]\nD: [0.752, 0.99]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.082, 0.932]\nB: [0.262, 0.046]\nC: [0.434, 0.576]\nD: [0.686, 0.437]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_151_0.jpg", "2D-spatial/point_tracking/point_tracking_151_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.082, 0.932]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.082, 0.932]\nB: [0.262, 0.046]\nC: [0.434, 0.576]\nD: [0.686, 0.437]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.053, 0.623]\nB: [0.624, 0.428]\nC: [0.518, 0.784]\nD: [0.141, 0.376]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_152_0.jpg", "2D-spatial/point_tracking/point_tracking_152_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.624, 0.428]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.053, 0.623]\nB: [0.624, 0.428]\nC: [0.518, 0.784]\nD: [0.141, 0.376]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.059, 0.533]\nB: [0.697, 0.415]\nC: [0.114, 0.313]\nD: [0.328, 0.618]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_153_0.jpg", "2D-spatial/point_tracking/point_tracking_153_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.113, 0.313]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.059, 0.533]\nB: [0.697, 0.415]\nC: [0.114, 0.313]\nD: [0.328, 0.618]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.488, 0.838]\nB: [0.287, 0.106]\nC: [0.472, 0.074]\nD: [0.079, 0.354]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_154_0.jpg", "2D-spatial/point_tracking/point_tracking_154_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.572, -0.121]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.488, 0.838]\nB: [0.287, 0.106]\nC: [0.472, 0.074]\nD: [0.079, 0.354]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.631, 0.352]\nB: [0.646, 0.557]\nC: [0.682, 0.502]\nD: [0.586, 0.751]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_155_0.jpg", "2D-spatial/point_tracking/point_tracking_155_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.631, 0.352]\nB: [0.646, 0.557]\nC: [0.682, 0.502]\nD: [0.586, 0.751]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.125, 0.593]\nB: [0.518, 0.506]\nC: [0.515, 0.327]\nD: [0.285, 0.07]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_156_0.jpg", "2D-spatial/point_tracking/point_tracking_156_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.588, 0.496]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.125, 0.593]\nB: [0.518, 0.506]\nC: [0.515, 0.327]\nD: [0.285, 0.07]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.176, 0.766]\nB: [0.337, 0.765]\nC: [0.905, 0.67]\nD: [0.04, 0.456]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_157_0.jpg", "2D-spatial/point_tracking/point_tracking_157_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.04, 0.456]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.176, 0.766]\nB: [0.337, 0.765]\nC: [0.905, 0.67]\nD: [0.04, 0.456]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.232, 0.766]\nB: [0.161, 0.72]\nC: [0.323, 0.222]\nD: [0.795, 0.138]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_158_0.jpg", "2D-spatial/point_tracking/point_tracking_158_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.361, 0.266]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.232, 0.766]\nB: [0.161, 0.72]\nC: [0.323, 0.222]\nD: [0.795, 0.138]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.333, 0.389]\nB: [0.691, 0.301]\nC: [0.868, 0.47]\nD: [0.649, 0.094]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_159_0.jpg", "2D-spatial/point_tracking/point_tracking_159_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.333, 0.389]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.333, 0.389]\nB: [0.691, 0.301]\nC: [0.868, 0.47]\nD: [0.649, 0.094]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.412, 0.254]\nB: [0.803, 0.989]\nC: [0.898, 0.497]\nD: [0.43, 0.295]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_160_0.jpg", "2D-spatial/point_tracking/point_tracking_160_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.392, 0.302]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.412, 0.254]\nB: [0.803, 0.989]\nC: [0.898, 0.497]\nD: [0.43, 0.295]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.864, 0.427]\nC: [0.189, 0.222]\nD: [0.86, 0.108]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_161_0.jpg", "2D-spatial/point_tracking/point_tracking_161_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.864, 0.427]\nC: [0.189, 0.222]\nD: [0.86, 0.108]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.961, 0.515]\nB: [0.312, 0.682]\nC: [0.209, 0.16]\nD: [0.943, 0.395]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_162_0.jpg", "2D-spatial/point_tracking/point_tracking_162_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.181, 0.189]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.961, 0.515]\nB: [0.312, 0.682]\nC: [0.209, 0.16]\nD: [0.943, 0.395]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.606, 0.797]\nB: [0.0, 0.0]\nC: [0.538, 0.287]\nD: [0.14, 0.104]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_163_0.jpg", "2D-spatial/point_tracking/point_tracking_163_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.606, 0.797]\nB: [0.0, 0.0]\nC: [0.538, 0.287]\nD: [0.14, 0.104]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.289, 0.952]\nB: [0.872, 0.205]\nC: [0.0, 0.0]\nD: [0.633, 0.427]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_164_0.jpg", "2D-spatial/point_tracking/point_tracking_164_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.289, 0.952]\nB: [0.872, 0.205]\nC: [0.0, 0.0]\nD: [0.633, 0.427]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.47, 0.37]\nB: [0.35, 0.4]\nC: [0.042, 0.785]\nD: [0.081, 0.262]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_165_0.jpg", "2D-spatial/point_tracking/point_tracking_165_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.351, 0.401]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.47, 0.37]\nB: [0.35, 0.4]\nC: [0.042, 0.785]\nD: [0.081, 0.262]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.161, 0.071]\nB: [0.948, 0.753]\nC: [0.387, 0.629]\nD: [0.408, 0.774]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_166_0.jpg", "2D-spatial/point_tracking/point_tracking_166_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.718, 0.256]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.161, 0.071]\nB: [0.948, 0.753]\nC: [0.387, 0.629]\nD: [0.408, 0.774]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.18, 0.986]\nB: [0.148, 0.12]\nC: [0.474, 0.356]\nD: [0.634, 0.061]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_167_0.jpg", "2D-spatial/point_tracking/point_tracking_167_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.551, 0.394]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.18, 0.986]\nB: [0.148, 0.12]\nC: [0.474, 0.356]\nD: [0.634, 0.061]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.642, 0.55]\nB: [0.894, 0.525]\nC: [0.887, 0.681]\nD: [0.583, 0.912]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_168_0.jpg", "2D-spatial/point_tracking/point_tracking_168_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.724, 0.512]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.642, 0.55]\nB: [0.894, 0.525]\nC: [0.887, 0.681]\nD: [0.583, 0.912]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.987, 0.403]\nB: [0.465, 0.446]\nC: [0.05, 0.858]\nD: [0.457, 0.194]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_169_0.jpg", "2D-spatial/point_tracking/point_tracking_169_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.504, 0.202]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.987, 0.403]\nB: [0.465, 0.446]\nC: [0.05, 0.858]\nD: [0.457, 0.194]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.852, 0.571]\nB: [0.771, 0.593]\nC: [0.19, 0.794]\nD: [0.512, 0.314]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_170_0.jpg", "2D-spatial/point_tracking/point_tracking_170_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.513, 0.314]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.852, 0.571]\nB: [0.771, 0.593]\nC: [0.19, 0.794]\nD: [0.512, 0.314]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.998, 0.808]\nB: [0.0, 0.0]\nC: [0.98, 0.396]\nD: [0.419, 0.553]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_171_0.jpg", "2D-spatial/point_tracking/point_tracking_171_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.998, 0.808]\nB: [0.0, 0.0]\nC: [0.98, 0.396]\nD: [0.419, 0.553]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.52, 0.284]\nB: [0.475, 0.251]\nC: [0.321, 0.629]\nD: [0.432, 0.371]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_172_0.jpg", "2D-spatial/point_tracking/point_tracking_172_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.427, 0.372]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.52, 0.284]\nB: [0.475, 0.251]\nC: [0.321, 0.629]\nD: [0.432, 0.371]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.781, 0.578]\nC: [0.642, 0.382]\nD: [0.679, 0.324]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_173_0.jpg", "2D-spatial/point_tracking/point_tracking_173_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.751, 0.277]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.781, 0.578]\nC: [0.642, 0.382]\nD: [0.679, 0.324]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.525, 0.662]\nB: [0.774, 0.504]\nC: [0.263, 0.754]\nD: [0.896, 0.303]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_174_0.jpg", "2D-spatial/point_tracking/point_tracking_174_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.525, 0.662]\nB: [0.774, 0.504]\nC: [0.263, 0.754]\nD: [0.896, 0.303]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.336, 0.241]\nB: [0.754, 0.592]\nC: [0.711, 0.154]\nD: [0.814, 0.269]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_175_0.jpg", "2D-spatial/point_tracking/point_tracking_175_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.711, 0.154]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.336, 0.241]\nB: [0.754, 0.592]\nC: [0.711, 0.154]\nD: [0.814, 0.269]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.357, 0.26]\nB: [0.145, 0.457]\nC: [0.26, 0.791]\nD: [0.896, 0.054]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_176_0.jpg", "2D-spatial/point_tracking/point_tracking_176_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.357, 0.259]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.357, 0.26]\nB: [0.145, 0.457]\nC: [0.26, 0.791]\nD: [0.896, 0.054]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.0, 0.0]\nB: [0.249, 0.178]\nC: [0.969, 0.236]\nD: [0.363, 0.049]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_177_0.jpg", "2D-spatial/point_tracking/point_tracking_177_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.509, 0.617]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.0, 0.0]\nB: [0.249, 0.178]\nC: [0.969, 0.236]\nD: [0.363, 0.049]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.396, 0.165]\nB: [0.966, 0.511]\nC: [0.101, 0.549]\nD: [0.871, 0.899]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_178_0.jpg", "2D-spatial/point_tracking/point_tracking_178_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 910 and the height is 480.", "context": "Select from the following choices.\nA: [0.396, 0.165]\nB: [0.966, 0.511]\nC: [0.101, 0.549]\nD: [0.871, 0.899]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.335, 0.835]\nB: [0.526, 0.468]\nC: [0.441, 0.847]\nD: [0.584, 0.202]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_179_0.jpg", "2D-spatial/point_tracking/point_tracking_179_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.491, 0.453]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.335, 0.835]\nB: [0.526, 0.468]\nC: [0.441, 0.847]\nD: [0.584, 0.202]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.352, 0.43]\nB: [0.396, 0.842]\nC: [0.544, 0.168]\nD: [0.755, 0.432]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_180_0.jpg", "2D-spatial/point_tracking/point_tracking_180_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.352, 0.43]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.352, 0.43]\nB: [0.396, 0.842]\nC: [0.544, 0.168]\nD: [0.755, 0.432]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.738, 0.079]\nB: [0.295, 0.566]\nC: [0.04, 0.229]\nD: [0.771, 0.673]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_181_0.jpg", "2D-spatial/point_tracking/point_tracking_181_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.292, 0.642]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.738, 0.079]\nB: [0.295, 0.566]\nC: [0.04, 0.229]\nD: [0.771, 0.673]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.888, 0.387]\nB: [0.016, 0.294]\nC: [0.918, 0.591]\nD: [0.308, 0.501]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_182_0.jpg", "2D-spatial/point_tracking/point_tracking_182_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.888, 0.387]\nB: [0.016, 0.294]\nC: [0.918, 0.591]\nD: [0.308, 0.501]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.383, 0.798]\nB: [0.668, 0.133]\nC: [0.133, 0.739]\nD: [0.192, 0.076]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_183_0.jpg", "2D-spatial/point_tracking/point_tracking_183_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.383, 0.798]\nB: [0.668, 0.133]\nC: [0.133, 0.739]\nD: [0.192, 0.076]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.634, 0.284]\nB: [0.0, 0.0]\nC: [0.315, 0.604]\nD: [0.141, 0.357]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_184_0.jpg", "2D-spatial/point_tracking/point_tracking_184_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.634, 0.284]\nB: [0.0, 0.0]\nC: [0.315, 0.604]\nD: [0.141, 0.357]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.691, 0.879]\nB: [0.362, 0.72]\nC: [0.157, 0.764]\nD: [0.272, 0.551]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_185_0.jpg", "2D-spatial/point_tracking/point_tracking_185_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.272, 0.551]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.691, 0.879]\nB: [0.362, 0.72]\nC: [0.157, 0.764]\nD: [0.272, 0.551]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.448, 0.266]\nB: [0.5, 0.567]\nC: [0.943, 0.037]\nD: [0.019, 0.535]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_186_0.jpg", "2D-spatial/point_tracking/point_tracking_186_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.448, 0.266]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.448, 0.266]\nB: [0.5, 0.567]\nC: [0.943, 0.037]\nD: [0.019, 0.535]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.664, 0.291]\nB: [0.629, 0.96]\nC: [0.638, 0.438]\nD: [0.072, 0.128]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_187_0.jpg", "2D-spatial/point_tracking/point_tracking_187_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.59, 0.45]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.664, 0.291]\nB: [0.629, 0.96]\nC: [0.638, 0.438]\nD: [0.072, 0.128]"}, "output": {"output_text": "C"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.628, 0.379]\nB: [0.793, 0.079]\nC: [0.084, 0.828]\nD: [0.959, 0.595]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_188_0.jpg", "2D-spatial/point_tracking/point_tracking_188_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.959, 0.595]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.628, 0.379]\nB: [0.793, 0.079]\nC: [0.084, 0.828]\nD: [0.959, 0.595]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.086, 0.897]\nB: [0.891, 0.598]\nC: [0.731, 0.612]\nD: [0.338, -0.004]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_189_0.jpg", "2D-spatial/point_tracking/point_tracking_189_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.417, 0.005]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.086, 0.897]\nB: [0.891, 0.598]\nC: [0.731, 0.612]\nD: [0.338, -0.004]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.314, 0.635]\nB: [0.437, 0.344]\nC: [0.11, 0.731]\nD: [0.763, 0.089]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_190_0.jpg", "2D-spatial/point_tracking/point_tracking_190_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.437, 0.344]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.314, 0.635]\nB: [0.437, 0.344]\nC: [0.11, 0.731]\nD: [0.763, 0.089]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.303, 0.199]\nB: [0.353, 0.651]\nC: [0.302, 0.987]\nD: [0.305, 0.316]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_191_0.jpg", "2D-spatial/point_tracking/point_tracking_191_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.0, 0.0]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.303, 0.199]\nB: [0.353, 0.651]\nC: [0.302, 0.987]\nD: [0.305, 0.316]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.145, 0.87]\nB: [0.947, 0.301]\nC: [0.046, 0.995]\nD: [0.0, 0.0]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_192_0.jpg", "2D-spatial/point_tracking/point_tracking_192_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.465, 0.564]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.145, 0.87]\nB: [0.947, 0.301]\nC: [0.046, 0.995]\nD: [0.0, 0.0]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.622, 0.432]\nB: [0.421, 0.201]\nC: [0.707, 0.491]\nD: [0.55, 0.329]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_193_0.jpg", "2D-spatial/point_tracking/point_tracking_193_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.513, 0.53]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.622, 0.432]\nB: [0.421, 0.201]\nC: [0.707, 0.491]\nD: [0.55, 0.329]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.556, 0.744]\nB: [0.085, 0.886]\nC: [0.475, 0.451]\nD: [0.417, 0.52]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_194_0.jpg", "2D-spatial/point_tracking/point_tracking_194_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.735, 0.381]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.556, 0.744]\nB: [0.085, 0.886]\nC: [0.475, 0.451]\nD: [0.417, 0.52]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_davis", "options": "A: [0.089, 0.936]\nB: [0.642, 0.328]\nC: [0.611, 0.959]\nD: [0.166, 0.377]", "visual_input_component": ["natural_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_195_0.jpg", "2D-spatial/point_tracking/point_tracking_195_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.114, 0.302]) in Image 1 within the Image 2? Note that the width of the input RGB image is 854 and the height is 480.", "context": "Select from the following choices.\nA: [0.089, 0.936]\nB: [0.642, 0.328]\nC: [0.611, 0.959]\nD: [0.166, 0.377]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.443, 0.616]\nB: [0.663, 0.356]\nC: [0.079, 0.21]\nD: [0.586, -0.124]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_196_0.jpg", "2D-spatial/point_tracking/point_tracking_196_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.733, -0.02]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.443, 0.616]\nB: [0.663, 0.356]\nC: [0.079, 0.21]\nD: [0.586, -0.124]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.385, 0.321]\nB: [0.931, 0.242]\nC: [0.011, 0.867]\nD: [0.917, 0.788]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_197_0.jpg", "2D-spatial/point_tracking/point_tracking_197_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.385, 0.321]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.385, 0.321]\nB: [0.931, 0.242]\nC: [0.011, 0.867]\nD: [0.917, 0.788]"}, "output": {"output_text": "A"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.757, 0.024]\nB: [0.333, -0.045]\nC: [0.773, 0.154]\nD: [0.253, 0.821]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_198_0.jpg", "2D-spatial/point_tracking/point_tracking_198_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.314, -0.005]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.757, 0.024]\nB: [0.333, -0.045]\nC: [0.773, 0.154]\nD: [0.253, 0.821]"}, "output": {"output_text": "B"}, "task": "point_tracking"}
{"source": "tapvid_rgb_stacking", "options": "A: [0.627, 0.508]\nB: [0.71, 0.649]\nC: [0.888, 0.125]\nD: [0.302, 0.307]", "visual_input_component": ["synthetic_image"], "input": {"input_image_path": ["2D-spatial/point_tracking/point_tracking_199_0.jpg", "2D-spatial/point_tracking/point_tracking_199_1.jpg"], "question": "What is the position coordinates of the point with coordinates ([0.302, 0.306]) in Image 1 within the Image 2? Note that the width of the input RGB image is 256 and the height is 256.", "context": "Select from the following choices.\nA: [0.627, 0.508]\nB: [0.71, 0.649]\nC: [0.888, 0.125]\nD: [0.302, 0.307]"}, "output": {"output_text": "D"}, "task": "point_tracking"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_0_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_0_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_0_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_0_3.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_1_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_1_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_1_2.png"], "question": "Which picture shows the water bottle inside the tent?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_2_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_2_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_2_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_2_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_2_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_2_5.png"], "question": "Which object is next to the block?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_3_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_3_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_3_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_3_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_3_4.png"], "question": "Which object is next to the one shaped like a cube?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_4_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_4_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_4_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_4_3.png"], "question": "Which object is next to the flashlight?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_5_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_5_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_5_2.png"], "question": "Which picture shows the piggy bank inside the gift box?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_6_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_6_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_6_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_6_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_6_4.png"], "question": "Which object is above the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_7_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_7_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_7_2.png"], "question": "Which object is shaped like a cone and is above the desk?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_8_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_8_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_8_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_8_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_8_4.png"], "question": "Which object is beside the dice?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_9_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_9_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_9_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_9_3.png"], "question": "Which object is next to the watermelon?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_10_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_10_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_10_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_10_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_10_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_10_5.png"], "question": "Which object is beside the one shaped like a cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_11_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_11_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_11_2.png"], "question": "Which picture shows the cake inside the oven?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_12_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_12_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_12_2.png"], "question": "Which object is below the bed?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_13_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_13_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_13_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_13_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_13_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_13_5.png"], "question": "Which object is beside the one shaped like a cube?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_14_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_14_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_14_2.png"], "question": "Which object is above the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_15_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_15_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_15_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_15_3.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_16_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_16_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_16_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_16_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_16_4.png"], "question": "Which object is beside the volleyball?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_17_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_17_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_17_2.png"], "question": "Which object is shaped like a sphere and is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_18_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_18_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_18_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_18_3.png"], "question": "Which object is next to the clock?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_19_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_19_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_19_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_19_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_19_4.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_20_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_20_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_20_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_21_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_21_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_21_2.png"], "question": "Which object is below the bed?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_22_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_22_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_22_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_22_3.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_23_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_23_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_23_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_23_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_23_4.png"], "question": "Which object is next to the pine cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_24_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_24_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_24_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_25_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_25_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_25_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_26_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_26_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_26_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_26_3.png"], "question": "Which object is above the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_27_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_27_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_27_2.png"], "question": "What is at the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_28_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_28_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_28_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_28_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_28_4.png"], "question": "Which object is beside the bead?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_29_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_29_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_29_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_29_3.png"], "question": "Which object is beside the crate?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_30_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_30_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_30_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_30_3.png"], "question": "Which object is next to the cup?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_31_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_31_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_31_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_31_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_31_4.png"], "question": "Which object is above the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_32_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_32_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_32_2.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_33_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_33_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_33_2.png"], "question": "Which picture shows the muffins outside the oven?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_34_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_34_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_34_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_34_3.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_35_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_35_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_35_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_36_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_36_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_36_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_36_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_36_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_36_5.png"], "question": "Which object is next to the dog dish?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_37_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_37_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_37_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_37_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_38_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_38_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_38_2.png"], "question": "Which object below the bed is shaped like a cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_39_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_39_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_39_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_39_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_40_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_40_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_40_2.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_41_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_41_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_41_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_41_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_42_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_42_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_42_2.png"], "question": "Which object is above the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_43_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_43_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_43_2.png"], "question": "Which picture shows the cow outside the barn?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_44_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_44_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_44_2.png"], "question": "Which picture shows the soccer ball outside the gift box?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_45_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_45_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_45_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_45_3.png"], "question": "Which object is next to the flashlight?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_46_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_46_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_46_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_46_3.png"], "question": "Which object is next to the watermelon?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_47_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_47_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_47_2.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_48_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_48_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_48_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_48_3.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_49_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_49_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_49_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_49_3.png"], "question": "Which object is next to the bead?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_50_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_50_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_50_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_50_3.png"], "question": "Which object is next to the clock?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_51_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_51_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_51_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_51_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_51_4.png"], "question": "Which object is beside the pair of shoes?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_52_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_52_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_52_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_52_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_52_4.png"], "question": "Which object is beside the one shaped like a sphere?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_53_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_53_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_53_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_53_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_53_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_53_5.png"], "question": "Which object is beside the tub of ice cream?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_54_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_54_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_54_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_54_3.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_55_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_55_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_55_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_55_3.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_56_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_56_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_56_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_56_3.png"], "question": "Which object is next to the block?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_57_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_57_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_57_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_57_3.png"], "question": "Which object is below the desk?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_58_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_58_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_58_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_58_3.png"], "question": "Which object is above the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_59_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_59_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_59_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_59_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_59_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_59_5.png"], "question": "Which object is next to the trash can?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_60_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_60_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_60_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_60_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_60_4.png"], "question": "Which object is beside the mailing box?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_61_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_61_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_61_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_61_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_62_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_62_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_62_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_62_3.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_63_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_63_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_63_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_63_3.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_64_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_64_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_64_2.png"], "question": "Which object is shaped like a sphere and is below the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_65_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_65_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_65_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_66_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_66_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_66_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_66_3.png"], "question": "Which object is next to the basketball?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_67_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_67_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_67_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_67_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_67_4.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_68_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_68_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_68_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_68_3.png"], "question": "Which object is next to the flashlight?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_69_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_69_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_69_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_69_3.png"], "question": "Which object is next to the pine cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_70_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_70_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_70_2.png"], "question": "What is at the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_71_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_71_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_71_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_71_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_71_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_71_5.png"], "question": "Which object is beside the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_72_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_72_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_72_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_72_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_72_4.png"], "question": "Which object is beside the one shaped like a cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_73_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_73_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_73_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_73_3.png"], "question": "What is at the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_74_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_74_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_74_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_74_3.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_75_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_75_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_75_2.png"], "question": "Which picture shows the roast beef inside the oven?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_76_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_76_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_76_2.png"], "question": "What is at the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_77_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_77_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_77_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_77_3.png"], "question": "Which object is next to the bead?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_78_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_78_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_78_2.png"], "question": "Which object is shaped like a cylinder and is below the bed?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_79_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_79_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_79_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_79_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_79_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_79_5.png"], "question": "Which object is next to the one shaped like a cube?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_80_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_80_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_80_2.png"], "question": "Which object below the bed is shaped like a cylinder?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_81_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_81_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_81_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_81_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_81_4.png"], "question": "Which object is beside the clock?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_82_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_82_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_82_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_83_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_83_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_83_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_83_3.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_84_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_84_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_84_2.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_85_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_85_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_85_2.png"], "question": "Which object is shaped like a cube and is above the bed?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_86_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_86_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_86_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_86_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_87_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_87_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_87_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_87_3.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_88_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_88_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_88_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_89_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_89_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_89_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_89_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_89_4.png"], "question": "Which object is next to the storage bin?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_90_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_90_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_90_2.png"], "question": "Which object is below the desk?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_91_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_91_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_91_2.png"], "question": "Which picture shows the basketball inside the gift box?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_92_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_92_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_92_2.png"], "question": "Which picture shows the toy airplane outside the gift box?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_93_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_93_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_93_2.png"], "question": "Which object is shaped like a sphere and is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_94_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_94_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_94_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_94_3.png"], "question": "Which object is beside the pine cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_95_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_95_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_95_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_95_3.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_96_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_96_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_96_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_96_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_96_4.png"], "question": "Which object is beside the box of cookies?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_97_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_97_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_97_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_97_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_97_4.png"], "question": "Which object is beside the computer?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_98_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_98_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_98_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_98_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_98_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_98_5.png"], "question": "Which object is beside the butterfly?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_99_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_99_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_99_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_99_3.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_100_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_100_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_100_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_100_3.png"], "question": "Which object is next to the flashlight?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_101_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_101_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_101_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_102_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_102_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_102_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_102_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_102_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_102_5.png"], "question": "Which object is beside the one shaped like a cylinder?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "E"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_103_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_103_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_103_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_103_3.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_104_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_104_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_104_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_104_3.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_105_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_105_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_105_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_106_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_106_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_106_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_107_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_107_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_107_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_108_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_108_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_108_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_108_3.png"], "question": "Which object is next to the pair of shoes?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_109_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_109_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_109_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_109_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_110_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_110_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_110_2.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_111_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_111_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_111_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_111_3.png"], "question": "Which object is below the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_112_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_112_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_112_2.png"], "question": "Which picture shows the book inside the gift box?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_113_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_113_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_113_2.png"], "question": "Which object is above the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_114_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_114_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_114_2.png"], "question": "Which object is above the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_115_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_115_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_115_2.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_116_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_116_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_116_2.png"], "question": "Which object above the table is shaped like a cylinder?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_117_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_117_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_117_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_118_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_118_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_118_2.png"], "question": "Which object above the desk is shaped like a cylinder?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_119_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_119_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_119_2.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_120_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_120_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_120_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_120_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_121_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_121_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_121_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_121_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_121_4.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_122_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_122_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_122_2.png"], "question": "Which object above the bench is shaped like a cube?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_123_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_123_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_123_2.png"], "question": "What is at the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_124_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_124_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_124_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_124_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_124_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_124_5.png"], "question": "Which object is next to the one shaped like a cube?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_125_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_125_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_125_2.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_126_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_126_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_126_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_127_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_127_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_127_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_128_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_128_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_128_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_128_3.png"], "question": "What is at the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_129_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_129_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_129_2.png"], "question": "Which object is shaped like a sphere and is above the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_130_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_130_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_130_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_130_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_131_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_131_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_131_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_131_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_131_4.png"], "question": "Which object is beside the drum?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_132_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_132_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_132_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_132_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_133_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_133_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_133_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_133_3.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_134_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_134_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_134_2.png"], "question": "Which object is shaped like a cone and is below the desk?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_135_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_135_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_135_2.png"], "question": "Which picture shows the toy pony outside the gift box?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_136_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_136_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_136_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_136_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_136_4.png"], "question": "Which object is beside the backpack?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_137_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_137_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_137_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_137_3.png"], "question": "Which object is next to the pair of shoes?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_138_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_138_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_138_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_138_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_138_4.png"], "question": "Which object is below the desk?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_139_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_139_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_139_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_139_3.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_140_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_140_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_140_2.png"], "question": "Which picture shows the toy car inside the toy box?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_141_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_141_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_141_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_141_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_141_4.png"], "question": "Which object is beside the one shaped like a cube?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_142_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_142_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_142_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_142_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_142_4.png"], "question": "Which object is next to the one shaped like a cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_143_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_143_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_143_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_143_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_143_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_143_5.png"], "question": "Which object is next to the one shaped like a cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_144_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_144_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_144_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_145_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_145_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_145_2.png"], "question": "Which object is above the bed?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_146_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_146_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_146_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_147_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_147_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_147_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_147_3.png"], "question": "Which object is below the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_148_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_148_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_148_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_148_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_148_4.png"], "question": "Which object is beside the butterfly?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_149_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_149_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_149_2.png"], "question": "Which object is shaped like a cylinder and is above the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_150_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_150_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_150_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_150_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_150_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_150_5.png"], "question": "Which object is next to the block?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_151_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_151_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_151_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_151_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_152_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_152_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_152_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_152_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_152_4.png"], "question": "Which object is beside the cake?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_153_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_153_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_153_2.png"], "question": "Which object is below the bed?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_154_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_154_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_154_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_155_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_155_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_155_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_155_3.png"], "question": "What is at the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_156_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_156_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_156_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_156_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_156_4.png"], "question": "Which object is beside the pair of shoes?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_157_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_157_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_157_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_157_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_158_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_158_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_158_2.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_159_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_159_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_159_2.png"], "question": "Which object is above the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_160_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_160_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_160_2.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_161_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_161_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_161_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_161_3.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_162_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_162_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_162_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_162_3.png"], "question": "Which object is next to the roll of stickers?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_163_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_163_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_163_2.png"], "question": "Which picture shows the roast beef inside the oven?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_164_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_164_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_164_2.png"], "question": "Which picture shows the cookies outside the oven?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_165_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_165_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_165_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_165_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_165_4.png"], "question": "Which object is next to the one shaped like a sphere?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_166_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_166_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_166_2.png"], "question": "Which object is below the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_167_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_167_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_167_2.png"], "question": "Which picture shows the muffins outside the oven?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_168_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_168_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_168_2.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_169_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_169_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_169_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_169_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_170_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_170_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_170_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_170_3.png"], "question": "Which object is beside the storage bin?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_171_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_171_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_171_2.png"], "question": "Which object is shaped like a sphere and is below the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_172_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_172_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_172_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_172_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_172_4.png"], "question": "Which object is next to the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_173_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_173_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_173_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_173_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_173_4.png"], "question": "Which object is beside the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_174_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_174_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_174_2.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_175_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_175_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_175_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_175_3.png"], "question": "Which object is next to the drum?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_176_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_176_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_176_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_176_3.png"], "question": "What is in the middle?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_177_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_177_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_177_2.png"], "question": "Which object is shaped like a cube and is below the bench?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_178_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_178_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_178_2.png"], "question": "Which object is below the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_179_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_179_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_179_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_179_3.png"], "question": "Which object is next to the clock?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_180_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_180_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_180_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_180_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_180_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_180_5.png"], "question": "Which object is beside the flashlight?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_181_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_181_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_181_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_181_3.png"], "question": "Which object is above the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_182_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_182_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_182_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_183_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_183_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_183_2.png"], "question": "Which object below the table is shaped like a sphere?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_184_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_184_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_184_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_184_3.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_185_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_185_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_185_2.png"], "question": "Which object above the bench is shaped like a cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_186_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_186_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_186_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_186_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_186_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_186_5.png"], "question": "Which object is beside the watermelon?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "E"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_187_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_187_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_187_2.png"], "question": "Which object below the bed is shaped like a cone?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_188_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_188_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_188_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_188_3.png"], "question": "Which object is next to the trash can?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_189_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_189_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_189_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_189_3.png"], "question": "What is on the right?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_190_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_190_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_190_2.png"], "question": "Which picture shows the cow outside the barn?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_191_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_191_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_191_2.png"], "question": "What is on the left?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_192_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_192_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_192_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_192_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_192_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_192_5.png"], "question": "Which object is next to the one shaped like a sphere?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_193_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_193_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_193_2.png"], "question": "Which object is below the bed?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_194_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_194_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_194_2.png"], "question": "Which object is shaped like a cone and is above the table?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "B"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_195_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_195_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_195_2.png"], "question": "What is at the bottom?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_196_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_196_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_196_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_196_3.png"], "question": "What is at the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image"}, "output": {"output_text": "C"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_197_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_197_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_197_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_197_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_197_4.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_197_5.png"], "question": "Which object is next to the top?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image\nE: The sixth image"}, "output": {"output_text": "D"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_198_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_198_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_198_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_198_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_198_4.png"], "question": "Which object is next to the bunch of bananas?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "iconqa", "options": "A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_199_0.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_199_1.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_199_2.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_199_3.png", "2D-spatial/Icon_Question_Answering_with_Spatial_Context/Icon_Question_Answering_with_Spatial_Context_199_4.png"], "question": "Which object is beside the one shaped like a cube?", "context": "Please answer a multi-choice question in the spatial context of icon images. The input image is the first image.\nSelect from the following choices.A: The second image\nB: The third image\nC: The fourth image\nD: The fifth image"}, "output": {"output_text": "A"}, "task": "Icon_Question_Answering_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box that has four items and the three are touching the side.\nB: There is a box that has five items and all are in the center.\nC: There is a box that has three items and the four are touching the side.\nD: There is a bag that has four items and the three are touching the side.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_0_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_0_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_0_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box that has four items and the three are touching the side.\nB: There is a box that has five items and all are in the center.\nC: There is a box that has three items and the four are touching the side.\nD: There is a bag that has four items and the three are touching the side."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is a red square touching the base\nB: there is a white circle touching the base\nC: there is a black square touching the base\nD: there is a black triangle touching the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_1_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_1_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_1_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is a red square touching the base\nB: there is a white circle touching the base\nC: there is a black square touching the base\nD: there is a black triangle touching the base"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with 1 black and 1 blue item.\nB: There is a box with 1 black and 1 green item.\nC: There is a box with 2 black items.\nD: There is a box with 1 red and 1 blue item.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_2_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_2_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_2_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with 1 black and 1 blue item.\nB: There is a box with 1 black and 1 green item.\nC: There is a box with 2 black items.\nD: There is a box with 1 red and 1 blue item."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a black block above a yellow block.\nB: There is a yellow block above a black block.\nC: There is a yellow block below a black block.\nD: There is a yellow block next to a black block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_3_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_3_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_3_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a black block above a yellow block.\nB: There is a yellow block above a black block.\nC: There is a yellow block below a black block.\nD: There is a yellow block next to a black block."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a blue block as the top of a tower.\nB: There is a red ball at the top of a tower.\nC: There is a yellow block at the base of a tower.\nD: There is a yellow block as the top of a tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_4_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_4_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_4_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a blue block as the top of a tower.\nB: There is a red ball at the top of a tower.\nC: There is a yellow block at the base of a tower.\nD: There is a yellow block as the top of a tower."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 2 towers that contain white blocks\nB: There are 2 towers that contain black blocks\nC: There are 3 towers that contain black blocks\nD: There is 1 tower that contains black blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_5_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_5_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_5_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 2 towers that contain white blocks\nB: There are 2 towers that contain black blocks\nC: There are 3 towers that contain black blocks\nD: There is 1 tower that contains black blocks"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: All three towers have a blue base.\nB: None of the towers have a blue base.\nC: Only one tower has a blue base.\nD: Two of the three towers has a blue base.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_6_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_6_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_6_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: All three towers have a blue base.\nB: None of the towers have a blue base.\nC: Only one tower has a blue base.\nD: Two of the three towers has a blue base."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a blue sphere as the base of a tower with more than two blocks\nB: There is a red block as the base of a tower with more than two blocks.\nC: There is a blue block as the base of a tower with more than two blocks.\nD: There is a blue block as the base of a single block tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_7_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_7_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_7_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a blue sphere as the base of a tower with more than two blocks\nB: There is a red block as the base of a tower with more than two blocks.\nC: There is a blue block as the base of a tower with more than two blocks.\nD: There is a blue block as the base of a single block tower."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are two colors touching the wall.\nB: The wall has multiple colors.\nC: No colors are touching the wall.\nD: There is only one color touching the wall.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_8_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_8_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_8_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are two colors touching the wall.\nB: The wall has multiple colors.\nC: No colors are touching the wall.\nD: There is only one color touching the wall."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is at least 1 triangle closely touching a box corner\nB: There is at least 1 circle closely touching a box edge\nC: There is at least 1 square closely touching a circle\nD: There is at least 1 square closely tocuhing a box corner", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_9_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_9_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_9_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is at least 1 triangle closely touching a box corner\nB: There is at least 1 circle closely touching a box edge\nC: There is at least 1 square closely touching a circle\nD: There is at least 1 square closely tocuhing a box corner"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 box with 2 black circles\nB: There is 1 box with 3 black circles\nC: There are 3 boxes with 2 black circles\nD: There are 2 boxes with 1 black circle", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_10_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_10_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_10_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 box with 2 black circles\nB: There is 1 box with 3 black circles\nC: There are 3 boxes with 2 black circles\nD: There are 2 boxes with 1 black circle"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is one tower with a black block at the top\nB: there is one tower with a red block at the top\nC: there are two towers with a black block at the top\nD: there is one tower with no block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_11_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_11_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_11_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is one tower with a black block at the top\nB: there is one tower with a red block at the top\nC: there are two towers with a black block at the top\nD: there is one tower with no block at the top"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: A yellow block is under a green block.\nB: There is a yellow block on a blue block.\nC: There is a red block next to a blue block.\nD: The green block is above the red block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_12_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_12_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_12_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: A yellow block is under a green block.\nB: There is a yellow block on a blue block.\nC: There is a red block next to a blue block.\nD: The green block is above the red block."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: All towers have different base colors.\nB: There are only two towers which has the same base color.\nC: Only one tower has a unique base color.\nD: There are three towers with the same base color.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_13_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_13_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_13_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: All towers have different base colors.\nB: There are only two towers which has the same base color.\nC: Only one tower has a unique base color.\nD: There are three towers with the same base color."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are three yellow blocks in the middle of a tower.\nB: There are two yellow blocks as the base of a tower.\nC: There are two red blocks as the base of a tower.\nD: There is one yellow block at the top of a tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_14_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_14_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_14_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are three yellow blocks in the middle of a tower.\nB: There are two yellow blocks as the base of a tower.\nC: There are two red blocks as the base of a tower.\nD: There is one yellow block at the top of a tower."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with items of orange and pink color.\nB: There is a box with items of only black and blue color.\nC: There is a box with items of red and white color.\nD: There is a box with items of green and yellow color.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_15_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_15_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_15_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with items of orange and pink color.\nB: There is a box with items of only black and blue color.\nC: There is a box with items of red and white color.\nD: There is a box with items of green and yellow color."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a black tower.\nB: There is a black tree.\nC: There is a black bridge.\nD: There is a white tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_16_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_16_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_16_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a black tower.\nB: There is a black tree.\nC: There is a black bridge.\nD: There is a white tower."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is exactly one yellow triangle touching the edge\nB: There is exactly one red triangle touching the edge\nC: There are no yellow triangles touching the edge\nD: There are two yellow triangles touching the edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_17_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_17_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_17_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is exactly one yellow triangle touching the edge\nB: There is exactly one red triangle touching the edge\nC: There are no yellow triangles touching the edge\nD: There are two yellow triangles touching the edge"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are exactly 2 blue blocks\nB: There are no blue blocks\nC: There are at least 3 blue blocks\nD: There are more than 10 blue blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_18_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_18_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_18_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are exactly 2 blue blocks\nB: There are no blue blocks\nC: There are at least 3 blue blocks\nD: There are more than 10 blue blocks"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are two white items in the middle of the box.\nB: There is one black item and one white item at the edge of the box.\nC: There are two black items closely touching the bottom of a box.\nD: There is a single black item at the top of the box.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_19_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_19_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_19_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are two white items in the middle of the box.\nB: There is one black item and one white item at the edge of the box.\nC: There are two black items closely touching the bottom of a box.\nD: There is a single black item at the top of the box."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is no tower with a blue block at the base\nB: there is a tower with a red block at the base\nC: there are multiple towers with a blue block at the base\nD: there is exactly one tower with a blue block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_20_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_20_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_20_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is no tower with a blue block at the base\nB: there is a tower with a red block at the base\nC: there are multiple towers with a blue block at the base\nD: there is exactly one tower with a blue block at the base"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box, which a blue triangle and at least two black items.\nB: There is a box, which a blue circle and at least two black items.\nC: There is a box, which a blue triangle and only one black item.\nD: There is a box, which a green triangle and at least two black items.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_21_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_21_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_21_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box, which a blue triangle and at least two black items.\nB: There is a box, which a blue circle and at least two black items.\nC: There is a box, which a blue triangle and only one black item.\nD: There is a box, which a green triangle and at least two black items."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: One tower has a red block on top of a blue block\nB: One tower has a yellow block on top of a green block\nC: One tower has a yellow block on top of a blue block\nD: One tower has a blue block on top of a yellow block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_22_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_22_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_22_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: One tower has a red block on top of a blue block\nB: One tower has a yellow block on top of a green block\nC: One tower has a yellow block on top of a blue block\nD: One tower has a blue block on top of a yellow block"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 towers with black blocks\nB: No towers have black blocks\nC: There is 1 tower that contains black blocks\nD: There are 2 towers that contain at least 1 black block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_23_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_23_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_23_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 towers with black blocks\nB: No towers have black blocks\nC: There is 1 tower that contains black blocks\nD: There are 2 towers that contain at least 1 black block"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: A black block is at the top of a tower\nB: There is 1 tower with a black block at the bottom\nC: A tower with a red block at the bottom\nD: There are 2 towers with black blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_24_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_24_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_24_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: A black block is at the top of a tower\nB: There is 1 tower with a black block at the bottom\nC: A tower with a red block at the bottom\nD: There are 2 towers with black blocks"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a pyramid with four blocks.\nB: There is a tower with four blocks.\nC: There is a tower with three blocks.\nD: There is a tower with five blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_25_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_25_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_25_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a pyramid with four blocks.\nB: There is a tower with four blocks.\nC: There is a tower with three blocks.\nD: There is a tower with five blocks."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a yellow block on a blue block.\nB: There is a yellow block on a green block.\nC: There is a red block on a blue block.\nD: There is a blue block on a yellow block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_26_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_26_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_26_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a yellow block on a blue block.\nB: There is a yellow block on a green block.\nC: There is a red block on a blue block.\nD: There is a blue block on a yellow block."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 boxes with a black item on top.\nB: There are 2 boxes with a white item on top.\nC: There is 1 box with a black item on top.\nD: There are 2 boxes with a black item on top.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_27_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_27_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_27_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 boxes with a black item on top.\nB: There are 2 boxes with a white item on top.\nC: There is 1 box with a black item on top.\nD: There are 2 boxes with a black item on top."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is exactly one red triangle touching the edge\nB: there are two blue triangles touching the edge\nC: there is exactly one blue square touching the edge\nD: there is exactly one blue triangle touching the edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_28_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_28_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_28_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is exactly one red triangle touching the edge\nB: there are two blue triangles touching the edge\nC: there is exactly one blue square touching the edge\nD: there is exactly one blue triangle touching the edge"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: One of the grey boxes has exactly seven objects\nB: One of the grey boxes has exactly eight objects\nC: One of the grey boxes has exactly four objects\nD: One of the grey box has exactly six objects", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_29_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_29_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_29_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: One of the grey boxes has exactly seven objects\nB: One of the grey boxes has exactly eight objects\nC: One of the grey boxes has exactly four objects\nD: One of the grey box has exactly six objects"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is exactly one tower with two blocks\nB: there are no towers with three blocks\nC: there are at least two towers with four blocks\nD: there is at least one tower with three blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_30_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_30_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_30_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is exactly one tower with two blocks\nB: there are no towers with three blocks\nC: there are at least two towers with four blocks\nD: there is at least one tower with three blocks"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a blue triangle touching the side.\nB: There is a red hexagon in the center.\nC: There is a yellow square touching the side.\nD: There is a green circle in the corner.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_31_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_31_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_31_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a blue triangle touching the side.\nB: There is a red hexagon in the center.\nC: There is a yellow square touching the side.\nD: There is a green circle in the corner."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with exactly four blocks with a yellow block at the bottom\nB: There is a tower with exactly three blocks with a yellow block at the top\nC: There is a tower with three red blocks at the top\nD: There is a tower with exactly two blocks, both yellow", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_32_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_32_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_32_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with exactly four blocks with a yellow block at the bottom\nB: There is a tower with exactly three blocks with a yellow block at the top\nC: There is a tower with three red blocks at the top\nD: There is a tower with exactly two blocks, both yellow"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: No boxes contain yellow items\nB: All boxes contain blue items\nC: There is at least 1 yellow item in each box\nD: Each box contains only red items", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_33_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_33_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_33_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: No boxes contain yellow items\nB: All boxes contain blue items\nC: There is at least 1 yellow item in each box\nD: Each box contains only red items"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: None of the black triangles are touching the center\nB: All of the black triangles are touching an edge\nC: None of the black triangles are touching a edge\nD: Some black triangles are touching an edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_34_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_34_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_34_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: None of the black triangles are touching the center\nB: All of the black triangles are touching an edge\nC: None of the black triangles are touching a edge\nD: Some black triangles are touching an edge"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 stack with only purple and orange blocks\nB: There is 1 pile with only green and white blocks\nC: There is 1 tower with only blue and black blocks\nD: There is 1 tower with only red and yellow blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_35_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_35_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_35_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 stack with only purple and orange blocks\nB: There is 1 pile with only green and white blocks\nC: There is 1 tower with only blue and black blocks\nD: There is 1 tower with only red and yellow blocks"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 boxes with a triangle in the middle\nB: There are 2 boxes with a triangle far from the corner\nC: There are 2 circles with a square closely touching a corner\nD: There are 2 boxes with a triangle closely touching a corner", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_36_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_36_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_36_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 boxes with a triangle in the middle\nB: There are 2 boxes with a triangle far from the corner\nC: There are 2 circles with a square closely touching a corner\nD: There are 2 boxes with a triangle closely touching a corner"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is exactly one circle touching the edge\nB: there are no circles touching the edge\nC: there are at least two circles touching the edge\nD: there are three triangles touching the edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_37_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_37_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_37_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is exactly one circle touching the edge\nB: there are no circles touching the edge\nC: there are at least two circles touching the edge\nD: there are three triangles touching the edge"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with only two items of black and yellow color.\nB: There is a box with two items of red and blue color.\nC: There is a box with three items of black and yellow color.\nD: There is a drawer with two items of green and yellow color.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_38_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_38_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_38_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with only two items of black and yellow color.\nB: There is a box with two items of red and blue color.\nC: There is a box with three items of black and yellow color.\nD: There is a drawer with two items of green and yellow color."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with three blocks.\nB: There is a tower with six blocks.\nC: There is a tower with four blocks.\nD: There is a tower with five blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_39_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_39_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_39_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with three blocks.\nB: There is a tower with six blocks.\nC: There is a tower with four blocks.\nD: There is a tower with five blocks."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a three blocks tower which has only one blue block.\nB: There is a three blocks tower which has only red blocks.\nC: There is a two blocks tower which has only one blue block.\nD: There is a four blocks tower which has two blue blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_40_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_40_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_40_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a three blocks tower which has only one blue block.\nB: There is a three blocks tower which has only red blocks.\nC: There is a two blocks tower which has only one blue block.\nD: There is a four blocks tower which has two blue blocks."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is ablue block on a black block.\nB: There is no block in the picture.\nC: There is a blue block next to a black block.\nD: A black block is on top of a blue block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_41_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_41_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_41_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is ablue block on a black block.\nB: There is no block in the picture.\nC: There is a blue block next to a black block.\nD: A black block is on top of a blue block."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 2 towers with 2 yellow blocks\nB: There is 1 tower with 3 yellow blocks\nC: There is 1 tower with 2 yellow blocks\nD: There is 1 tower with 2 blue blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_42_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_42_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_42_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 2 towers with 2 yellow blocks\nB: There is 1 tower with 3 yellow blocks\nC: There is 1 tower with 2 yellow blocks\nD: There is 1 tower with 2 blue blocks"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: A box holds a blue triangle, a blue square, and a yellow circle.\nB: A box contains a blue circle, a yellow triangle, and a yellow square.\nC: There is a box with a blue triangle, a yellow square and a yellow circle.\nD: There is a box with a blue triangle, a yellow square", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_43_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_43_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_43_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: A box holds a blue triangle, a blue square, and a yellow circle.\nB: A box contains a blue circle, a yellow triangle, and a yellow square.\nC: There is a box with a blue triangle, a yellow square and a yellow circle.\nD: There is a box with a blue triangle, a yellow square"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with no items inside.\nB: There is a box with items of three different shapes.\nC: There is a box with items of only one color.\nD: There is a box with items of various colors.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_44_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_44_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_44_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with no items inside.\nB: There is a box with items of three different shapes.\nC: There is a box with items of only one color.\nD: There is a box with items of various colors."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 2 boxes with only red and yellow items.\nB: There are 3 boxes with only black and yellow items.\nC: There are 2 boxes with only black and blue items.\nD: There are 2 boxes with only black and yellow items.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_45_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_45_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_45_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 2 boxes with only red and yellow items.\nB: There are 3 boxes with only black and yellow items.\nC: There are 2 boxes with only black and blue items.\nD: There are 2 boxes with only black and yellow items."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a red block above a yellow block.\nB: There is a black block above a yellow block.\nC: There is a yellow block below a black block.\nD: There is a yellow block above a black block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_46_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_46_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_46_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a red block above a yellow block.\nB: There is a black block above a yellow block.\nC: There is a yellow block below a black block.\nD: There is a yellow block above a black block."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with a yellow block over a blue block\nB: There is a tower with a red block over a blue block\nC: There is a tower with a yellow block over a green block\nD: There is a tower with a yellow block next to a blue block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_47_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_47_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_47_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with a yellow block over a blue block\nB: There is a tower with a red block over a blue block\nC: There is a tower with a yellow block over a green block\nD: There is a tower with a yellow block next to a blue block"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with only two black and blue items.\nB: There is a box with different colored items.\nC: There is a box with several black and blue items.\nD: There is a box with only black items.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_48_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_48_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_48_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with only two black and blue items.\nB: There is a box with different colored items.\nC: There is a box with several black and blue items.\nD: There is a box with only black items."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with 4 items and 2 yellow squares\nB: There is a box with 3 items and 2 yellow squares in the middle.\nC: There is a box with 4 items and 2 yellow squares in the middle.\nD: There is a box with 4 items and 2 red circles in the middle.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_49_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_49_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_49_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with 4 items and 2 yellow squares\nB: There is a box with 3 items and 2 yellow squares in the middle.\nC: There is a box with 4 items and 2 yellow squares in the middle.\nD: There is a box with 4 items and 2 red circles in the middle."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are two black towers with multiple blocks.\nB: There is a black tower with several blocks.\nC: There is a white tower with only one block.\nD: There is a black tower with only one block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_50_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_50_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_50_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are two black towers with multiple blocks.\nB: There is a black tower with several blocks.\nC: There is a white tower with only one block.\nD: There is a black tower with only one block."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 black circles\nB: There are 2 white triangles\nC: There are 2 black triangles\nD: There are 5 black triangles", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_51_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_51_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_51_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 black circles\nB: There are 2 white triangles\nC: There are 2 black triangles\nD: There are 5 black triangles"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with four blocks.\nB: There is a row of candles.\nC: There is a stack of plates.\nD: There is a pile of books.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_52_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_52_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_52_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with four blocks.\nB: There is a row of candles.\nC: There is a stack of plates.\nD: There is a pile of books."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are no blue blocks\nB: There are at least 3 blue blocks\nC: There are exactly two blue blocks\nD: There is only one blue block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_53_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_53_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_53_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are no blue blocks\nB: There are at least 3 blue blocks\nC: There are exactly two blue blocks\nD: There is only one blue block"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 4 yellow items and one large circle touching the wall.\nB: There are 3 yellow items but none are touching the wall.\nC: There are 3 yellow items touching the wall and at least one small circle nearly touching the wall.\nD: There are 2 yellow items touching the wall and no small circles.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_54_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_54_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_54_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 4 yellow items and one large circle touching the wall.\nB: There are 3 yellow items but none are touching the wall.\nC: There are 3 yellow items touching the wall and at least one small circle nearly touching the wall.\nD: There are 2 yellow items touching the wall and no small circles."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with three colors and no items on top.\nB: There is a box with two colors and a white item on top.\nC: There is a round container with all 3 colors and a black item beside it.\nD: There is a box with all 3 colors and a black item on top.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_55_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_55_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_55_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with three colors and no items on top.\nB: There is a box with two colors and a white item on top.\nC: There is a round container with all 3 colors and a black item beside it.\nD: There is a box with all 3 colors and a black item on top."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 tower with a yellow block at the top\nB: There is 1 tower with a yellow block at the base\nC: There is 1 tower with a red block at the base\nD: There are 2 towers with a yellow block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_56_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_56_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_56_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 tower with a yellow block at the top\nB: There is 1 tower with a yellow block at the base\nC: There is 1 tower with a red block at the base\nD: There are 2 towers with a yellow block at the base"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there are two black triangles touching the base\nB: there is one black triangle touching the base\nC: there is one black triangle not touching the base\nD: there are no black triangles touching the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_57_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_57_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_57_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there are two black triangles touching the base\nB: there is one black triangle touching the base\nC: there is one black triangle not touching the base\nD: there are no black triangles touching the base"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with exactly three blocks with a yellow block at the top\nB: There is a tower with three blocks with a blue block at the top\nC: There is a tower with four blocks and a red block at the top\nD: There is a tower with two blocks and a green block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_58_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_58_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_58_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with exactly three blocks with a yellow block at the top\nB: There is a tower with three blocks with a blue block at the top\nC: There is a tower with four blocks and a red block at the top\nD: There is a tower with two blocks and a green block at the top"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 2 blue blocks\nB: There is 1 blue block\nC: There are 2 red blocks\nD: There are 3 green blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_59_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_59_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_59_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 2 blue blocks\nB: There is 1 blue block\nC: There are 2 red blocks\nD: There are 3 green blocks"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with 3 items and a black item on top.\nB: There is a box with 5 items and a red item on top.\nC: There is a box with 2 items and a blue item on top.\nD: There is a box with 3 items and a white item on top.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_60_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_60_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_60_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with 3 items and a black item on top.\nB: There is a box with 5 items and a red item on top.\nC: There is a box with 2 items and a blue item on top.\nD: There is a box with 3 items and a white item on top."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: t least two of the towers ha yellow bases.\nB: None of the towers have yellow bases.\nC: At most two of the towers have yellow bases.\nD: All of the towers have yellow bases.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_61_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_61_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_61_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: t least two of the towers ha yellow bases.\nB: None of the towers have yellow bases.\nC: At most two of the towers have yellow bases.\nD: All of the towers have yellow bases."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: the tower with two blocks has a black block at the top\nB: the tower with four blocks has a black block at the bottom\nC: the tower with four blocks has a red block at the top\nD: the tower with four blocks has a black block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_62_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_62_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_62_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: the tower with two blocks has a black block at the top\nB: the tower with four blocks has a black block at the bottom\nC: the tower with four blocks has a red block at the top\nD: the tower with four blocks has a black block at the top"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with items of 2 different colors and a black square.\nB: There is a box with items of 4 different colors and no square.\nC: There is a box with items of 2 different colors and a red square.\nD: There is a box with items of 3 different colors and a black square.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_63_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_63_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_63_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with items of 2 different colors and a black square.\nB: There is a box with items of 4 different colors and no square.\nC: There is a box with items of 2 different colors and a red square.\nD: There is a box with items of 3 different colors and a black square."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a yellow square touching the wall.\nB: There is a blue rectangle on the floor.\nC: There is a green circle floating in the air.\nD: There is a red triangle near the door.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_64_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_64_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_64_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a yellow square touching the wall.\nB: There is a blue rectangle on the floor.\nC: There is a green circle floating in the air.\nD: There is a red triangle near the door."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with 3 items of the same color.\nB: There is a box with 4 items of all different colors.\nC: There is a box with 2 items of different colors.\nD: There is a box with 3 items of all 3 different colors.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_65_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_65_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_65_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with 3 items of the same color.\nB: There is a box with 4 items of all different colors.\nC: There is a box with 2 items of different colors.\nD: There is a box with 3 items of all 3 different colors."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: the tower has two blue blocks with a yellow block at the top\nB: there are three blocks in the tower with a red block at the top\nC: there is a tower with exactly two blocks having a blue block at the top.\nD: the tower has a single blue block at the top and bottom", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_66_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_66_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_66_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: the tower has two blue blocks with a yellow block at the top\nB: there are three blocks in the tower with a red block at the top\nC: there is a tower with exactly two blocks having a blue block at the top.\nD: the tower has a single blue block at the top and bottom"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with a yellow block over a red block\nB: There is a tower with a green block over a yellow block\nC: There is a tower with a yellow block over a blue block\nD: There is a tower with a blue block over a yellow block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_67_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_67_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_67_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with a yellow block over a red block\nB: There is a tower with a green block over a yellow block\nC: There is a tower with a yellow block over a blue block\nD: There is a tower with a blue block over a yellow block"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: green block on the side\nB: blue block at the bottom\nC: yellow block at the top\nD: red block in the middle", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_68_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_68_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_68_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: green block on the side\nB: blue block at the bottom\nC: yellow block at the top\nD: red block in the middle"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a square closely touching the side of a box.\nB: There is a square closely touching the bottom of a box.\nC: There is no square closely touching the top of a box.\nD: There is no square closely touching the bottom of a box.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_69_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_69_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_69_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a square closely touching the side of a box.\nB: There is a square closely touching the bottom of a box.\nC: There is no square closely touching the top of a box.\nD: There is no square closely touching the bottom of a box."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with a yellow block, a blue block and a black block.\nB: There is a tower with a yellow block, a green block and a black block.\nC: There is a tower with a yellow block, a blue block and\nD: There is a tower with a red block, a blue block and a black block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_70_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_70_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_70_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with a yellow block, a blue block and a black block.\nB: There is a tower with a yellow block, a green block and a black block.\nC: There is a tower with a yellow block, a blue block and\nD: There is a tower with a red block, a blue block and a black block."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a black tower with only one block.\nB: There is a black tower with multiple blocks.\nC: There is a black tower with no blocks.\nD: There is a white tower with only one block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_71_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_71_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_71_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a black tower with only one block.\nB: There is a black tower with multiple blocks.\nC: There is a black tower with no blocks.\nD: There is a white tower with only one block."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 boxes each with black and yellow items.\nB: There is a box with only 3 items of black and yellow color.\nC: There is a black and yellow box with 3 items.\nD: There is a box with various items of different colors.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_72_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_72_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_72_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 boxes each with black and yellow items.\nB: There is a box with only 3 items of black and yellow color.\nC: There is a black and yellow box with 3 items.\nD: There is a box with various items of different colors."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is a black square touching the base\nB: there is a black circle touching the base\nC: there is a white square touching the base\nD: the square is floating above the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_73_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_73_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_73_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is a black square touching the base\nB: there is a black circle touching the base\nC: there is a white square touching the base\nD: the square is floating above the base"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are exactly two black squares touching an edge\nB: There are exactly three black squares not touching any edge\nC: There is exactly one black square not touching any edge\nD: There are exactly two black squares not touching any edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_74_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_74_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_74_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are exactly two black squares touching an edge\nB: There are exactly three black squares not touching any edge\nC: There is exactly one black square not touching any edge\nD: There are exactly two black squares not touching any edge"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is at least one tower with exactly two blocks having a blue block at the top\nB: there is no tower with exactly two blocks having a blue block at the top\nC: there is at least one tower with exactly two blocks having a red\nD: there is at least one tower with exactly three blocks having a blue block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_75_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_75_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_75_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is at least one tower with exactly two blocks having a blue block at the top\nB: there is no tower with exactly two blocks having a blue block at the top\nC: there is at least one tower with exactly two blocks having a red\nD: there is at least one tower with exactly three blocks having a blue block at the top"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 boxes with blue, yellow, and red items\nB: There is 1 box with only blue and yellow items\nC: There is 1 box with only red and green items\nD: There are 2 boxes with only blue and yellow items", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_76_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_76_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_76_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 boxes with blue, yellow, and red items\nB: There is 1 box with only blue and yellow items\nC: There is 1 box with only red and green items\nD: There are 2 boxes with only blue and yellow items"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 tower with a blue block at the base\nB: There is 1 tower with a blue block at the top\nC: There is 1 tower with a red block at the base\nD: There are 2 towers with a blue block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_77_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_77_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_77_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 tower with a blue block at the base\nB: There is 1 tower with a blue block at the top\nC: There is 1 tower with a red block at the base\nD: There are 2 towers with a blue block at the base"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 towers with a blue block at the base\nB: There are 2 towers with a red block at the base\nC: There is 1 tower with a green block at the top\nD: There is 1 tower with a blue block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_78_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_78_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_78_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 towers with a blue block at the base\nB: There are 2 towers with a red block at the base\nC: There is 1 tower with a green block at the top\nD: There is 1 tower with a blue block at the base"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a blue block on a single-block tower.\nB: There is a blue block as the top of a tower with at least two blocks.\nC: There is a blue block at the base of a tower with at least two blocks.\nD: There is a green block as the top of a tower with at least two blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_79_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_79_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_79_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a blue block on a single-block tower.\nB: There is a blue block as the top of a tower with at least two blocks.\nC: There is a blue block at the base of a tower with at least two blocks.\nD: There is a green block as the top of a tower with at least two blocks."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with a yellow triangle and three blue items.\nB: There is a box with a yellow square and three green items.\nC: There is a box with a yellow circle and two red items.\nD: There is a box with a yellow circle and three blue items.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_80_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_80_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_80_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with a yellow triangle and three blue items.\nB: There is a box with a yellow square and three green items.\nC: There is a box with a yellow circle and two red items.\nD: There is a box with a yellow circle and three blue items."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: All 3 colors are not touching the wall.\nB: None of the colors are touching the wall.\nC: ll 3 different colors are touching the wall.\nD: Only 2 colors are touching the wall.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_81_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_81_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_81_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: All 3 colors are not touching the wall.\nB: None of the colors are touching the wall.\nC: ll 3 different colors are touching the wall.\nD: Only 2 colors are touching the wall."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is one yellow block at the top of a tower.\nB: There is one red block as the base of a tower.\nC: There are two yellow blocks as the base of a tower.\nD: There are two blue blocks as the base of a tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_82_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_82_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_82_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is one yellow block at the top of a tower.\nB: There is one red block as the base of a tower.\nC: There are two yellow blocks as the base of a tower.\nD: There are two blue blocks as the base of a tower."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is at least one black block on a blue block.\nB: There is at least one black block on a green block.\nC: There is at least one blue block on a black block.\nD: There are only black blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_83_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_83_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_83_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is at least one black block on a blue block.\nB: There is at least one black block on a green block.\nC: There is at least one blue block on a black block.\nD: There are only black blocks."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is a red object touching the edge\nB: there is a green object touching the edge\nC: there is a blue object touching the edge\nD: there is a blue object in the center", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_84_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_84_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_84_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is a red object touching the edge\nB: there is a green object touching the edge\nC: there is a blue object touching the edge\nD: there is a blue object in the center"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 2 towers with only blue and black blocks\nB: There is 1 tower with only yellow and blue blocks\nC: There is 1 tower with only red and green blocks\nD: There is 1 tower with only blue and black blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_85_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_85_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_85_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 2 towers with only blue and black blocks\nB: There is 1 tower with only yellow and blue blocks\nC: There is 1 tower with only red and green blocks\nD: There is 1 tower with only blue and black blocks"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is one yellow item touching the floor.\nB: There are three yellow items touching the wall.\nC: There are two yellow items touching the wall.\nD: There are two blue items touching the wall.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_86_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_86_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_86_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is one yellow item touching the floor.\nB: There are three yellow items touching the wall.\nC: There are two yellow items touching the wall.\nD: There are two blue items touching the wall."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: one of the grey square contains exactly four objects\nB: one of the grey square contains exactly five objects\nC: one of the grey square contains exactly three objects\nD: one of the grey squares contains exactly six objects", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_87_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_87_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_87_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: one of the grey square contains exactly four objects\nB: one of the grey square contains exactly five objects\nC: one of the grey square contains exactly three objects\nD: one of the grey squares contains exactly six objects"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there are two blue circles touching the base\nB: there are two yellow circles touching the base\nC: there are three yellow circles touching the base\nD: there is one yellow circle in the middle", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_88_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_88_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_88_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there are two blue circles touching the base\nB: there are two yellow circles touching the base\nC: there are three yellow circles touching the base\nD: there is one yellow circle in the middle"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 2 black triangles\nB: There are no black triangles\nC: There are 3 black triangles\nD: There are 2 white triangles", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_89_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_89_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_89_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 2 black triangles\nB: There are no black triangles\nC: There are 3 black triangles\nD: There are 2 white triangles"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a red block at the top of the tower with only one block.\nB: There is a black block as the base of a tower with at least two blocks.\nC: There is a black block at the base of a tower with only one block.\nD: There is a black block floating in the air beside the tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_90_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_90_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_90_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a red block at the top of the tower with only one block.\nB: There is a black block as the base of a tower with at least two blocks.\nC: There is a black block at the base of a tower with only one block.\nD: There is a black block floating in the air beside the tower."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a green circle in the center of a box.\nB: There is a blue square closely touching the bottom of a box.\nC: There is a yellow star floating above a box.\nD: There is a red triangle in the top right corner of a box.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_91_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_91_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_91_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a green circle in the center of a box.\nB: There is a blue square closely touching the bottom of a box.\nC: There is a yellow star floating above a box.\nD: There is a red triangle in the top right corner of a box."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is only one yellow block as the base of a tower.\nB: There is one yellow block at the top of a tower.\nC: There are three yellow blocks at the base of the tower.\nD: There are two yellow blocks as the base of a tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_92_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_92_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_92_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is only one yellow block as the base of a tower.\nB: There is one yellow block at the top of a tower.\nC: There are three yellow blocks at the base of the tower.\nD: There are two yellow blocks as the base of a tower."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is one tower having a black block over a blue block\nB: there is one tower having a blue block over a black block\nC: there are two towers having black blocks over blue blocks\nD: there is one tower having a green block over a black block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_93_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_93_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_93_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is one tower having a black block over a blue block\nB: there is one tower having a blue block over a black block\nC: there are two towers having black blocks over blue blocks\nD: there is one tower having a green block over a black block"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are three towers that have two blue blocks.\nB: There is one tower that has two blue blocks.\nC: There are two towers that have one blue block.\nD: There are two towers that has two blue blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_94_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_94_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_94_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are three towers that have two blue blocks.\nB: There is one tower that has two blue blocks.\nC: There are two towers that have one blue block.\nD: There are two towers that has two blue blocks."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 4 yellow squares\nB: There are 3 yellow circles\nC: There are 3 yellow squares\nD: There are 3 blue squares", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_95_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_95_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_95_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 4 yellow squares\nB: There are 3 yellow circles\nC: There are 3 yellow squares\nD: There are 3 blue squares"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with 2 items and a yellow one touching the wall.\nB: There are no items in the box.\nC: A green item is touching the wall.\nD: The box contains 5 items.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_96_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_96_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_96_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with 2 items and a yellow one touching the wall.\nB: There are no items in the box.\nC: A green item is touching the wall.\nD: The box contains 5 items."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is a tree beside the tower\nB: there is a car near the tower\nC: there is a tower with exactly one block\nD: there is a tower with three blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_97_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_97_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_97_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is a tree beside the tower\nB: there is a car near the tower\nC: there is a tower with exactly one block\nD: there is a tower with three blocks"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there are two towers with black blocks at the base\nB: there is exactly one tower with a white block at the base\nC: there is no tower with a black block at the base\nD: there is exactly one tower with a black block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_98_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_98_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_98_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there are two towers with black blocks at the base\nB: there is exactly one tower with a white block at the base\nC: there is no tower with a black block at the base\nD: there is exactly one tower with a black block at the base"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 4 black blocks\nB: There are no black blocks\nC: There are 3 black blocks\nD: There are 2 black blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_99_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_99_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_99_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 4 black blocks\nB: There are no black blocks\nC: There are 3 black blocks\nD: There are 2 black blocks"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is only one tower with at least two blue blocks.\nB: There are no towers with yellow blocks.\nC: There are two towers with at least two yellow blocks.\nD: There is only one tower with at least two yellow blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_100_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_100_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_100_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is only one tower with at least two blue blocks.\nB: There are no towers with yellow blocks.\nC: There are two towers with at least two yellow blocks.\nD: There is only one tower with at least two yellow blocks."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there are at least three red triangles not touching any edge\nB: there are at least three yellow triangles touching one edge\nC: there are at least three yellow triangles not touching any edge\nD: there are exactly two yellow triangles not touching any edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_101_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_101_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_101_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there are at least three red triangles not touching any edge\nB: there are at least three yellow triangles touching one edge\nC: there are at least three yellow triangles not touching any edge\nD: there are exactly two yellow triangles not touching any edge"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with a red circle and at least two black items.\nB: There is a box with a yellow triangle and at least two black items.\nC: There is a box with a yellow square and at least two black items.\nD: There is a box with a yellow square and no black items.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_102_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_102_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_102_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with a red circle and at least two black items.\nB: There is a box with a yellow triangle and at least two black items.\nC: There is a box with a yellow square and at least two black items.\nD: There is a box with a yellow square and no black items."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with no blocks.\nB: There is a tower with only one block.\nC: There is a tower with multiple blocks.\nD: There is no tower at all.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_103_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_103_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_103_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with no blocks.\nB: There is a tower with only one block.\nC: There is a tower with multiple blocks.\nD: There is no tower at all."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: yellow block at the top\nB: yellow block at the bottom\nC: blue block at the top\nD: red block in the middle", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_104_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_104_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_104_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: yellow block at the top\nB: yellow block at the bottom\nC: blue block at the top\nD: red block in the middle"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there are multiple towers with blocks of different colors\nB: there are no towers with blocks of the same color\nC: there are two towers with more than one block where all the blocks are of same color\nD: there is only one tower with blocks of the same color", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_105_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_105_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_105_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there are multiple towers with blocks of different colors\nB: there are no towers with blocks of the same color\nC: there are two towers with more than one block where all the blocks are of same color\nD: there is only one tower with blocks of the same color"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: One tower has a yellow block on top of a red block\nB: One tower has a blue block on top of a yellow block\nC: One tower has a red block on top of a green block\nD: One tower has a yellow block on top of a blue block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_106_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_106_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_106_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: One tower has a yellow block on top of a red block\nB: One tower has a blue block on top of a yellow block\nC: One tower has a red block on top of a green block\nD: One tower has a yellow block on top of a blue block"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are at least 3 blue blocks\nB: There are no blue blocks\nC: There are exactly 5 blue blocks\nD: There are at most 2 blue blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_107_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_107_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_107_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are at least 3 blue blocks\nB: There are no blue blocks\nC: There are exactly 5 blue blocks\nD: There are at most 2 blue blocks"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: The tower with four blocks has a black block at the bottom\nB: The tower with four blocks has a black block at the top\nC: The tower with three blocks has a black block at the top\nD: The tower with four blocks has a blue block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_108_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_108_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_108_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: The tower with four blocks has a black block at the bottom\nB: The tower with four blocks has a black block at the top\nC: The tower with three blocks has a black block at the top\nD: The tower with four blocks has a blue block at the top"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: All towers contain 1 green block\nB: Some towers contain 1 blue block\nC: All towers contain 2 blue blocks\nD: ll towers contain 1 blue block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_109_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_109_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_109_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: All towers contain 1 green block\nB: Some towers contain 1 blue block\nC: All towers contain 2 blue blocks\nD: ll towers contain 1 blue block"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there are two towers with blue blocks in the middle\nB: there are three towers having red blocks at the top\nC: there is one tower with a green block at the base\nD: there are two towers having a yellow block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_110_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_110_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_110_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there are two towers with blue blocks in the middle\nB: there are three towers having red blocks at the top\nC: there is one tower with a green block at the base\nD: there are two towers having a yellow block at the base"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: All yellow blocks are at the bottom of the towers.\nB: There are no towers with a yellow block on top.\nC: There is at least a yellow block as the top of a tower.\nD: There are no yellow blocks in the towers.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_111_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_111_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_111_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: All yellow blocks are at the bottom of the towers.\nB: There are no towers with a yellow block on top.\nC: There is at least a yellow block as the top of a tower.\nD: There are no yellow blocks in the towers."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a black block in the middle of a tower with three blocks.\nB: There is a black block at the bottom of a tower with three blocks.\nC: There is a black block as the top of a tower with three blocks.\nD: There is a red block at the top of a tower with three blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_112_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_112_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_112_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a black block in the middle of a tower with three blocks.\nB: There is a black block at the bottom of a tower with three blocks.\nC: There is a black block as the top of a tower with three blocks.\nD: There is a red block at the top of a tower with three blocks."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with exactly four blocks with a black block at the bottom\nB: There is a tower with exactly one block which is black\nC: There is a tower with exactly three blocks with a white block at the top\nD: There is a tower with exactly two blocks with a black block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_113_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_113_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_113_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with exactly four blocks with a black block at the bottom\nB: There is a tower with exactly one block which is black\nC: There is a tower with exactly three blocks with a white block at the top\nD: There is a tower with exactly two blocks with a black block at the top"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are three towers with the same height and the base is red.\nB: There is one tower with different height and the base is yellow.\nC: There are two towers with the same height and the base is green.\nD: There are two tower with different height and the base is yellow.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_114_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_114_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_114_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are three towers with the same height and the base is red.\nB: There is one tower with different height and the base is yellow.\nC: There are two towers with the same height and the base is green.\nD: There are two tower with different height and the base is yellow."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is one blue block as the base of a tower.\nB: There are two blue blocks as the base of a tower.\nC: There are two red blocks as the base of a tower.\nD: There are three blue blocks as the base of a tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_115_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_115_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_115_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is one blue block as the base of a tower.\nB: There are two blue blocks as the base of a tower.\nC: There are two red blocks as the base of a tower.\nD: There are three blue blocks as the base of a tower."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are two yellow blocks in the middle of the tower.\nB: The base of the tower contains a red block.\nC: There is one blue block as the base of the tower.\nD: There is only one yellow block as the base of a tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_116_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_116_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_116_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are two yellow blocks in the middle of the tower.\nB: The base of the tower contains a red block.\nC: There is one blue block as the base of the tower.\nD: There is only one yellow block as the base of a tower."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a blue block next to a black block.\nB: There is a blue block below a black block.\nC: There is a blue block above a black block.\nD: There is a black block above a blue block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_117_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_117_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_117_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a blue block next to a black block.\nB: There is a blue block below a black block.\nC: There is a blue block above a black block.\nD: There is a black block above a blue block."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with exactly two black items and at least two blue items.\nB: There is a box with exactly two blue items and at most two black items.\nC: There is a box with exactly two blue items and at least two black items.\nD: There is a box with less than two blue items and exactly two black items", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_118_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_118_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_118_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with exactly two black items and at least two blue items.\nB: There is a box with exactly two blue items and at most two black items.\nC: There is a box with exactly two blue items and at least two black items.\nD: There is a box with less than two blue items and exactly two black items"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a yellow item closely touching right wall of a box.\nB: There is a red item closely touching right wall of a box.\nC: There is no yellow item closely touching right wall of a box.\nD: No items are touching the right wall of the box.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_119_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_119_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_119_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a yellow item closely touching right wall of a box.\nB: There is a red item closely touching right wall of a box.\nC: There is no yellow item closely touching right wall of a box.\nD: No items are touching the right wall of the box."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: All towers have only red blocks\nB: Only one tower has a blue block\nC: No towers have blue blocks\nD: ll 3 towers have at least 1 blue block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_120_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_120_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_120_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: All towers have only red blocks\nB: Only one tower has a blue block\nC: No towers have blue blocks\nD: ll 3 towers have at least 1 blue block"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a square touching the corner that is not yellow.\nB: There is a square touching the middle that is not yellow.\nC: There is a square in the center that is not yellow.\nD: There is a square touching the corner that is yellow.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_121_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_121_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_121_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a square touching the corner that is not yellow.\nB: There is a square touching the middle that is not yellow.\nC: There is a square in the center that is not yellow.\nD: There is a square touching the corner that is yellow."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: tleast one black triangle is not touching the edge\nB: No black triangles are present\nC: All black triangles are touching the edge\nD: All triangles are white and touching the edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_122_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_122_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_122_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: tleast one black triangle is not touching the edge\nB: No black triangles are present\nC: All black triangles are touching the edge\nD: All triangles are white and touching the edge"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with only one block.\nB: There is a tower with two blocks.\nC: There is no tower.\nD: There is a tower with multiple blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_123_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_123_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_123_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with only one block.\nB: There is a tower with two blocks.\nC: There is no tower.\nD: There is a tower with multiple blocks."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with 4 items of 3 different colors.\nB: There is a box with 3 items of all 3 different colors.\nC: There is a box with 2 items of all 3 different colors.\nD: There is a box with 3 items of all the same color.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_124_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_124_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_124_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with 4 items of 3 different colors.\nB: There is a box with 3 items of all 3 different colors.\nC: There is a box with 2 items of all 3 different colors.\nD: There is a box with 3 items of all the same color."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a green block at the top of the tower.\nB: The base of the tower is red.\nC: There is a blue block as the base of a tower.\nD: The tower has a yellow base block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_125_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_125_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_125_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a green block at the top of the tower.\nB: The base of the tower is red.\nC: There is a blue block as the base of a tower.\nD: The tower has a yellow base block."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a pyramid with four blocks.\nB: There is a tower with six blocks.\nC: There is a house with four blocks.\nD: There is a tower with four blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_126_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_126_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_126_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a pyramid with four blocks.\nB: There is a tower with six blocks.\nC: There is a house with four blocks.\nD: There is a tower with four blocks."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is no yellow circle closely touching the bottom of a box.\nB: There is no yellow triangle closely touching the bottom of a box.\nC: There is a yellow circle closely touching the bottom of a box.\nD: There is no blue circle closely touching the bottom of a box.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_127_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_127_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_127_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is no yellow circle closely touching the bottom of a box.\nB: There is no yellow triangle closely touching the bottom of a box.\nC: There is a yellow circle closely touching the bottom of a box.\nD: There is no blue circle closely touching the bottom of a box."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 tower with a red block at the base\nB: There is 1 tower with a yellow block at the base\nC: There is 1 tower with a blue block at the base\nD: There are 2 towers with a yellow block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_128_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_128_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_128_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 tower with a red block at the base\nB: There is 1 tower with a yellow block at the base\nC: There is 1 tower with a blue block at the base\nD: There are 2 towers with a yellow block at the base"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 white circles\nB: There are 4 black circles\nC: There are 2 black circles\nD: There are 2 white squares", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_129_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_129_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_129_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 white circles\nB: There are 4 black circles\nC: There are 2 black circles\nD: There are 2 white squares"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a black tower.\nB: There is a black house.\nC: There is a white tower.\nD: There is a black tree.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_130_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_130_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_130_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a black tower.\nB: There is a black house.\nC: There is a white tower.\nD: There is a black tree."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: All towers have different heights.\nB: Most towers are of different heights.\nC: There is only one tower with a unique height.\nD: There are at least two towers with the same height.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_131_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_131_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_131_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: All towers have different heights.\nB: Most towers are of different heights.\nC: There is only one tower with a unique height.\nD: There are at least two towers with the same height."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a green hexagon on the table.\nB: There is a red circle on the floor.\nC: There is a yellow square touching the wall.\nD: There is a blue triangle near the door.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_132_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_132_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_132_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a green hexagon on the table.\nB: There is a red circle on the floor.\nC: There is a yellow square touching the wall.\nD: There is a blue triangle near the door."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there are exactly two squares not touching any edge\nB: there are exactly five squares not touching any edge\nC: there are exactly three squares not touching any edge\nD: there are exactly four squares not touching any edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_133_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_133_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_133_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there are exactly two squares not touching any edge\nB: there are exactly five squares not touching any edge\nC: there are exactly three squares not touching any edge\nD: there are exactly four squares not touching any edge"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 tower with a red block and a blue block\nB: There is 1 tower with a yellow block and a blue block\nC: There are 2 towers with yellow blocks\nD: There is 1 tower with yellow and red blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_134_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_134_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_134_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 tower with a red block and a blue block\nB: There is 1 tower with a yellow block and a blue block\nC: There are 2 towers with yellow blocks\nD: There is 1 tower with yellow and red blocks"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a blue item in the center of a box.\nB: There is a blue item touching the left wall of a box.\nC: There is a blue item closely touching right wall of a box.\nD: There is a red item closely touching right wall of a box.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_135_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_135_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_135_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a blue item in the center of a box.\nB: There is a blue item touching the left wall of a box.\nC: There is a blue item closely touching right wall of a box.\nD: There is a red item closely touching right wall of a box."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: One of the grey boxes has exactly two objects both of which are circles\nB: One of the grey boxes has exactly three objects all of which are squares\nC: One of the grey box has exactly three objects one of which is a circle\nD: One of the grey boxes has exactly one object which is a triangle", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_136_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_136_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_136_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: One of the grey boxes has exactly two objects both of which are circles\nB: One of the grey boxes has exactly three objects all of which are squares\nC: One of the grey box has exactly three objects one of which is a circle\nD: One of the grey boxes has exactly one object which is a triangle"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are three blue squares touching the edge\nB: There are no blue squares in the picture\nC: There is only one blue square in the center\nD: There are exactly two blue squares not touching the edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_137_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_137_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_137_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are three blue squares touching the edge\nB: There are no blue squares in the picture\nC: There is only one blue square in the center\nD: There are exactly two blue squares not touching the edge"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: Only 2 yellow and one black item are touching the wall.\nB: Only 2 yellow and one red item are touching the wall.\nC: Only 3 yellow and one black item are touching the wall.\nD: Only 1 yellow and one black item are touching the wall.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_138_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_138_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_138_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: Only 2 yellow and one black item are touching the wall.\nB: Only 2 yellow and one red item are touching the wall.\nC: Only 3 yellow and one black item are touching the wall.\nD: Only 1 yellow and one black item are touching the wall."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: One box has 2 yellow squares\nB: One box has 3 yellow squares\nC: Two boxes have yellow squares\nD: One box has 2 red squares", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_139_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_139_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_139_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: One box has 2 yellow squares\nB: One box has 3 yellow squares\nC: Two boxes have yellow squares\nD: One box has 2 red squares"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are more than 5 blue blocks\nB: There are no blue blocks\nC: There are exactly 2 blue blocks\nD: There are at least 3 blue blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_140_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_140_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_140_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are more than 5 blue blocks\nB: There are no blue blocks\nC: There are exactly 2 blue blocks\nD: There are at least 3 blue blocks"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: the tower with three blocks has a yellow block at the top\nB: the tower with two blocks has a yellow block at the top\nC: the tower with two blocks has a blue block at the top\nD: the tower with two blocks has a yellow block at the bottom", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_141_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_141_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_141_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: the tower with three blocks has a yellow block at the top\nB: the tower with two blocks has a yellow block at the top\nC: the tower with two blocks has a blue block at the top\nD: the tower with two blocks has a yellow block at the bottom"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with 4 items of various colors.\nB: There is a box with 3 items of all 3 different colors.\nC: There is a box with 3 items all of the same color.\nD: There is a box with 2 items of all 3 different colors.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_142_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_142_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_142_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with 4 items of various colors.\nB: There is a box with 3 items of all 3 different colors.\nC: There is a box with 3 items all of the same color.\nD: There is a box with 2 items of all 3 different colors."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is one tower with a white block at the top\nB: there is one tower with a black block at the top\nC: there is a skyscraper with a blue block at the top\nD: there are two towers with a red block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_143_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_143_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_143_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is one tower with a white block at the top\nB: there is one tower with a black block at the top\nC: there is a skyscraper with a blue block at the top\nD: there are two towers with a red block at the top"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is a tower with a four block which has a red block over a blue block\nB: there is a tower with a four block which has a blue block over a blue block\nC: there is a tower with three blocks which has a blue block over a blue block\nD: there is a tower with a four block which has a yellow", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_144_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_144_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_144_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is a tower with a four block which has a red block over a blue block\nB: there is a tower with a four block which has a blue block over a blue block\nC: there is a tower with three blocks which has a blue block over a blue block\nD: there is a tower with a four block which has a yellow"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are three blue squares touching the edge\nB: There are two red squares in the center\nC: There are exactly two blue squares not touching the edge\nD: All blue squares are touching the edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_145_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_145_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_145_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are three blue squares touching the edge\nB: There are two red squares in the center\nC: There are exactly two blue squares not touching the edge\nD: All blue squares are touching the edge"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: t least two of the towers ha yellow bases.\nB: None of the towers have yellow bases.\nC: All of the towers have blue bases.\nD: At least one of the towers has a red base.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_146_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_146_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_146_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: t least two of the towers ha yellow bases.\nB: None of the towers have yellow bases.\nC: All of the towers have blue bases.\nD: At least one of the towers has a red base."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with a blue square and a blue triangle.\nB: There is a box with a blue circle and a blue triangle.\nC: There is a box with a green circle and a green triangle.\nD: There is a box with a red circle and a red triangle.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_147_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_147_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_147_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with a blue square and a blue triangle.\nB: There is a box with a blue circle and a blue triangle.\nC: There is a box with a green circle and a green triangle.\nD: There is a box with a red circle and a red triangle."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: The top of the two four block towers are red.\nB: The top of the two four block towers  are yellow.\nC: The bottom of the two four block towers are yellow.\nD: The top of the single five block tower is yellow.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_148_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_148_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_148_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: The top of the two four block towers are red.\nB: The top of the two four block towers  are yellow.\nC: The bottom of the two four block towers are yellow.\nD: The top of the single five block tower is yellow."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is a tower with a yellow block over a blue block\nB: there is a tower with a red block over a green block\nC: there is a tower with a black block over a red block\nD: there is a tower with a black block over a blue block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_149_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_149_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_149_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is a tower with a yellow block over a blue block\nB: there is a tower with a red block over a green block\nC: there is a tower with a black block over a red block\nD: there is a tower with a black block over a blue block"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 tower with a blue block at the base\nB: There are 2 towers with yellow blocks at the base\nC: There are 3 towers with green blocks at the base\nD: There is 1 tower with a yellow block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_150_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_150_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_150_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 tower with a blue block at the base\nB: There are 2 towers with yellow blocks at the base\nC: There are 3 towers with green blocks at the base\nD: There is 1 tower with a yellow block at the base"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with a blue block above a blue block\nB: There is a tower with a blue block above a red block\nC: There is a tower with a red block above a blue block\nD: There is a tower with a blue block below a blue block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_151_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_151_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_151_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with a blue block above a blue block\nB: There is a tower with a blue block above a red block\nC: There is a tower with a red block above a blue block\nD: There is a tower with a blue block below a blue block"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is a red circle in the center\nB: there are no circles touching the edge\nC: all circles are blue\nD: there is at least one yellow circle touching the edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_152_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_152_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_152_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is a red circle in the center\nB: there are no circles touching the edge\nC: all circles are blue\nD: there is at least one yellow circle touching the edge"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is only 1 tower that contains white blocks\nB: There are 3 towers that contain black blocks\nC: There are two towers that contain black blocks\nD: There is only 1 tower than contains black blccks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_153_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_153_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_153_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is only 1 tower that contains white blocks\nB: There are 3 towers that contain black blocks\nC: There are two towers that contain black blocks\nD: There is only 1 tower than contains black blccks"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with items of only black color.\nB: There is a box with exactly 3 items of black and blue color.\nC: There is a box with more than 3 items of black and red color.\nD: There is a box with 3 items at most of black and blue color.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_154_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_154_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_154_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with items of only black color.\nB: There is a box with exactly 3 items of black and blue color.\nC: There is a box with more than 3 items of black and red color.\nD: There is a box with 3 items at most of black and blue color."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a stack of 2 green blocks side by side\nB: There is a tower with 2 red blocks stacked together\nC: There is a tower with 3 blue blocks stacked together\nD: There is a tower with 2 blue blocks stacked together", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_155_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_155_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_155_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a stack of 2 green blocks side by side\nB: There is a tower with 2 red blocks stacked together\nC: There is a tower with 3 blue blocks stacked together\nD: There is a tower with 2 blue blocks stacked together"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: One box has 3 yellow squares\nB: One box has 2 blue squares\nC: One box has 2 red squares\nD: One box has 2 yellow squares", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_156_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_156_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_156_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: One box has 3 yellow squares\nB: One box has 2 blue squares\nC: One box has 2 red squares\nD: One box has 2 yellow squares"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with 3 blue blocks stacked together\nB: There is a tower with 2 red blocks stacked together\nC: There is a tower with 2 blue blocks stacked together\nD: There is a single blue block in the tower", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_157_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_157_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_157_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with 3 blue blocks stacked together\nB: There is a tower with 2 red blocks stacked together\nC: There is a tower with 2 blue blocks stacked together\nD: There is a single blue block in the tower"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is no blue block.\nB: There is at least one black block on a blue block.\nC: There is a blue block on a black block.\nD: There are only black blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_158_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_158_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_158_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is no blue block.\nB: There is at least one black block on a blue block.\nC: There is a blue block on a black block.\nD: There are only black blocks."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: The top of the two three block towers are yellow.\nB: The top of the two four block towers  are yellow.\nC: The bottom of the two four block towers are yellow.\nD: The top of the two four block towers are red.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_159_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_159_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_159_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: The top of the two three block towers are yellow.\nB: The top of the two four block towers  are yellow.\nC: The bottom of the two four block towers are yellow.\nD: The top of the two four block towers are red."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are exactly two black squares touching every edge\nB: There are exactly two white squares not touching any edge\nC: There are exactly two black squares not touching any edge\nD: There are exactly three black squares not touching any edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_160_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_160_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_160_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are exactly two black squares touching every edge\nB: There are exactly two white squares not touching any edge\nC: There are exactly two black squares not touching any edge\nD: There are exactly three black squares not touching any edge"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there are two yellow circles touching the base\nB: there are two red circles touching the base\nC: there are three yellow circles touching the base\nD: there is one yellow circle touching the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_161_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_161_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_161_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there are two yellow circles touching the base\nB: there are two red circles touching the base\nC: there are three yellow circles touching the base\nD: there is one yellow circle touching the base"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a black block at the bottom of a tower with two blocks.\nB: There is a black block alone on a flat surface.\nC: There is a red block at the top of a tower with three blocks.\nD: There is a black block as the top of a tower with at least two blocks.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_162_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_162_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_162_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a black block at the bottom of a tower with two blocks.\nB: There is a black block alone on a flat surface.\nC: There is a red block at the top of a tower with three blocks.\nD: There is a black block as the top of a tower with at least two blocks."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: All blue items are in different boxes.\nB: ll blue items are in the same box.\nC: None of the blue items are in the same box.\nD: Only some blue items are in the same box.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_163_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_163_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_163_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: All blue items are in different boxes.\nB: ll blue items are in the same box.\nC: None of the blue items are in the same box.\nD: Only some blue items are in the same box."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 towers with 1 yellow block\nB: There are 2 towers with 3 yellow blocks\nC: There is 1 tower with 2 red blocks\nD: There is 1 tower with 3 yellow blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_164_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_164_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_164_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 towers with 1 yellow block\nB: There are 2 towers with 3 yellow blocks\nC: There is 1 tower with 2 red blocks\nD: There is 1 tower with 3 yellow blocks"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 red circle\nB: There is 1 black circle\nC: There is 1 black square\nD: There are 2 black circles", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_165_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_165_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_165_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 red circle\nB: There is 1 black circle\nC: There is 1 black square\nD: There are 2 black circles"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is exactly one tower with a red block at base\nB: There is exactly one tower with a yellow block at base\nC: There are two towers with a yellow block at base\nD: There is no tower with a yellow block at base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_166_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_166_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_166_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is exactly one tower with a red block at base\nB: There is exactly one tower with a yellow block at base\nC: There are two towers with a yellow block at base\nD: There is no tower with a yellow block at base"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is at least one tower which has a yellow block above a black block\nB: there is at least one tower which has a black block above a yellow block\nC: all towers have a yellow block above a black block\nD: there is no tower which has a yellow block above a black block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_167_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_167_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_167_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is at least one tower which has a yellow block above a black block\nB: there is at least one tower which has a black block above a yellow block\nC: all towers have a yellow block above a black block\nD: there is no tower which has a yellow block above a black block"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with a blue block at the top.\nB: There is a blue tower with all blocks the same color.\nC: There is a tower that the second block from the base is blue.\nD: There is a tower with the second block from the top blue.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_168_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_168_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_168_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with a blue block at the top.\nB: There is a blue tower with all blocks the same color.\nC: There is a tower that the second block from the base is blue.\nD: There is a tower with the second block from the top blue."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: blue squares are touching the bottom edge\nB: blue squares are touching the top edge\nC: blue squares are not touching any edge\nD: blue squares are touching all edges", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_169_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_169_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_169_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: blue squares are touching the bottom edge\nB: blue squares are touching the top edge\nC: blue squares are not touching any edge\nD: blue squares are touching all edges"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with a yellow circle and 2 black squares.\nB: There is a box with a yellow triangle and 2 black circles.\nC: There is a box with a yellow triangle and 2 black squares.\nD: There is a box with a yellow triangle and 3 black squares.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_170_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_170_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_170_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with a yellow circle and 2 black squares.\nB: There is a box with a yellow triangle and 2 black circles.\nC: There is a box with a yellow triangle and 2 black squares.\nD: There is a box with a yellow triangle and 3 black squares."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a yellow block as the base of a tower.\nB: There is a yellow block at the top of the tower.\nC: There is no yellow block as the base of a tower.\nD: There are two yellow blocks in the middle of the tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_171_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_171_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_171_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a yellow block as the base of a tower.\nB: There is a yellow block at the top of the tower.\nC: There is no yellow block as the base of a tower.\nD: There are two yellow blocks in the middle of the tower."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are multiple towers with different colors.\nB: There is a single block tower with multiple colors.\nC: There is a two blocks tower with different colors.\nD: There is a two blocks tower that has only one color.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_172_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_172_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_172_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are multiple towers with different colors.\nB: There is a single block tower with multiple colors.\nC: There is a two blocks tower with different colors.\nD: There is a two blocks tower that has only one color."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: the single block is yellow\nB: the tower with two blocks has a yellow block at the top\nC: the tower with two blocks has a red block at the top\nD: the tower with three blocks has a yellow block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_173_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_173_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_173_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: the single block is yellow\nB: the tower with two blocks has a yellow block at the top\nC: the tower with two blocks has a red block at the top\nD: the tower with three blocks has a yellow block at the top"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with a blue block over a yellow block\nB: There is a tower with two yellow blocks\nC: There is a tower with a yellow block over a blue block\nD: There is a tower with a green block over a yellow block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_174_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_174_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_174_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with a blue block over a yellow block\nB: There is a tower with two yellow blocks\nC: There is a tower with a yellow block over a blue block\nD: There is a tower with a green block over a yellow block"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is one black triangle not touching any edge\nB: there are two black triangles touching the edges\nC: there are no black triangles visible\nD: there are two black triangles not touching any edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_175_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_175_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_175_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is one black triangle not touching any edge\nB: there are two black triangles touching the edges\nC: there are no black triangles visible\nD: there are two black triangles not touching any edge"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are no black triangles touching any edge\nB: There is exactly one black triangle touching an edge\nC: There are two black triangles not touching any edges\nD: There is exactly one black triangle not touching any edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_176_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_176_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_176_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are no black triangles touching any edge\nB: There is exactly one black triangle touching an edge\nC: There are two black triangles not touching any edges\nD: There is exactly one black triangle not touching any edge"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are two towers that has black block at the top.\nB: There are no towers in the image.\nC: There is only one tower with a black block at the top.\nD: There are two towers, but they have red blocks at the top.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_177_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_177_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_177_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are two towers that has black block at the top.\nB: There are no towers in the image.\nC: There is only one tower with a black block at the top.\nD: There are two towers, but they have red blocks at the top."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 4 black circles\nB: There are 3 black circles\nC: There are 2 white circles\nD: There are 2 black circles", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_178_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_178_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_178_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 4 black circles\nB: There are 3 black circles\nC: There are 2 white circles\nD: There are 2 black circles"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a blocking tower made of three stones.\nB: There is a tower with four same colored blocks.\nC: There is a tower with three different colored blocks.\nD: There is a tower that has three the same blocks color.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_179_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_179_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_179_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a blocking tower made of three stones.\nB: There is a tower with four same colored blocks.\nC: There is a tower with three different colored blocks.\nD: There is a tower that has three the same blocks color."}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are five circles not touching any edge\nB: There are exactly four circles touching one edge\nC: There are exactly three circles not touching any edge\nD: There are exactly four circles not touching any edge", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_180_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_180_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_180_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are five circles not touching any edge\nB: There are exactly four circles touching one edge\nC: There are exactly three circles not touching any edge\nD: There are exactly four circles not touching any edge"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a red block as the top of a tower with at least two blocks.\nB: There is a blue block as the bottom of a tower with at least two blocks.\nC: There is a blue block as the top of a tower with at least two blocks.\nD: There is a blue block as the top of a single block tower", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_181_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_181_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_181_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a red block as the top of a tower with at least two blocks.\nB: There is a blue block as the bottom of a tower with at least two blocks.\nC: There is a blue block as the top of a tower with at least two blocks.\nD: There is a blue block as the top of a single block tower"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: one of the grey squares is empty\nB: one of the grey squares has exactly five objects\nC: one of the grey square has exactly four objects\nD: one of the grey squares has exactly three objects", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_182_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_182_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_182_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: one of the grey squares is empty\nB: one of the grey squares has exactly five objects\nC: one of the grey square has exactly four objects\nD: one of the grey squares has exactly three objects"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is at least 1 circle closely touching a box corner\nB: There is at least 1 square closely tocuhing a box corner\nC: There is at least 1 square touching the center of a box\nD: There is at least 1 triangle closely touching a box corner", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_183_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_183_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_183_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is at least 1 circle closely touching a box corner\nB: There is at least 1 square closely tocuhing a box corner\nC: There is at least 1 square touching the center of a box\nD: There is at least 1 triangle closely touching a box corner"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: Each grey box contains atleast one yellow object touching the edge\nB: Each grey box has no object touching the edge\nC: Each grey box is empty\nD: Each grey box contains a green object", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_184_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_184_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_184_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: Each grey box contains atleast one yellow object touching the edge\nB: Each grey box has no object touching the edge\nC: Each grey box is empty\nD: Each grey box contains a green object"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is at least 1 tower with a blue block at the top\nB: There are exactly 2 towers with a blue block at the top\nC: There are no towers with a blue block at the top\nD: There is at least 1 tower with a green block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_185_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_185_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_185_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is at least 1 tower with a blue block at the top\nB: There are exactly 2 towers with a blue block at the top\nC: There are no towers with a blue block at the top\nD: There is at least 1 tower with a green block at the top"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: No towers have any height.\nB: All towers have different heights.\nC: There are at least two towers with the same height.\nD: There is only one tower with the same height.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_186_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_186_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_186_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: No towers have any height.\nB: All towers have different heights.\nC: There are at least two towers with the same height.\nD: There is only one tower with the same height."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a tower with three blue blocks.\nB: There is a tower with a black block and two blue blocks.\nC: There is a tower with two black blocks and a blue block.\nD: There is a tower with a black block and a red block.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_187_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_187_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_187_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a tower with three blue blocks.\nB: There is a tower with a black block and two blue blocks.\nC: There is a tower with two black blocks and a blue block.\nD: There is a tower with a black block and a red block."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is a tower with a yellow block below a red block at the top\nB: there is a tower with a red block below a yellow block at the top\nC: there is a tower with a blue block below a green block at the top\nD: there is a tower with a yellow block below a yellow block at the top", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_188_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_188_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_188_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is a tower with a yellow block below a red block at the top\nB: there is a tower with a red block below a yellow block at the top\nC: there is a tower with a blue block below a green block at the top\nD: there is a tower with a yellow block below a yellow block at the top"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 2 blue blocks\nB: There are 4 blue blocks\nC: There are 3 blue blocks\nD: There are 2 red blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_189_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_189_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_189_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 2 blue blocks\nB: There are 4 blue blocks\nC: There are 3 blue blocks\nD: There are 2 red blocks"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is 1 tower with 2 yellow blocks at the base\nB: There are 2 towers with 1 yellow block at the base\nC: There is 1 tower with 1 red block at the base\nD: There is 1 tower with 1 yellow block at the base", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_190_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_190_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_190_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is 1 tower with 2 yellow blocks at the base\nB: There are 2 towers with 1 yellow block at the base\nC: There is 1 tower with 1 red block at the base\nD: There is 1 tower with 1 yellow block at the base"}, "output": {"output_text": "D"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are two red blocks as the base of a tower.\nB: There is one yellow block as the base of a tower.\nC: There are two yellow blocks as the base of a tower.\nD: There are three yellow blocks as the base of a tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_191_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_191_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_191_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are two red blocks as the base of a tower.\nB: There is one yellow block as the base of a tower.\nC: There are two yellow blocks as the base of a tower.\nD: There are three yellow blocks as the base of a tower."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: there is no tower with a yellow block above a black block\nB: there is at least one tower which has a yellow block above a black block\nC: every tower has a yellow block above a black block\nD: there is a yellow block below every black block", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_192_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_192_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_192_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: there is no tower with a yellow block above a black block\nB: there is at least one tower which has a yellow block above a black block\nC: every tower has a yellow block above a black block\nD: there is a yellow block below every black block"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 yellow squares\nB: There are 4 yellow squares\nC: There are 3 yellow circles\nD: There are 2 yellow squares", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_193_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_193_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_193_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 yellow squares\nB: There are 4 yellow squares\nC: There are 3 yellow circles\nD: There are 2 yellow squares"}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are exactly two black blocks as the top of a tower.\nB: There are exactly two black blocks at the bottom of a tower.\nC: There is one black block at the top of a tower.\nD: There are exactly three black blocks as the top of a tower.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_194_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_194_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_194_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are exactly two black blocks as the top of a tower.\nB: There are exactly two black blocks at the bottom of a tower.\nC: There is one black block at the top of a tower.\nD: There are exactly three black blocks as the top of a tower."}, "output": {"output_text": "A"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a box with items of various colors.\nB: There is a box with items of only one color.\nC: There is no box with items in it.\nD: There are multiple boxes with items of one color each.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_195_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_195_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_195_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a box with items of various colors.\nB: There is a box with items of only one color.\nC: There is no box with items in it.\nD: There are multiple boxes with items of one color each."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There is a blue item floating in the middle of the box.\nB: There is a blue item closely touching right wall of a box.\nC: There is a green item touching the ceiling of a box.\nD: There is a red item closely touching the left wall of a box.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_196_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_196_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_196_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There is a blue item floating in the middle of the box.\nB: There is a blue item closely touching right wall of a box.\nC: There is a green item touching the ceiling of a box.\nD: There is a red item closely touching the left wall of a box."}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 boxes with a black item on top.\nB: There are 2 boxes with a white item on top.\nC: There are 2 boxes with a black item on top.\nD: There are 2 boxes with nothing on top.", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_197_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_197_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_197_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 boxes with a black item on top.\nB: There are 2 boxes with a white item on top.\nC: There are 2 boxes with a black item on top.\nD: There are 2 boxes with nothing on top."}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 2 white circles\nB: There are 2 black circles\nC: There are 3 black circles\nD: There are 4 black circles", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_198_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_198_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_198_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 2 white circles\nB: There are 2 black circles\nC: There are 3 black circles\nD: There are 4 black circles"}, "output": {"output_text": "B"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "nlvr", "options": "A: There are 3 white blocks\nB: There are 2 black blocks\nC: There are 3 black blocks\nD: There are 4 black blocks", "visual_input_component": "synthetic image", "input": {"input_image_path": ["2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_199_0.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_199_1.png", "2D-spatial/Image_Captioning_with_Spatial_Context/Image_Captioning_with_Spatial_Context_199_2.png"], "question": "Please correctly describe this set of images from the perspective of the spatial context.", "context": "Please correctly describe this set of images from the perspective of the spatial context.\nSelect from the following choices.\nA: There are 3 white blocks\nB: There are 2 black blocks\nC: There are 3 black blocks\nD: There are 4 black blocks"}, "output": {"output_text": "C"}, "task": "Image_Captioning_with_Spatial_Context"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[755.199, 1687.366, 0.912], [762.788, 1426.72, 1.06], [630.862, 1571.41, 1.003], [798.666, 1466.0, 0.68]]\nB: [[752.983, 1266.122, 0.837], [675.965, 1325.79, 0.95], [756.034, 1628.64, 0.801], [696.028, 1386.4, 0.67]]\nC: [[753.288, 1465.266, 0.978], [728.298, 1787.05, 0.81], [812.921, 1600.32, 0.911], [834.531, 1762.1, 0.91]]\nD: [[705.473, 1565.779, 0.995], [702.703, 1568.02, 0.92], [699.933, 1570.26, 0.845], [697.471, 1572.4, 0.77]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_0_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_0_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_0_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_0_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_0_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_0_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_0_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_0_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[755.199, 1687.366, 0.912], [762.788, 1426.72, 1.06], [630.862, 1571.41, 1.003], [798.666, 1466.0, 0.68]]\nB: [[752.983, 1266.122, 0.837], [675.965, 1325.79, 0.95], [756.034, 1628.64, 0.801], [696.028, 1386.4, 0.67]]\nC: [[753.288, 1465.266, 0.978], [728.298, 1787.05, 0.81], [812.921, 1600.32, 0.911], [834.531, 1762.1, 0.91]]\nD: [[705.473, 1565.779, 0.995], [702.703, 1568.02, 0.92], [699.933, 1570.26, 0.845], [697.471, 1572.4, 0.77]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1779.824, 2603.51, 0.357], [1779.617, 2603.65, 0.307], [1779.419, 2603.795, 0.441], [1779.221, 2603.94, 0.574]]\nB: [[1820.656, 2604.08, 0.355], [1608.069, 2300.22, 0.346], [1590.874, 2776.0, 0.366], [1586.173, 2790.75, 0.602]]\nC: [[2053.203, 2562.85, 0.348], [1922.673, 2150.26, 0.297], [1762.465, 2275.213, 0.516], [1794.318, 2966.29, 0.652]]\nD: [[1676.53, 2378.45, 0.304], [1630.8, 2506.41, 0.34], [1460.959, 2537.73, 0.431], [1807.291, 2750.98, 0.686]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_1_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_1_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_1_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_1_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_1_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_1_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_1_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_1_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1779.824, 2603.51, 0.357], [1779.617, 2603.65, 0.307], [1779.419, 2603.795, 0.441], [1779.221, 2603.94, 0.574]]\nB: [[1820.656, 2604.08, 0.355], [1608.069, 2300.22, 0.346], [1590.874, 2776.0, 0.366], [1586.173, 2790.75, 0.602]]\nC: [[2053.203, 2562.85, 0.348], [1922.673, 2150.26, 0.297], [1762.465, 2275.213, 0.516], [1794.318, 2966.29, 0.652]]\nD: [[1676.53, 2378.45, 0.304], [1630.8, 2506.41, 0.34], [1460.959, 2537.73, 0.431], [1807.291, 2750.98, 0.686]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[648.721, 1650.064, 0.332], [648.899, 1649.775, 0.623], [649.829, 1649.485, 1.045], [649.829, 1649.485, 1.07]]\nB: [[652.771, 1330.238, 0.27], [755.559, 1907.786, 0.731], [646.182, 1892.589, 1.216], [597.495, 1779.123, 0.96]]\nC: [[699.141, 1374.83, 0.288], [751.036, 1823.862, 0.739], [640.56, 1789.673, 1.201], [595.069, 1390.425, 1.03]]\nD: [[747.646, 1793.494, 0.307], [651.728, 1395.546, 0.51], [557.034, 1729.201, 1.22], [743.254, 1745.25, 1.28]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_2_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_2_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_2_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_2_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_2_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_2_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_2_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_2_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[648.721, 1650.064, 0.332], [648.899, 1649.775, 0.623], [649.829, 1649.485, 1.045], [649.829, 1649.485, 1.07]]\nB: [[652.771, 1330.238, 0.27], [755.559, 1907.786, 0.731], [646.182, 1892.589, 1.216], [597.495, 1779.123, 0.96]]\nC: [[699.141, 1374.83, 0.288], [751.036, 1823.862, 0.739], [640.56, 1789.673, 1.201], [595.069, 1390.425, 1.03]]\nD: [[747.646, 1793.494, 0.307], [651.728, 1395.546, 0.51], [557.034, 1729.201, 1.22], [743.254, 1745.25, 1.28]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[372.341, 646.643, 0.41], [323.457, 728.14, 0.355], [328.402, 680.116, 0.356], [304.89, 638.729, 0.37]]\nB: [[374.71, 547.041, 0.452], [266.865, 747.941, 0.359], [360.504, 710.201, 0.414], [289.281, 637.508, 0.34]]\nC: [[324.105, 664.423, 0.389], [324.125, 664.423, 0.395], [324.145, 664.423, 0.402], [324.165, 664.423, 0.409]]\nD: [[382.975, 542.454, 0.448], [273.435, 575.926, 0.36], [306.415, 582.477, 0.37], [367.698, 624.849, 0.412]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_3_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_3_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_3_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_3_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_3_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_3_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_3_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_3_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[372.341, 646.643, 0.41], [323.457, 728.14, 0.355], [328.402, 680.116, 0.356], [304.89, 638.729, 0.37]]\nB: [[374.71, 547.041, 0.452], [266.865, 747.941, 0.359], [360.504, 710.201, 0.414], [289.281, 637.508, 0.34]]\nC: [[324.105, 664.423, 0.389], [324.125, 664.423, 0.395], [324.145, 664.423, 0.402], [324.165, 664.423, 0.409]]\nD: [[382.975, 542.454, 0.448], [273.435, 575.926, 0.36], [306.415, 582.477, 0.37], [367.698, 624.849, 0.412]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[319.582, 1213.1, 0.433], [414.088, 1032.0, 0.628], [421.328, 1137.51, 0.496], [344.955, 1253.44, 0.638]]\nB: [[363.433, 1098.33, 0.529], [363.433, 1098.33, 0.564], [363.433, 1098.33, 0.599], [363.433, 1098.33, 0.634]]\nC: [[310.015, 1243.97, 0.462], [343.153, 1122.0, 0.606], [333.209, 1019.58, 0.517], [431.855, 1307.51, 0.556]]\nD: [[300.468, 996.48, 0.537], [331.062, 1300.52, 0.537], [400.879, 1176.8, 0.602], [389.732, 1170.04, 0.637]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_4_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_4_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_4_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_4_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_4_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_4_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_4_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_4_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[319.582, 1213.1, 0.433], [414.088, 1032.0, 0.628], [421.328, 1137.51, 0.496], [344.955, 1253.44, 0.638]]\nB: [[363.433, 1098.33, 0.529], [363.433, 1098.33, 0.564], [363.433, 1098.33, 0.599], [363.433, 1098.33, 0.634]]\nC: [[310.015, 1243.97, 0.462], [343.153, 1122.0, 0.606], [333.209, 1019.58, 0.517], [431.855, 1307.51, 0.556]]\nD: [[300.468, 996.48, 0.537], [331.062, 1300.52, 0.537], [400.879, 1176.8, 0.602], [389.732, 1170.04, 0.637]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[393.191, 899.659, 0.591], [332.44, 1277.512, 0.54], [378.779, 1199.743, 0.483], [388.415, 1186.22, 0.761]]\nB: [[373.967, 1296.428, 0.56], [468.08, 1301.812, 0.52], [423.341, 1242.289, 0.478], [463.453, 1026.04, 0.769]]\nC: [[396.335, 1122.142, 0.513], [395.62, 1122.119, 0.55], [394.907, 1122.104, 0.586], [392.701, 1122.16, 0.734]]\nD: [[366.604, 1119.109, 0.592], [355.44, 1130.172, 0.57], [469.284, 957.093, 0.569], [384.2, 1040.44, 0.813]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_5_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_5_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_5_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_5_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_5_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_5_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_5_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_5_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[393.191, 899.659, 0.591], [332.44, 1277.512, 0.54], [378.779, 1199.743, 0.483], [388.415, 1186.22, 0.761]]\nB: [[373.967, 1296.428, 0.56], [468.08, 1301.812, 0.52], [423.341, 1242.289, 0.478], [463.453, 1026.04, 0.769]]\nC: [[396.335, 1122.142, 0.513], [395.62, 1122.119, 0.55], [394.907, 1122.104, 0.586], [392.701, 1122.16, 0.734]]\nD: [[366.604, 1119.109, 0.592], [355.44, 1130.172, 0.57], [469.284, 957.093, 0.569], [384.2, 1040.44, 0.813]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1912.796, 2415.138, 0.226], [1612.76, 2000.136, 0.469], [1650.05, 2082.715, 0.705], [1870.661, 2666.852, 0.889]]\nB: [[2044.921, 2427.821, 0.251], [2197.918, 2811.408, 0.435], [1594.209, 2091.568, 0.541], [1595.884, 2911.557, 0.739]]\nC: [[1855.648, 2492.891, 0.267], [1855.098, 2493.555, 0.467], [1854.597, 2494.197, 0.634], [1854.096, 2494.841, 0.801]]\nD: [[1651.93, 2405.938, 0.246], [2153.625, 2215.89, 0.442], [1530.771, 2046.654, 0.746], [2201.19, 2084.755, 0.722]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_6_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_6_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_6_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_6_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_6_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_6_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_6_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_6_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1912.796, 2415.138, 0.226], [1612.76, 2000.136, 0.469], [1650.05, 2082.715, 0.705], [1870.661, 2666.852, 0.889]]\nB: [[2044.921, 2427.821, 0.251], [2197.918, 2811.408, 0.435], [1594.209, 2091.568, 0.541], [1595.884, 2911.557, 0.739]]\nC: [[1855.648, 2492.891, 0.267], [1855.098, 2493.555, 0.467], [1854.597, 2494.197, 0.634], [1854.096, 2494.841, 0.801]]\nD: [[1651.93, 2405.938, 0.246], [2153.625, 2215.89, 0.442], [1530.771, 2046.654, 0.746], [2201.19, 2084.755, 0.722]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1835.457, 2530.979, -0.6], [1831.738, 2535.381, -0.475], [1828.016, 2539.789, -0.35], [1823.826, 2544.548, -0.226]]\nB: [[1728.159, 2657.767, -0.6], [1671.146, 2191.293, -0.456], [1889.85, 2711.258, -0.39], [1500.543, 2142.17, -0.266]]\nC: [[1868.34, 2656.949, -0.6], [1847.319, 3027.849, -0.442], [1621.372, 2206.666, -0.29], [1944.205, 2824.5, -0.259]]\nD: [[1798.206, 2853.486, -0.5], [1737.945, 2982.299, -0.415], [1782.37, 2464.903, -0.33], [2009.484, 2271.222, -0.188]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_7_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_7_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_7_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_7_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_7_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_7_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_7_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_7_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1835.457, 2530.979, -0.6], [1831.738, 2535.381, -0.475], [1828.016, 2539.789, -0.35], [1823.826, 2544.548, -0.226]]\nB: [[1728.159, 2657.767, -0.6], [1671.146, 2191.293, -0.456], [1889.85, 2711.258, -0.39], [1500.543, 2142.17, -0.266]]\nC: [[1868.34, 2656.949, -0.6], [1847.319, 3027.849, -0.442], [1621.372, 2206.666, -0.29], [1944.205, 2824.5, -0.259]]\nD: [[1798.206, 2853.486, -0.5], [1737.945, 2982.299, -0.415], [1782.37, 2464.903, -0.33], [2009.484, 2271.222, -0.188]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2091.918, 2820.157, -0.699], [2085.742, 2471.429, -0.7], [1560.979, 2272.985, -0.534], [1615.9, 2272.87, -0.391]]\nB: [[1807.911, 2559.964, -0.854], [1804.558, 2563.859, -0.725], [1801.201, 2567.758, -0.596], [1797.7, 2572.03, -0.433]]\nC: [[2128.41, 2627.282, -0.79], [1547.739, 2837.704, -0.791], [1686.195, 2104.816, -0.492], [1645.0, 2561.72, -0.364]]\nD: [[1649.251, 2758.133, -0.686], [1533.206, 2890.142, -0.825], [2007.154, 2531.762, -0.478], [2127.3, 2070.45, -0.347]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_8_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_8_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_8_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_8_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_8_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_8_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_8_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_8_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2091.918, 2820.157, -0.699], [2085.742, 2471.429, -0.7], [1560.979, 2272.985, -0.534], [1615.9, 2272.87, -0.391]]\nB: [[1807.911, 2559.964, -0.854], [1804.558, 2563.859, -0.725], [1801.201, 2567.758, -0.596], [1797.7, 2572.03, -0.433]]\nC: [[2128.41, 2627.282, -0.79], [1547.739, 2837.704, -0.791], [1686.195, 2104.816, -0.492], [1645.0, 2561.72, -0.364]]\nD: [[1649.251, 2758.133, -0.686], [1533.206, 2890.142, -0.825], [2007.154, 2531.762, -0.478], [2127.3, 2070.45, -0.347]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[437.202, 1086.964, 0.692], [437.221, 1087.01, 0.817], [437.244, 1087.066, 0.842], [437.244, 1087.066, 0.842]]\nB: [[357.432, 1159.623, 0.607], [351.412, 1296.28, 0.836], [516.977, 1219.588, 0.769], [425.277, 1005.318, 0.772]]\nC: [[520.991, 1274.564, 0.812], [478.068, 1065.93, 0.705], [398.533, 912.914, 0.73], [470.356, 1123.201, 0.712]]\nD: [[377.562, 951.154, 0.715], [472.017, 932.55, 0.727], [361.039, 1097.241, 0.701], [508.246, 1284.882, 0.804]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_9_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_9_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_9_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_9_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_9_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_9_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_9_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_9_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[437.202, 1086.964, 0.692], [437.221, 1087.01, 0.817], [437.244, 1087.066, 0.842], [437.244, 1087.066, 0.842]]\nB: [[357.432, 1159.623, 0.607], [351.412, 1296.28, 0.836], [516.977, 1219.588, 0.769], [425.277, 1005.318, 0.772]]\nC: [[520.991, 1274.564, 0.812], [478.068, 1065.93, 0.705], [398.533, 912.914, 0.73], [470.356, 1123.201, 0.712]]\nD: [[377.562, 951.154, 0.715], [472.017, 932.55, 0.727], [361.039, 1097.241, 0.701], [508.246, 1284.882, 0.804]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[506.527, 1099.076, 0.589], [420.185, 1298.755, 0.489], [362.914, 1033.947, 0.414], [396.859, 1002.99, 0.306]]\nB: [[424.014, 1100.606, 0.706], [424.133, 1100.728, 0.496], [424.173, 1100.769, 0.426], [424.212, 1100.81, 0.306]]\nC: [[456.889, 932.553, 0.793], [391.51, 1069.937, 0.527], [431.845, 933.545, 0.5], [394.898, 1320.05, 0.264]]\nD: [[378.115, 1221.413, 0.672], [347.816, 1131.373, 0.529], [364.847, 1229.038, 0.466], [397.183, 1091.0, 0.25]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_10_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_10_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_10_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_10_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_10_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_10_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_10_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_10_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[506.527, 1099.076, 0.589], [420.185, 1298.755, 0.489], [362.914, 1033.947, 0.414], [396.859, 1002.99, 0.306]]\nB: [[424.014, 1100.606, 0.706], [424.133, 1100.728, 0.496], [424.173, 1100.769, 0.426], [424.212, 1100.81, 0.306]]\nC: [[456.889, 932.553, 0.793], [391.51, 1069.937, 0.527], [431.845, 933.545, 0.5], [394.898, 1320.05, 0.264]]\nD: [[378.115, 1221.413, 0.672], [347.816, 1131.373, 0.529], [364.847, 1229.038, 0.466], [397.183, 1091.0, 0.25]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[420.731, 1013.531, 0.8], [361.227, 1037.43, 0.587], [485.796, 1006.664, 0.647], [418.217, 1072.225, 0.587]]\nB: [[374.363, 1267.963, 0.71], [402.578, 1232.818, 0.668], [434.034, 921.569, 0.52], [421.208, 1297.52, 0.506]]\nC: [[425.982, 1091.597, 0.73], [425.994, 1091.597, 0.733], [426.028, 1091.597, 0.541], [426.039, 1091.597, 0.619]]\nD: [[468.986, 997.688, 0.61], [441.053, 1239.106, 0.742], [435.348, 1170.376, 0.513], [358.562, 1151.219, 0.672]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_11_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_11_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_11_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_11_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_11_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_11_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_11_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_11_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[420.731, 1013.531, 0.8], [361.227, 1037.43, 0.587], [485.796, 1006.664, 0.647], [418.217, 1072.225, 0.587]]\nB: [[374.363, 1267.963, 0.71], [402.578, 1232.818, 0.668], [434.034, 921.569, 0.52], [421.208, 1297.52, 0.506]]\nC: [[425.982, 1091.597, 0.73], [425.994, 1091.597, 0.733], [426.028, 1091.597, 0.541], [426.039, 1091.597, 0.619]]\nD: [[468.986, 997.688, 0.61], [441.053, 1239.106, 0.742], [435.348, 1170.376, 0.513], [358.562, 1151.219, 0.672]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2016.542, 845.631, 1.13], [2028.77, 874.497, 1.083], [1971.835, 957.221, 1.15], [1681.888, 953.919, 1.139]]\nB: [[1978.335, 863.179, 0.943], [1978.33, 863.187, 1.065], [1978.325, 863.194, 1.015], [1978.319, 863.201, 0.965]]\nC: [[1640.806, 1002.654, 1.092], [2125.94, 982.727, 1.09], [1765.046, 957.217, 1.116], [2264.988, 900.054, 0.911]]\nD: [[1688.119, 734.16, 0.877], [1887.56, 864.137, 1.092], [2139.033, 980.382, 1.191], [1969.445, 813.79, 0.775]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_12_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_12_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_12_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_12_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_12_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_12_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_12_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_12_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2016.542, 845.631, 1.13], [2028.77, 874.497, 1.083], [1971.835, 957.221, 1.15], [1681.888, 953.919, 1.139]]\nB: [[1978.335, 863.179, 0.943], [1978.33, 863.187, 1.065], [1978.325, 863.194, 1.015], [1978.319, 863.201, 0.965]]\nC: [[1640.806, 1002.654, 1.092], [2125.94, 982.727, 1.09], [1765.046, 957.217, 1.116], [2264.988, 900.054, 0.911]]\nD: [[1688.119, 734.16, 0.877], [1887.56, 864.137, 1.092], [2139.033, 980.382, 1.191], [1969.445, 813.79, 0.775]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[639.138, 1624.989, -0.086], [636.359, 1627.431, -0.053], [632.807, 1630.318, 0.08], [629.158, 1633.096, 0.314]]\nB: [[543.626, 1367.896, -0.075], [653.208, 1574.861, -0.054], [757.25, 1346.07, 0.08], [540.23, 1650.674, 0.362]]\nC: [[537.409, 1426.609, -0.082], [626.472, 1686.779, -0.051], [691.803, 1387.102, 0.07], [744.081, 1369.746, 0.365]]\nD: [[557.32, 1516.073, -0.08], [526.841, 1596.276, -0.06], [611.464, 1793.408, 0.1], [674.543, 1593.857, 0.364]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_13_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_13_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_13_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_13_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_13_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_13_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_13_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_13_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[639.138, 1624.989, -0.086], [636.359, 1627.431, -0.053], [632.807, 1630.318, 0.08], [629.158, 1633.096, 0.314]]\nB: [[543.626, 1367.896, -0.075], [653.208, 1574.861, -0.054], [757.25, 1346.07, 0.08], [540.23, 1650.674, 0.362]]\nC: [[537.409, 1426.609, -0.082], [626.472, 1686.779, -0.051], [691.803, 1387.102, 0.07], [744.081, 1369.746, 0.365]]\nD: [[557.32, 1516.073, -0.08], [526.841, 1596.276, -0.06], [611.464, 1793.408, 0.1], [674.543, 1593.857, 0.364]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[407.887, 1163.323, 0.511], [407.929, 1163.41, 0.511], [407.934, 1163.409, 0.524], [407.951, 1163.403, 0.537]]\nB: [[388.853, 1125.736, 0.56], [434.747, 1231.09, 0.419], [348.138, 1361.198, 0.597], [328.283, 1154.348, 0.58]]\nC: [[374.741, 1227.419, 0.46], [461.986, 1151.55, 0.428], [486.887, 1127.556, 0.491], [354.147, 1359.889, 0.505]]\nD: [[471.139, 1113.037, 0.544], [333.263, 956.23, 0.501], [355.318, 1217.053, 0.538], [456.915, 1087.324, 0.512]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_14_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_14_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_14_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_14_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_14_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_14_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_14_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_14_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[407.887, 1163.323, 0.511], [407.929, 1163.41, 0.511], [407.934, 1163.409, 0.524], [407.951, 1163.403, 0.537]]\nB: [[388.853, 1125.736, 0.56], [434.747, 1231.09, 0.419], [348.138, 1361.198, 0.597], [328.283, 1154.348, 0.58]]\nC: [[374.741, 1227.419, 0.46], [461.986, 1151.55, 0.428], [486.887, 1127.556, 0.491], [354.147, 1359.889, 0.505]]\nD: [[471.139, 1113.037, 0.544], [333.263, 956.23, 0.501], [355.318, 1217.053, 0.538], [456.915, 1087.324, 0.512]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1269.546, 1024.852, 1.042], [1269.744, 1025.178, 1.042], [1270.216, 1025.754, 0.992], [1270.837, 1026.506, 1.042]]\nB: [[1423.653, 1173.455, 1.097], [1300.351, 866.909, 0.934], [1179.097, 946.025, 1.104], [1411.454, 1138.532, 1.187]]\nC: [[1145.602, 896.06, 1.073], [1144.171, 966.324, 1.002], [1499.487, 1042.061, 0.91], [1482.233, 956.251, 1.138]]\nD: [[1137.684, 944.23, 0.905], [1316.46, 1218.835, 0.861], [1509.763, 1193.692, 1.048], [1361.774, 1108.409, 0.891]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_15_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_15_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_15_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_15_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_15_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_15_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_15_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_15_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1269.546, 1024.852, 1.042], [1269.744, 1025.178, 1.042], [1270.216, 1025.754, 0.992], [1270.837, 1026.506, 1.042]]\nB: [[1423.653, 1173.455, 1.097], [1300.351, 866.909, 0.934], [1179.097, 946.025, 1.104], [1411.454, 1138.532, 1.187]]\nC: [[1145.602, 896.06, 1.073], [1144.171, 966.324, 1.002], [1499.487, 1042.061, 0.91], [1482.233, 956.251, 1.138]]\nD: [[1137.684, 944.23, 0.905], [1316.46, 1218.835, 0.861], [1509.763, 1193.692, 1.048], [1361.774, 1108.409, 0.891]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1251.433, 1108.948, 0.433], [1176.759, 1115.714, 0.456], [1227.53, 991.616, 0.633], [1095.585, 1183.286, 0.618]]\nB: [[1509.989, 949.628, 0.539], [1350.384, 1212.22, 0.56], [1071.64, 893.308, 0.484], [1153.706, 1063.833, 0.645]]\nC: [[1298.993, 1034.258, 0.529], [1299.542, 1034.749, 0.554], [1300.09, 1035.239, 0.579], [1300.639, 1035.729, 0.604]]\nD: [[1378.947, 975.996, 0.598], [1493.813, 900.58, 0.493], [1370.14, 1033.836, 0.656], [1047.788, 1106.271, 0.659]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_16_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_16_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_16_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_16_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_16_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_16_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_16_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_16_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1251.433, 1108.948, 0.433], [1176.759, 1115.714, 0.456], [1227.53, 991.616, 0.633], [1095.585, 1183.286, 0.618]]\nB: [[1509.989, 949.628, 0.539], [1350.384, 1212.22, 0.56], [1071.64, 893.308, 0.484], [1153.706, 1063.833, 0.645]]\nC: [[1298.993, 1034.258, 0.529], [1299.542, 1034.749, 0.554], [1300.09, 1035.239, 0.579], [1300.639, 1035.729, 0.604]]\nD: [[1378.947, 975.996, 0.598], [1493.813, 900.58, 0.493], [1370.14, 1033.836, 0.656], [1047.788, 1106.271, 0.659]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[306.186, 763.667, 1.488], [378.546, 697.6, 1.528], [320.79, 550.53, 1.74], [377.634, 523.623, 1.596]]\nB: [[387.559, 726.167, 1.211], [356.987, 561.8, 1.228], [377.54, 655.25, 1.8], [372.07, 602.526, 1.352]]\nC: [[392.768, 743.908, 1.542], [292.481, 723.4, 1.31], [330.74, 682.85, 1.79], [283.31, 638.538, 1.433]]\nD: [[348.147, 646.209, 1.444], [348.144, 646.2, 1.482], [348.14, 646.19, 1.52], [348.137, 646.181, 1.559]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_17_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_17_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_17_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_17_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_17_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_17_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_17_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_17_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[306.186, 763.667, 1.488], [378.546, 697.6, 1.528], [320.79, 550.53, 1.74], [377.634, 523.623, 1.596]]\nB: [[387.559, 726.167, 1.211], [356.987, 561.8, 1.228], [377.54, 655.25, 1.8], [372.07, 602.526, 1.352]]\nC: [[392.768, 743.908, 1.542], [292.481, 723.4, 1.31], [330.74, 682.85, 1.79], [283.31, 638.538, 1.433]]\nD: [[348.147, 646.209, 1.444], [348.144, 646.2, 1.482], [348.14, 646.19, 1.52], [348.137, 646.181, 1.559]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1706.39, 1019.22, 0.455], [2191.986, 926.298, 0.316], [1675.02, 886.17, 0.299], [1941.62, 757.75, 0.341]]\nB: [[2247.24, 737.46, 0.384], [1527.442, 724.25, 0.347], [1575.02, 976.52, 0.327], [1630.08, 842.33, 0.316]]\nC: [[2075.96, 1012.24, 0.409], [1869.437, 795.581, 0.371], [2223.74, 1044.39, 0.397], [1567.73, 972.01, 0.379]]\nD: [[1895.77, 878.51, 0.433], [1895.672, 878.506, 0.338], [1895.77, 878.51, 0.343], [1895.77, 878.51, 0.393]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_18_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_18_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_18_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_18_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_18_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_18_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_18_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_18_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1706.39, 1019.22, 0.455], [2191.986, 926.298, 0.316], [1675.02, 886.17, 0.299], [1941.62, 757.75, 0.341]]\nB: [[2247.24, 737.46, 0.384], [1527.442, 724.25, 0.347], [1575.02, 976.52, 0.327], [1630.08, 842.33, 0.316]]\nC: [[2075.96, 1012.24, 0.409], [1869.437, 795.581, 0.371], [2223.74, 1044.39, 0.397], [1567.73, 972.01, 0.379]]\nD: [[1895.77, 878.51, 0.433], [1895.672, 878.506, 0.338], [1895.77, 878.51, 0.343], [1895.77, 878.51, 0.393]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2234.916, 722.86, 0.39], [1901.638, 1017.1, 0.487], [1734.516, 780.849, 0.344], [1885.643, 867.521, 0.263]]\nB: [[1568.94, 897.301, 0.449], [2000.828, 702.741, 0.446], [1573.358, 1014.024, 0.477], [1578.275, 964.592, 0.265]]\nC: [[2141.663, 908.252, 0.394], [1802.749, 988.498, 0.349], [1873.147, 986.016, 0.413], [2189.02, 894.117, 0.265]]\nD: [[1895.727, 877.737, 0.418], [1895.727, 877.737, 0.418], [1895.727, 877.737, 0.418], [1895.716, 877.802, 0.292]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_19_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_19_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_19_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_19_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_19_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_19_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_19_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_19_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2234.916, 722.86, 0.39], [1901.638, 1017.1, 0.487], [1734.516, 780.849, 0.344], [1885.643, 867.521, 0.263]]\nB: [[1568.94, 897.301, 0.449], [2000.828, 702.741, 0.446], [1573.358, 1014.024, 0.477], [1578.275, 964.592, 0.265]]\nC: [[2141.663, 908.252, 0.394], [1802.749, 988.498, 0.349], [1873.147, 986.016, 0.413], [2189.02, 894.117, 0.265]]\nD: [[1895.727, 877.737, 0.418], [1895.727, 877.737, 0.418], [1895.727, 877.737, 0.418], [1895.716, 877.802, 0.292]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[336.27, 647.992, 0.436], [346.74, 708.566, 0.649], [354.42, 746.112, 0.69], [376.74, 611.59, 0.61]]\nB: [[340.58, 661.842, 0.526], [340.58, 661.842, 0.576], [340.58, 661.842, 0.626], [340.58, 661.842, 0.676]]\nC: [[387.54, 767.29, 0.509], [330.38, 600.327, 0.526], [387.34, 562.731, 0.738], [287.65, 743.046, 0.73]]\nD: [[347.27, 591.306, 0.458], [329.15, 678.06, 0.571], [380.55, 710.329, 0.52], [408.38, 545.098, 0.802]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_20_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_20_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_20_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_20_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_20_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_20_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_20_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_20_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[336.27, 647.992, 0.436], [346.74, 708.566, 0.649], [354.42, 746.112, 0.69], [376.74, 611.59, 0.61]]\nB: [[340.58, 661.842, 0.526], [340.58, 661.842, 0.576], [340.58, 661.842, 0.626], [340.58, 661.842, 0.676]]\nC: [[387.54, 767.29, 0.509], [330.38, 600.327, 0.526], [387.34, 562.731, 0.738], [287.65, 743.046, 0.73]]\nD: [[347.27, 591.306, 0.458], [329.15, 678.06, 0.571], [380.55, 710.329, 0.52], [408.38, 545.098, 0.802]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[641.894, 1481.081, -0.116], [755.139, 1696.093, 0.085], [744.337, 1645.874, -0.021], [549.883, 1475.291, 0.091]]\nB: [[609.159, 1822.97, -0.114], [725.77, 1759.652, 0.076], [541.265, 1644.526, -0.022], [634.034, 1389.951, 0.08]]\nC: [[639.585, 1606.675, -0.122], [640.106, 1606.245, 0.078], [640.626, 1605.815, -0.022], [641.147, 1605.384, 0.078]]\nD: [[553.206, 1422.477, -0.138], [630.222, 1490.963, 0.087], [720.491, 1414.036, -0.022], [698.708, 1478.6, 0.08]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_21_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_21_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_21_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_21_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_21_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_21_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_21_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_21_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[641.894, 1481.081, -0.116], [755.139, 1696.093, 0.085], [744.337, 1645.874, -0.021], [549.883, 1475.291, 0.091]]\nB: [[609.159, 1822.97, -0.114], [725.77, 1759.652, 0.076], [541.265, 1644.526, -0.022], [634.034, 1389.951, 0.08]]\nC: [[639.585, 1606.675, -0.122], [640.106, 1606.245, 0.078], [640.626, 1605.815, -0.022], [641.147, 1605.384, 0.078]]\nD: [[553.206, 1422.477, -0.138], [630.222, 1490.963, 0.087], [720.491, 1414.036, -0.022], [698.708, 1478.6, 0.08]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1654.688, 731.801, 1.203], [1825.149, 800.76, 1.006], [1536.825, 955.686, 1.262], [2011.454, 920.864, 1.228]]\nB: [[1716.132, 870.368, 1.137], [1714.324, 869.208, 1.137], [1712.096, 868.352, 1.187], [1709.574, 867.934, 1.232]]\nC: [[1523.418, 951.06, 0.924], [1452.823, 761.345, 1.206], [2023.787, 900.571, 0.99], [1938.184, 774.207, 1.182]]\nD: [[1653.54, 790.02, 1.21], [1790.64, 885.935, 1.33], [1634.81, 909.54, 1.184], [1807.277, 934.183, 1.469]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_22_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_22_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_22_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_22_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_22_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_22_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_22_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_22_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1654.688, 731.801, 1.203], [1825.149, 800.76, 1.006], [1536.825, 955.686, 1.262], [2011.454, 920.864, 1.228]]\nB: [[1716.132, 870.368, 1.137], [1714.324, 869.208, 1.137], [1712.096, 868.352, 1.187], [1709.574, 867.934, 1.232]]\nC: [[1523.418, 951.06, 0.924], [1452.823, 761.345, 1.206], [2023.787, 900.571, 0.99], [1938.184, 774.207, 1.182]]\nD: [[1653.54, 790.02, 1.21], [1790.64, 885.935, 1.33], [1634.81, 909.54, 1.184], [1807.277, 934.183, 1.469]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1943.293, 1014.905, 2.05], [1962.947, 881.292, 1.464], [1771.641, 818.432, 1.783], [2024.383, 893.384, 1.855]]\nB: [[1842.723, 879.95, 1.901], [2117.17, 1006.474, 1.903], [1573.854, 942.118, 1.735], [2097.928, 1012.432, 1.953]]\nC: [[1897.834, 865.209, 1.738], [1897.834, 865.195, 1.688], [1897.833, 865.116, 1.688], [1897.831, 865.001, 1.688]]\nD: [[1801.762, 704.249, 1.493], [1762.225, 848.144, 1.446], [1867.693, 770.539, 1.836], [2098.827, 762.104, 1.81]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_23_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_23_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_23_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_23_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_23_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_23_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_23_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_23_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1943.293, 1014.905, 2.05], [1962.947, 881.292, 1.464], [1771.641, 818.432, 1.783], [2024.383, 893.384, 1.855]]\nB: [[1842.723, 879.95, 1.901], [2117.17, 1006.474, 1.903], [1573.854, 942.118, 1.735], [2097.928, 1012.432, 1.953]]\nC: [[1897.834, 865.209, 1.738], [1897.834, 865.195, 1.688], [1897.833, 865.116, 1.688], [1897.831, 865.001, 1.688]]\nD: [[1801.762, 704.249, 1.493], [1762.225, 848.144, 1.446], [1867.693, 770.539, 1.836], [2098.827, 762.104, 1.81]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[251.423, 613.532, -0.224], [353.651, 580.245, -0.187], [307.419, 820.39, -0.19], [319.555, 661.929, -0.115]]\nB: [[288.517, 703.944, -0.206], [287.575, 632.764, -0.222], [372.62, 616.315, -0.154], [261.943, 809.962, -0.108]]\nC: [[279.61, 776.103, -0.238], [372.908, 643.544, -0.172], [347.733, 585.413, -0.159], [339.729, 666.886, -0.117]]\nD: [[311.976, 694.922, -0.216], [311.533, 694.408, -0.203], [311.103, 693.883, -0.191], [309.589, 691.756, -0.099]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_24_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_24_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_24_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_24_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_24_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_24_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_24_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_24_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[251.423, 613.532, -0.224], [353.651, 580.245, -0.187], [307.419, 820.39, -0.19], [319.555, 661.929, -0.115]]\nB: [[288.517, 703.944, -0.206], [287.575, 632.764, -0.222], [372.62, 616.315, -0.154], [261.943, 809.962, -0.108]]\nC: [[279.61, 776.103, -0.238], [372.908, 643.544, -0.172], [347.733, 585.413, -0.159], [339.729, 666.886, -0.117]]\nD: [[311.976, 694.922, -0.216], [311.533, 694.408, -0.203], [311.103, 693.883, -0.191], [309.589, 691.756, -0.099]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[428.593, 999.538, 0.927], [376.399, 1068.754, 0.828], [470.584, 1252.944, 0.961], [513.123, 1108.855, 0.935]]\nB: [[449.491, 963.875, 0.968], [378.432, 1021.223, 1.012], [349.93, 1322.277, 1.125], [411.187, 1019.406, 0.996]]\nC: [[447.511, 981.997, 0.973], [404.158, 1082.968, 0.919], [454.929, 1283.771, 0.917], [471.926, 1109.792, 0.83]]\nD: [[435.351, 1103.132, 0.814], [435.351, 1103.132, 0.964], [435.351, 1103.132, 1.014], [435.351, 1103.132, 0.989]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_25_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_25_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_25_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_25_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_25_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_25_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_25_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_25_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[428.593, 999.538, 0.927], [376.399, 1068.754, 0.828], [470.584, 1252.944, 0.961], [513.123, 1108.855, 0.935]]\nB: [[449.491, 963.875, 0.968], [378.432, 1021.223, 1.012], [349.93, 1322.277, 1.125], [411.187, 1019.406, 0.996]]\nC: [[447.511, 981.997, 0.973], [404.158, 1082.968, 0.919], [454.929, 1283.771, 0.917], [471.926, 1109.792, 0.83]]\nD: [[435.351, 1103.132, 0.814], [435.351, 1103.132, 0.964], [435.351, 1103.132, 1.014], [435.351, 1103.132, 0.989]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1289.156, 997.931, 0.17], [1528.988, 1149.542, 0.135], [1524.67, 1103.565, 0.144], [1254.51, 1059.655, 0.132]]\nB: [[1576.762, 1083.802, 0.16], [1394.53, 1020.578, 0.13], [1145.932, 1107.624, 0.169], [1436.14, 1231.523, 0.156]]\nC: [[1340.124, 1032.575, 0.154], [1340.123, 1032.575, 0.154], [1340.121, 1032.574, 0.154], [1340.12, 1032.574, 0.154]]\nD: [[1216.577, 1183.272, 0.123], [1258.5, 1034.393, 0.163], [1273.558, 1228.419, 0.14], [1288.46, 870.176, 0.174]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_26_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_26_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_26_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_26_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_26_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_26_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_26_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_26_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1289.156, 997.931, 0.17], [1528.988, 1149.542, 0.135], [1524.67, 1103.565, 0.144], [1254.51, 1059.655, 0.132]]\nB: [[1576.762, 1083.802, 0.16], [1394.53, 1020.578, 0.13], [1145.932, 1107.624, 0.169], [1436.14, 1231.523, 0.156]]\nC: [[1340.124, 1032.575, 0.154], [1340.123, 1032.575, 0.154], [1340.121, 1032.574, 0.154], [1340.12, 1032.574, 0.154]]\nD: [[1216.577, 1183.272, 0.123], [1258.5, 1034.393, 0.163], [1273.558, 1228.419, 0.14], [1288.46, 870.176, 0.174]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1255.784, 1121.555, 1.33], [1075.123, 1055.841, 1.22], [1441.444, 1208.639, 1.1], [1537.429, 1076.298, 1.45]]\nB: [[1100.277, 1164.491, 1.56], [1180.448, 1259.127, 1.17], [1475.037, 1060.06, 1.36], [1311.756, 864.536, 1.05]]\nC: [[1328.793, 876.335, 1.12], [1429.236, 996.25, 1.26], [1195.871, 932.001, 1.51], [1480.133, 1028.558, 1.25]]\nD: [[1328.425, 1052.566, 1.31], [1328.425, 1052.566, 1.31], [1328.425, 1052.566, 1.31], [1328.425, 1052.566, 1.31]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_27_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_27_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_27_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_27_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_27_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_27_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_27_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_27_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1255.784, 1121.555, 1.33], [1075.123, 1055.841, 1.22], [1441.444, 1208.639, 1.1], [1537.429, 1076.298, 1.45]]\nB: [[1100.277, 1164.491, 1.56], [1180.448, 1259.127, 1.17], [1475.037, 1060.06, 1.36], [1311.756, 864.536, 1.05]]\nC: [[1328.793, 876.335, 1.12], [1429.236, 996.25, 1.26], [1195.871, 932.001, 1.51], [1480.133, 1028.558, 1.25]]\nD: [[1328.425, 1052.566, 1.31], [1328.425, 1052.566, 1.31], [1328.425, 1052.566, 1.31], [1328.425, 1052.566, 1.31]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1273.894, 1072.524, 0.908], [1273.894, 1072.524, 0.909], [1273.894, 1072.524, 0.911], [1273.893, 1072.523, 0.912]]\nB: [[1252.346, 1105.514, 0.902], [1209.789, 1085.191, 0.984], [1114.268, 935.639, 0.74], [1170.16, 987.263, 0.918]]\nC: [[1108.639, 1162.182, 1.069], [1297.456, 1226.014, 0.862], [1466.955, 1006.358, 0.987], [1135.299, 1250.877, 0.943]]\nD: [[1221.891, 927.735, 0.939], [1126.972, 1155.177, 0.838], [1313.844, 1145.354, 1.042], [1328.412, 1083.367, 0.762]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_28_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_28_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_28_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_28_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_28_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_28_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_28_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_28_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1273.894, 1072.524, 0.908], [1273.894, 1072.524, 0.909], [1273.894, 1072.524, 0.911], [1273.893, 1072.523, 0.912]]\nB: [[1252.346, 1105.514, 0.902], [1209.789, 1085.191, 0.984], [1114.268, 935.639, 0.74], [1170.16, 987.263, 0.918]]\nC: [[1108.639, 1162.182, 1.069], [1297.456, 1226.014, 0.862], [1466.955, 1006.358, 0.987], [1135.299, 1250.877, 0.943]]\nD: [[1221.891, 927.735, 0.939], [1126.972, 1155.177, 0.838], [1313.844, 1145.354, 1.042], [1328.412, 1083.367, 0.762]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[522.342, 1943.251, 0.31], [505.853, 1457.715, 0.375], [513.011, 1502.032, 0.633], [529.48, 1609.413, 0.729]]\nB: [[626.523, 1972.698, 0.374], [529.275, 1724.592, 0.459], [517.251, 1365.431, 0.651], [714.07, 1806.899, 0.579]]\nC: [[576.087, 1806.167, 0.315], [734.652, 1339.382, 0.394], [725.143, 1697.177, 0.608], [592.16, 1326.812, 0.692]]\nD: [[622.249, 1646.081, 0.321], [621.683, 1646.405, 0.446], [621.109, 1646.715, 0.571], [620.64, 1647.021, 0.721]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_29_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_29_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_29_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_29_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_29_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_29_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_29_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_29_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[522.342, 1943.251, 0.31], [505.853, 1457.715, 0.375], [513.011, 1502.032, 0.633], [529.48, 1609.413, 0.729]]\nB: [[626.523, 1972.698, 0.374], [529.275, 1724.592, 0.459], [517.251, 1365.431, 0.651], [714.07, 1806.899, 0.579]]\nC: [[576.087, 1806.167, 0.315], [734.652, 1339.382, 0.394], [725.143, 1697.177, 0.608], [592.16, 1326.812, 0.692]]\nD: [[622.249, 1646.081, 0.321], [621.683, 1646.405, 0.446], [621.109, 1646.715, 0.571], [620.64, 1647.021, 0.721]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1796.561, 874.996, 1.254], [1796.561, 874.982, 1.216], [1796.561, 874.969, 1.182], [1796.561, 874.957, 1.151]]\nB: [[1829.822, 1005.261, 1.194], [2129.106, 967.913, 1.335], [1439.644, 885.763, 1.155], [2034.051, 719.497, 0.987]]\nC: [[2134.229, 737.814, 1.149], [1953.993, 1047.896, 1.349], [1612.579, 940.305, 1.146], [1599.447, 982.485, 1.365]]\nD: [[1699.287, 941.961, 1.224], [1590.817, 729.191, 1.195], [1711.432, 908.722, 0.971], [1659.459, 924.897, 1.335]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_30_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_30_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_30_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_30_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_30_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_30_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_30_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_30_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1796.561, 874.996, 1.254], [1796.561, 874.982, 1.216], [1796.561, 874.969, 1.182], [1796.561, 874.957, 1.151]]\nB: [[1829.822, 1005.261, 1.194], [2129.106, 967.913, 1.335], [1439.644, 885.763, 1.155], [2034.051, 719.497, 0.987]]\nC: [[2134.229, 737.814, 1.149], [1953.993, 1047.896, 1.349], [1612.579, 940.305, 1.146], [1599.447, 982.485, 1.365]]\nD: [[1699.287, 941.961, 1.224], [1590.817, 729.191, 1.195], [1711.432, 908.722, 0.971], [1659.459, 924.897, 1.335]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[406.663, 1099.631, 0.814], [406.711, 1099.639, 0.923], [406.735, 1099.643, 0.978], [406.717, 1099.695, 0.749]]\nB: [[427.835, 1064.967, 0.714], [484.647, 916.921, 0.994], [411.142, 919.994, 1.029], [362.349, 1103.394, 0.701]]\nC: [[396.877, 1112.011, 0.828], [415.047, 1175.011, 0.772], [440.647, 980.302, 0.825], [395.393, 899.719, 0.603]]\nD: [[473.72, 956.4, 0.8], [485.155, 1094.253, 0.884], [398.711, 1081.924, 0.932], [430.802, 1000.92, 0.78]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_31_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_31_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_31_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_31_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_31_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_31_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_31_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_31_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[406.663, 1099.631, 0.814], [406.711, 1099.639, 0.923], [406.735, 1099.643, 0.978], [406.717, 1099.695, 0.749]]\nB: [[427.835, 1064.967, 0.714], [484.647, 916.921, 0.994], [411.142, 919.994, 1.029], [362.349, 1103.394, 0.701]]\nC: [[396.877, 1112.011, 0.828], [415.047, 1175.011, 0.772], [440.647, 980.302, 0.825], [395.393, 899.719, 0.603]]\nD: [[473.72, 956.4, 0.8], [485.155, 1094.253, 0.884], [398.711, 1081.924, 0.932], [430.802, 1000.92, 0.78]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1313.621, 933.434, 0.218], [1514.36, 1049.794, 0.194], [1263.349, 1108.661, 0.157], [1490.47, 980.609, 0.195]]\nB: [[1232.867, 1016.208, 0.213], [1250.875, 1010.148, 0.221], [1205.37, 1035.121, 0.184], [1092.698, 953.727, 0.188]]\nC: [[1472.729, 957.241, 0.173], [1510.795, 1241.776, 0.219], [1118.45, 1223.791, 0.168], [1218.898, 1085.684, 0.171]]\nD: [[1337.482, 1035.208, 0.186], [1337.482, 1035.208, 0.186], [1337.482, 1035.208, 0.186], [1337.482, 1035.208, 0.186]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_32_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_32_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_32_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_32_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_32_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_32_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_32_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_32_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1313.621, 933.434, 0.218], [1514.36, 1049.794, 0.194], [1263.349, 1108.661, 0.157], [1490.47, 980.609, 0.195]]\nB: [[1232.867, 1016.208, 0.213], [1250.875, 1010.148, 0.221], [1205.37, 1035.121, 0.184], [1092.698, 953.727, 0.188]]\nC: [[1472.729, 957.241, 0.173], [1510.795, 1241.776, 0.219], [1118.45, 1223.791, 0.168], [1218.898, 1085.684, 0.171]]\nD: [[1337.482, 1035.208, 0.186], [1337.482, 1035.208, 0.186], [1337.482, 1035.208, 0.186], [1337.482, 1035.208, 0.186]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1992.775, 875.132, 0.942], [1985.564, 874.76, 0.95], [1978.377, 874.483, 0.958], [1971.974, 874.315, 0.986]]\nB: [[1755.791, 1044.883, 0.825], [1877.163, 968.52, 1.04], [2106.974, 814.325, 0.994], [1945.338, 748.73, 1.14]]\nC: [[1656.177, 762.998, 0.871], [2009.557, 758.93, 0.8], [1914.45, 722.289, 1.067], [1703.798, 972.938, 1.065]]\nD: [[1816.649, 760.428, 1.116], [1730.801, 1023.39, 1.04], [2342.252, 816.69, 1.126], [2334.939, 947.14, 0.896]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_33_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_33_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_33_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_33_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_33_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_33_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_33_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_33_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1992.775, 875.132, 0.942], [1985.564, 874.76, 0.95], [1978.377, 874.483, 0.958], [1971.974, 874.315, 0.986]]\nB: [[1755.791, 1044.883, 0.825], [1877.163, 968.52, 1.04], [2106.974, 814.325, 0.994], [1945.338, 748.73, 1.14]]\nC: [[1656.177, 762.998, 0.871], [2009.557, 758.93, 0.8], [1914.45, 722.289, 1.067], [1703.798, 972.938, 1.065]]\nD: [[1816.649, 760.428, 1.116], [1730.801, 1023.39, 1.04], [2342.252, 816.69, 1.126], [2334.939, 947.14, 0.896]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[331.098, 1052.547, 0.565], [408.797, 1100.389, 0.637], [418.765, 1332.696, 0.625], [352.119, 1242.135, 0.631]]\nB: [[421.121, 1227.662, 0.557], [446.642, 1087.379, 0.513], [450.924, 1107.261, 0.47], [392.549, 1175.812, 0.691]]\nC: [[396.535, 1162.355, 0.498], [396.535, 1162.355, 0.534], [396.535, 1162.355, 0.571], [396.535, 1162.355, 0.608]]\nD: [[463.951, 972.839, 0.532], [365.417, 1075.626, 0.44], [381.022, 1300.867, 0.549], [368.078, 1350.532, 0.537]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_34_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_34_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_34_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_34_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_34_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_34_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_34_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_34_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[331.098, 1052.547, 0.565], [408.797, 1100.389, 0.637], [418.765, 1332.696, 0.625], [352.119, 1242.135, 0.631]]\nB: [[421.121, 1227.662, 0.557], [446.642, 1087.379, 0.513], [450.924, 1107.261, 0.47], [392.549, 1175.812, 0.691]]\nC: [[396.535, 1162.355, 0.498], [396.535, 1162.355, 0.534], [396.535, 1162.355, 0.571], [396.535, 1162.355, 0.608]]\nD: [[463.951, 972.839, 0.532], [365.417, 1075.626, 0.44], [381.022, 1300.867, 0.549], [368.078, 1350.532, 0.537]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[321.096, 668.091, 0.879], [321.112, 668.116, 0.885], [321.128, 668.141, 0.891], [321.143, 668.166, 0.897]]\nB: [[268.388, 688.723, 0.734], [302.215, 796.657, 0.989], [302.241, 565.326, 1.022], [265.213, 770.117, 0.814]]\nC: [[314.729, 566.271, 0.999], [287.802, 590.987, 1.045], [272.417, 724.544, 0.717], [323.87, 780.287, 0.926]]\nD: [[376.158, 594.596, 0.841], [277.747, 714.363, 0.978], [382.966, 588.719, 0.996], [345.414, 561.146, 0.948]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_35_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_35_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_35_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_35_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_35_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_35_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_35_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_35_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[321.096, 668.091, 0.879], [321.112, 668.116, 0.885], [321.128, 668.141, 0.891], [321.143, 668.166, 0.897]]\nB: [[268.388, 688.723, 0.734], [302.215, 796.657, 0.989], [302.241, 565.326, 1.022], [265.213, 770.117, 0.814]]\nC: [[314.729, 566.271, 0.999], [287.802, 590.987, 1.045], [272.417, 724.544, 0.717], [323.87, 780.287, 0.926]]\nD: [[376.158, 594.596, 0.841], [277.747, 714.363, 0.978], [382.966, 588.719, 0.996], [345.414, 561.146, 0.948]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[754.263, 1891.382, 0.199], [593.138, 1372.169, 0.319], [709.86, 1582.863, 0.554], [634.519, 1531.646, 0.56]]\nB: [[729.655, 1603.012, 0.212], [526.812, 1703.833, 0.343], [552.52, 1297.518, 0.437], [592.969, 1803.518, 0.61]]\nC: [[632.049, 1352.661, 0.204], [726.247, 1377.851, 0.377], [577.44, 1302.511, 0.523], [636.437, 1877.196, 0.48]]\nD: [[655.912, 1592.667, 0.218], [655.637, 1593.173, 0.377], [655.34, 1593.667, 0.535], [654.899, 1594.227, 0.56]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_36_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_36_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_36_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_36_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_36_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_36_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_36_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_36_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[754.263, 1891.382, 0.199], [593.138, 1372.169, 0.319], [709.86, 1582.863, 0.554], [634.519, 1531.646, 0.56]]\nB: [[729.655, 1603.012, 0.212], [526.812, 1703.833, 0.343], [552.52, 1297.518, 0.437], [592.969, 1803.518, 0.61]]\nC: [[632.049, 1352.661, 0.204], [726.247, 1377.851, 0.377], [577.44, 1302.511, 0.523], [636.437, 1877.196, 0.48]]\nD: [[655.912, 1592.667, 0.218], [655.637, 1593.173, 0.377], [655.34, 1593.667, 0.535], [654.899, 1594.227, 0.56]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1863.967, 857.871, 0.65], [1863.962, 857.872, 0.65], [1863.962, 857.872, 0.65], [1863.962, 857.872, 0.65]]\nB: [[2064.908, 1013.124, 0.75], [2122.552, 822.014, 0.59], [2177.833, 1012.188, 0.75], [1595.769, 822.35, 0.73]]\nC: [[1731.702, 852.264, 0.72], [2128.868, 793.194, 0.77], [1755.246, 973.676, 0.67], [1568.102, 944.114, 0.53]]\nD: [[1764.474, 940.448, 0.74], [2091.49, 945.26, 0.67], [2118.947, 923.168, 0.72], [1633.719, 960.882, 0.7]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_37_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_37_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_37_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_37_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_37_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_37_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_37_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_37_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1863.967, 857.871, 0.65], [1863.962, 857.872, 0.65], [1863.962, 857.872, 0.65], [1863.962, 857.872, 0.65]]\nB: [[2064.908, 1013.124, 0.75], [2122.552, 822.014, 0.59], [2177.833, 1012.188, 0.75], [1595.769, 822.35, 0.73]]\nC: [[1731.702, 852.264, 0.72], [2128.868, 793.194, 0.77], [1755.246, 973.676, 0.67], [1568.102, 944.114, 0.53]]\nD: [[1764.474, 940.448, 0.74], [2091.49, 945.26, 0.67], [2118.947, 923.168, 0.72], [1633.719, 960.882, 0.7]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1780.13, 3028.6, -0.525], [1674.509, 2149.928, -0.337], [1576.236, 2276.286, -0.134], [1537.853, 2314.916, 0.007]]\nB: [[1601.026, 2969.24, -0.541], [2094.18, 2097.632, -0.298], [2014.168, 2653.318, -0.14], [1803.211, 2667.419, 0.009]]\nC: [[1811.441, 2574.96, -0.473], [1814.647, 2570.443, -0.296], [1818.149, 2566.591, -0.119], [1820.651, 2564.035, 0.009]]\nD: [[1791.545, 2532.09, -0.471], [1731.966, 2573.436, -0.251], [1598.687, 2327.018, -0.116], [1468.52, 2562.672, 0.009]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_38_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_38_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_38_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_38_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_38_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_38_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_38_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_38_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1780.13, 3028.6, -0.525], [1674.509, 2149.928, -0.337], [1576.236, 2276.286, -0.134], [1537.853, 2314.916, 0.007]]\nB: [[1601.026, 2969.24, -0.541], [2094.18, 2097.632, -0.298], [2014.168, 2653.318, -0.14], [1803.211, 2667.419, 0.009]]\nC: [[1811.441, 2574.96, -0.473], [1814.647, 2570.443, -0.296], [1818.149, 2566.591, -0.119], [1820.651, 2564.035, 0.009]]\nD: [[1791.545, 2532.09, -0.471], [1731.966, 2573.436, -0.251], [1598.687, 2327.018, -0.116], [1468.52, 2562.672, 0.009]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[418.17, 1093.457, 0.829], [405.894, 1044.702, 0.431], [420.968, 1127.645, 0.604], [406.025, 1265.181, 0.687]]\nB: [[422.18, 1093.142, 0.749], [422.146, 1093.149, 0.523], [422.164, 1093.151, 0.575], [422.182, 1093.152, 0.627]]\nC: [[424.56, 1104.052, 0.696], [456.777, 1163.284, 0.489], [355.959, 1084.822, 0.587], [353.668, 881.288, 0.749]]\nD: [[472.5, 1170.954, 0.897], [500.203, 1162.062, 0.492], [472.1, 1132.062, 0.684], [450.284, 916.311, 0.647]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_39_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_39_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_39_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_39_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_39_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_39_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_39_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_39_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[418.17, 1093.457, 0.829], [405.894, 1044.702, 0.431], [420.968, 1127.645, 0.604], [406.025, 1265.181, 0.687]]\nB: [[422.18, 1093.142, 0.749], [422.146, 1093.149, 0.523], [422.164, 1093.151, 0.575], [422.182, 1093.152, 0.627]]\nC: [[424.56, 1104.052, 0.696], [456.777, 1163.284, 0.489], [355.959, 1084.822, 0.587], [353.668, 881.288, 0.749]]\nD: [[472.5, 1170.954, 0.897], [500.203, 1162.062, 0.492], [472.1, 1132.062, 0.684], [450.284, 916.311, 0.647]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[390.675, 1148.918, 0.446], [390.675, 1148.918, 0.486], [390.675, 1148.918, 0.526], [390.675, 1148.918, 0.566]]\nB: [[325.378, 1080.282, 0.378], [401.054, 1111.492, 0.413], [443.699, 1336.224, 0.541], [437.757, 1205.106, 0.494]]\nC: [[376.096, 1180.944, 0.535], [365.879, 1297.989, 0.536], [347.139, 1107.499, 0.489], [390.705, 1129.597, 0.653]]\nD: [[319.548, 938.981, 0.435], [320.089, 1375.531, 0.568], [447.751, 1028.646, 0.524], [462.869, 953.708, 0.657]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_40_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_40_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_40_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_40_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_40_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_40_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_40_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_40_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[390.675, 1148.918, 0.446], [390.675, 1148.918, 0.486], [390.675, 1148.918, 0.526], [390.675, 1148.918, 0.566]]\nB: [[325.378, 1080.282, 0.378], [401.054, 1111.492, 0.413], [443.699, 1336.224, 0.541], [437.757, 1205.106, 0.494]]\nC: [[376.096, 1180.944, 0.535], [365.879, 1297.989, 0.536], [347.139, 1107.499, 0.489], [390.705, 1129.597, 0.653]]\nD: [[319.548, 938.981, 0.435], [320.089, 1375.531, 0.568], [447.751, 1028.646, 0.524], [462.869, 953.708, 0.657]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1843.257, 2538.618, -0.551], [1847.727, 2533.368, -0.252], [1851.69, 2528.728, 0.148], [1855.661, 2524.133, 0.447]]\nB: [[2149.832, 2452.89, -0.472], [2084.541, 3035.493, -0.262], [2202.07, 2375.125, 0.153], [1741.345, 2112.152, 0.38]]\nC: [[1481.384, 2461.292, -0.523], [1555.975, 2186.05, -0.244], [1900.07, 2064.722, 0.165], [2087.255, 2686.41, 0.442]]\nD: [[1970.321, 2572.246, -0.542], [1648.575, 2617.927, -0.295], [1998.79, 2542.913, 0.12], [2210.323, 2215.488, 0.469]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_41_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_41_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_41_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_41_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_41_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_41_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_41_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_41_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1843.257, 2538.618, -0.551], [1847.727, 2533.368, -0.252], [1851.69, 2528.728, 0.148], [1855.661, 2524.133, 0.447]]\nB: [[2149.832, 2452.89, -0.472], [2084.541, 3035.493, -0.262], [2202.07, 2375.125, 0.153], [1741.345, 2112.152, 0.38]]\nC: [[1481.384, 2461.292, -0.523], [1555.975, 2186.05, -0.244], [1900.07, 2064.722, 0.165], [2087.255, 2686.41, 0.442]]\nD: [[1970.321, 2572.246, -0.542], [1648.575, 2617.927, -0.295], [1998.79, 2542.913, 0.12], [2210.323, 2215.488, 0.469]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[405.649, 1108.528, 0.594], [405.644, 1108.505, 0.674], [405.626, 1108.416, 0.494], [405.656, 1108.482, 0.494]]\nB: [[334.296, 1327.717, 0.679], [384.849, 1314.532, 0.74], [423.319, 950.2, 0.426], [331.031, 1040.91, 0.551]]\nC: [[347.771, 1314.846, 0.498], [446.389, 1307.841, 0.727], [399.575, 1219.724, 0.443], [426.17, 1311.828, 0.436]]\nD: [[400.335, 1102.261, 0.598], [348.445, 1284.149, 0.65], [478.752, 1133.775, 0.474], [355.374, 1236.721, 0.511]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_42_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_42_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_42_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_42_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_42_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_42_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_42_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_42_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[405.649, 1108.528, 0.594], [405.644, 1108.505, 0.674], [405.626, 1108.416, 0.494], [405.656, 1108.482, 0.494]]\nB: [[334.296, 1327.717, 0.679], [384.849, 1314.532, 0.74], [423.319, 950.2, 0.426], [331.031, 1040.91, 0.551]]\nC: [[347.771, 1314.846, 0.498], [446.389, 1307.841, 0.727], [399.575, 1219.724, 0.443], [426.17, 1311.828, 0.436]]\nD: [[400.335, 1102.261, 0.598], [348.445, 1284.149, 0.65], [478.752, 1133.775, 0.474], [355.374, 1236.721, 0.511]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1428.27, 974.381, 1.985], [1433.79, 878.793, 1.553], [1487.91, 991.551, 2.084], [1094.85, 1099.062, 1.834]]\nB: [[1126.35, 864.162, 1.48], [1369.77, 1079.18, 2.095], [1104.67, 888.249, 1.995], [1467.5, 1079.513, 1.593]]\nC: [[1319.41, 1031.387, 1.821], [1319.41, 1031.387, 1.821], [1319.41, 1031.387, 1.821], [1319.41, 1031.387, 1.821]]\nD: [[1325.9, 922.588, 2.092], [1241.85, 1191.619, 1.687], [1156.96, 1063.21, 1.942], [1396.09, 908.012, 1.846]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_43_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_43_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_43_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_43_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_43_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_43_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_43_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_43_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1428.27, 974.381, 1.985], [1433.79, 878.793, 1.553], [1487.91, 991.551, 2.084], [1094.85, 1099.062, 1.834]]\nB: [[1126.35, 864.162, 1.48], [1369.77, 1079.18, 2.095], [1104.67, 888.249, 1.995], [1467.5, 1079.513, 1.593]]\nC: [[1319.41, 1031.387, 1.821], [1319.41, 1031.387, 1.821], [1319.41, 1031.387, 1.821], [1319.41, 1031.387, 1.821]]\nD: [[1325.9, 922.588, 2.092], [1241.85, 1191.619, 1.687], [1156.96, 1063.21, 1.942], [1396.09, 908.012, 1.846]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[409.465, 1216.476, 0.476], [318.714, 1226.609, 0.544], [365.342, 995.006, 0.666], [409.876, 1110.956, 0.592]]\nB: [[394.755, 1113.151, 0.528], [394.774, 1113.143, 0.578], [394.793, 1113.134, 0.628], [394.793, 1113.134, 0.703]]\nC: [[420.334, 1155.862, 0.481], [398.422, 922.217, 0.485], [347.385, 1076.56, 0.624], [333.837, 1269.244, 0.608]]\nD: [[335.019, 1099.773, 0.481], [389.242, 976.46, 0.466], [401.879, 992.855, 0.713], [331.612, 1204.414, 0.622]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_44_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_44_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_44_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_44_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_44_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_44_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_44_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_44_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[409.465, 1216.476, 0.476], [318.714, 1226.609, 0.544], [365.342, 995.006, 0.666], [409.876, 1110.956, 0.592]]\nB: [[394.755, 1113.151, 0.528], [394.774, 1113.143, 0.578], [394.793, 1113.134, 0.628], [394.793, 1113.134, 0.703]]\nC: [[420.334, 1155.862, 0.481], [398.422, 922.217, 0.485], [347.385, 1076.56, 0.624], [333.837, 1269.244, 0.608]]\nD: [[335.019, 1099.773, 0.481], [389.242, 976.46, 0.466], [401.879, 992.855, 0.713], [331.612, 1204.414, 0.622]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[361.726, 683.196, 1.397], [329.217, 690.805, 1.946], [283.896, 621.516, 1.958], [356.569, 620.728, 2.064]]\nB: [[418.211, 729.764, 1.616], [294.113, 717.923, 1.419], [354.161, 578.812, 1.657], [290.406, 708.411, 2.08]]\nC: [[294.448, 559.154, 1.483], [317.072, 572.818, 2.094], [333.21, 533.806, 2.046], [288.729, 702.966, 1.84]]\nD: [[349.242, 634.568, 1.725], [349.228, 634.584, 1.748], [349.213, 634.601, 1.771], [349.198, 634.618, 1.794]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_45_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_45_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_45_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_45_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_45_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_45_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_45_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_45_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[361.726, 683.196, 1.397], [329.217, 690.805, 1.946], [283.896, 621.516, 1.958], [356.569, 620.728, 2.064]]\nB: [[418.211, 729.764, 1.616], [294.113, 717.923, 1.419], [354.161, 578.812, 1.657], [290.406, 708.411, 2.08]]\nC: [[294.448, 559.154, 1.483], [317.072, 572.818, 2.094], [333.21, 533.806, 2.046], [288.729, 702.966, 1.84]]\nD: [[349.242, 634.568, 1.725], [349.228, 634.584, 1.748], [349.213, 634.601, 1.771], [349.198, 634.618, 1.794]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1342.828, 1030.123, -0.019], [1342.828, 1030.123, -0.019], [1342.828, 1030.123, -0.019], [1342.828, 1030.123, -0.019]]\nB: [[1097.533, 870.18, -0.018], [1280.182, 1133.641, -0.019], [1489.479, 998.305, -0.016], [1148.803, 1033.836, -0.018]]\nC: [[1127.233, 1005.075, -0.016], [1511.451, 847.909, -0.022], [1150.864, 1055.903, -0.02], [1443.444, 1006.94, -0.017]]\nD: [[1513.245, 1007.752, -0.022], [1331.585, 1065.932, -0.016], [1532.891, 854.441, -0.017], [1526.175, 951.171, -0.022]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_46_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_46_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_46_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_46_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_46_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_46_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_46_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_46_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1342.828, 1030.123, -0.019], [1342.828, 1030.123, -0.019], [1342.828, 1030.123, -0.019], [1342.828, 1030.123, -0.019]]\nB: [[1097.533, 870.18, -0.018], [1280.182, 1133.641, -0.019], [1489.479, 998.305, -0.016], [1148.803, 1033.836, -0.018]]\nC: [[1127.233, 1005.075, -0.016], [1511.451, 847.909, -0.022], [1150.864, 1055.903, -0.02], [1443.444, 1006.94, -0.017]]\nD: [[1513.245, 1007.752, -0.022], [1331.585, 1065.932, -0.016], [1532.891, 854.441, -0.017], [1526.175, 951.171, -0.022]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[368.243, 1147.276, 0.495], [320.188, 1220.441, 0.564], [378.673, 985.999, 0.66], [415.817, 1250.586, 0.729]]\nB: [[471.077, 1316.726, 0.65], [388.888, 998.99, 0.705], [397.125, 1213.868, 0.57], [326.301, 938.598, 0.746]]\nC: [[394.039, 1143.246, 0.615], [391.841, 1138.065, 0.615], [389.353, 1132.372, 0.64], [387.343, 1127.335, 0.765]]\nD: [[380.686, 1104.044, 0.666], [420.094, 1131.831, 0.503], [335.098, 1016.255, 0.76], [342.797, 1164.927, 0.672]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_47_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_47_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_47_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_47_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_47_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_47_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_47_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_47_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[368.243, 1147.276, 0.495], [320.188, 1220.441, 0.564], [378.673, 985.999, 0.66], [415.817, 1250.586, 0.729]]\nB: [[471.077, 1316.726, 0.65], [388.888, 998.99, 0.705], [397.125, 1213.868, 0.57], [326.301, 938.598, 0.746]]\nC: [[394.039, 1143.246, 0.615], [391.841, 1138.065, 0.615], [389.353, 1132.372, 0.64], [387.343, 1127.335, 0.765]]\nD: [[380.686, 1104.044, 0.666], [420.094, 1131.831, 0.503], [335.098, 1016.255, 0.76], [342.797, 1164.927, 0.672]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[529.721, 1110.52, 1.789], [418.305, 959.31, 1.737], [400.029, 1228.06, 2.221], [451.205, 1117.422, 2.204]]\nB: [[411.023, 1090.6, 1.799], [524.659, 1002.9, 1.558], [459.22, 1085.139, 1.901], [427.15, 911.884, 1.822]]\nC: [[503.852, 1131.29, 2.197], [402.563, 1323.31, 1.648], [532.361, 1202.739, 1.985], [480.913, 1034.846, 2.094]]\nD: [[456.587, 1114.23, 2.052], [448.914, 1116.73, 1.885], [448.331, 1116.811, 1.887], [446.322, 1116.881, 2.007]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_48_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_48_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_48_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_48_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_48_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_48_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_48_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_48_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[529.721, 1110.52, 1.789], [418.305, 959.31, 1.737], [400.029, 1228.06, 2.221], [451.205, 1117.422, 2.204]]\nB: [[411.023, 1090.6, 1.799], [524.659, 1002.9, 1.558], [459.22, 1085.139, 1.901], [427.15, 911.884, 1.822]]\nC: [[503.852, 1131.29, 2.197], [402.563, 1323.31, 1.648], [532.361, 1202.739, 1.985], [480.913, 1034.846, 2.094]]\nD: [[456.587, 1114.23, 2.052], [448.914, 1116.73, 1.885], [448.331, 1116.811, 1.887], [446.322, 1116.881, 2.007]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[410.862, 1106.326, 0.665], [410.862, 1106.326, 0.59], [410.862, 1106.326, 0.553], [410.862, 1106.326, 0.415]]\nB: [[404.294, 1051.995, 0.561], [398.816, 1084.635, 0.64], [356.338, 1249.05, 0.503], [446.466, 1282.71, 0.342]]\nC: [[336.012, 1230.001, 0.749], [456.309, 1162.403, 0.66], [488.514, 919.924, 0.477], [360.602, 1191.978, 0.369]]\nD: [[407.759, 1016.766, 0.63], [366.992, 935.62, 0.5], [484.755, 1037.534, 0.603], [490.174, 1145.425, 0.38]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_49_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_49_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_49_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_49_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_49_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_49_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_49_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_49_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[410.862, 1106.326, 0.665], [410.862, 1106.326, 0.59], [410.862, 1106.326, 0.553], [410.862, 1106.326, 0.415]]\nB: [[404.294, 1051.995, 0.561], [398.816, 1084.635, 0.64], [356.338, 1249.05, 0.503], [446.466, 1282.71, 0.342]]\nC: [[336.012, 1230.001, 0.749], [456.309, 1162.403, 0.66], [488.514, 919.924, 0.477], [360.602, 1191.978, 0.369]]\nD: [[407.759, 1016.766, 0.63], [366.992, 935.62, 0.5], [484.755, 1037.534, 0.603], [490.174, 1145.425, 0.38]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[674.817, 1495.218, -0.033], [637.599, 1735.138, 0.04], [753.378, 1282.425, 0.266], [579.881, 1534.752, 0.639]]\nB: [[709.455, 1518.871, -0.042], [743.219, 1536.668, 0.05], [546.398, 1312.775, 0.212], [543.916, 1348.998, 0.477]]\nC: [[646.543, 1481.061, -0.039], [690.227, 1278.475, 0.06], [658.101, 1765.87, 0.225], [673.992, 1863.036, 0.546]]\nD: [[654.306, 1593.839, -0.039], [654.897, 1593.314, 0.05], [655.544, 1592.867, 0.238], [656.181, 1592.404, 0.554]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_50_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_50_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_50_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_50_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_50_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_50_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_50_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_50_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[674.817, 1495.218, -0.033], [637.599, 1735.138, 0.04], [753.378, 1282.425, 0.266], [579.881, 1534.752, 0.639]]\nB: [[709.455, 1518.871, -0.042], [743.219, 1536.668, 0.05], [546.398, 1312.775, 0.212], [543.916, 1348.998, 0.477]]\nC: [[646.543, 1481.061, -0.039], [690.227, 1278.475, 0.06], [658.101, 1765.87, 0.225], [673.992, 1863.036, 0.546]]\nD: [[654.306, 1593.839, -0.039], [654.897, 1593.314, 0.05], [655.544, 1592.867, 0.238], [656.181, 1592.404, 0.554]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[318.904, 699.89, -0.305], [356.863, 749.45, -0.295], [342.145, 817.48, -0.165], [362.888, 813.169, -0.152]]\nB: [[311.846, 696.05, -0.326], [311.404, 695.55, -0.298], [309.653, 693.549, -0.188], [309.251, 693.028, -0.161]]\nC: [[270.331, 808.82, -0.365], [259.897, 752.35, -0.352], [311.667, 620.881, -0.218], [273.84, 705.279, -0.182]]\nD: [[285.234, 746.79, -0.327], [276.961, 728.57, -0.341], [274.698, 714.537, -0.21], [341.495, 714.928, -0.131]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_51_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_51_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_51_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_51_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_51_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_51_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_51_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_51_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[318.904, 699.89, -0.305], [356.863, 749.45, -0.295], [342.145, 817.48, -0.165], [362.888, 813.169, -0.152]]\nB: [[311.846, 696.05, -0.326], [311.404, 695.55, -0.298], [309.653, 693.549, -0.188], [309.251, 693.028, -0.161]]\nC: [[270.331, 808.82, -0.365], [259.897, 752.35, -0.352], [311.667, 620.881, -0.218], [273.84, 705.279, -0.182]]\nD: [[285.234, 746.79, -0.327], [276.961, 728.57, -0.341], [274.698, 714.537, -0.21], [341.495, 714.928, -0.131]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[432.564, 1385.721, 0.86], [382.527, 1126.126, 0.819], [443.57, 1262.347, 0.849], [446.802, 1286.824, 0.805]]\nB: [[370.754, 1092.547, 0.872], [331.67, 1167.043, 0.761], [399.201, 1018.42, 0.801], [365.027, 1292.343, 0.758]]\nC: [[408.524, 1190.723, 0.733], [408.524, 1190.723, 0.773], [408.524, 1190.723, 0.814], [408.524, 1190.723, 0.854]]\nD: [[402.914, 1215.467, 0.85], [450.76, 1135.126, 0.766], [461.237, 971.14, 0.851], [437.03, 1104.878, 0.788]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_52_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_52_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_52_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_52_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_52_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_52_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_52_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_52_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[432.564, 1385.721, 0.86], [382.527, 1126.126, 0.819], [443.57, 1262.347, 0.849], [446.802, 1286.824, 0.805]]\nB: [[370.754, 1092.547, 0.872], [331.67, 1167.043, 0.761], [399.201, 1018.42, 0.801], [365.027, 1292.343, 0.758]]\nC: [[408.524, 1190.723, 0.733], [408.524, 1190.723, 0.773], [408.524, 1190.723, 0.814], [408.524, 1190.723, 0.854]]\nD: [[402.914, 1215.467, 0.85], [450.76, 1135.126, 0.766], [461.237, 971.14, 0.851], [437.03, 1104.878, 0.788]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[509.54, 973.159, 0.18], [380.671, 1268.603, 0.2], [479.07, 1050.521, 0.236], [494.912, 1129.693, 0.227]]\nB: [[439.95, 1094.017, 0.17], [439.878, 1094.005, 0.2], [439.87, 1094.004, 0.204], [439.863, 1094.003, 0.207]]\nC: [[469.34, 1051.411, 0.16], [354.833, 915.321, 0.2], [409.43, 978.881, 0.18], [455.437, 1174.679, 0.197]]\nD: [[450.6, 1030.444, 0.18], [376.497, 1114.358, 0.2], [397.1, 1100.748, 0.221], [400.171, 883.327, 0.198]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_53_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_53_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_53_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_53_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_53_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_53_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_53_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_53_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[509.54, 973.159, 0.18], [380.671, 1268.603, 0.2], [479.07, 1050.521, 0.236], [494.912, 1129.693, 0.227]]\nB: [[439.95, 1094.017, 0.17], [439.878, 1094.005, 0.2], [439.87, 1094.004, 0.204], [439.863, 1094.003, 0.207]]\nC: [[469.34, 1051.411, 0.16], [354.833, 915.321, 0.2], [409.43, 978.881, 0.18], [455.437, 1174.679, 0.197]]\nD: [[450.6, 1030.444, 0.18], [376.497, 1114.358, 0.2], [397.1, 1100.748, 0.221], [400.171, 883.327, 0.198]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[745.24, 1895.181, 1.097], [763.549, 1536.044, 0.845], [570.809, 1601.968, 0.989], [652.246, 1423.25, 1.127]]\nB: [[532.12, 1872.39, 0.899], [664.607, 1965.812, 0.921], [689.926, 1479.159, 0.981], [613.897, 1574.3, 1.137]]\nC: [[548.35, 1392.059, 1.158], [726.228, 1698.055, 1.001], [546.517, 1691.289, 0.982], [644.748, 1773.44, 1.004]]\nD: [[638.38, 1644.304, 0.969], [638.651, 1644.538, 0.969], [639.028, 1644.741, 0.969], [639.302, 1644.98, 0.969]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_54_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_54_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_54_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_54_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_54_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_54_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_54_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_54_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[745.24, 1895.181, 1.097], [763.549, 1536.044, 0.845], [570.809, 1601.968, 0.989], [652.246, 1423.25, 1.127]]\nB: [[532.12, 1872.39, 0.899], [664.607, 1965.812, 0.921], [689.926, 1479.159, 0.981], [613.897, 1574.3, 1.137]]\nC: [[548.35, 1392.059, 1.158], [726.228, 1698.055, 1.001], [546.517, 1691.289, 0.982], [644.748, 1773.44, 1.004]]\nD: [[638.38, 1644.304, 0.969], [638.651, 1644.538, 0.969], [639.028, 1644.741, 0.969], [639.302, 1644.98, 0.969]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[498.649, 946.58, 0.483], [429.418, 1259.918, 0.62], [364.692, 1168.776, 0.667], [459.431, 959.241, 0.827]]\nB: [[455.692, 1030.221, 0.542], [455.48, 1267.707, 0.483], [500.554, 1295.934, 0.772], [397.8, 1198.962, 0.903]]\nC: [[424.598, 1092.173, 0.591], [424.547, 1092.198, 0.561], [424.495, 1092.222, 0.732], [424.504, 1092.223, 0.809]]\nD: [[442.794, 1098.547, 0.607], [348.384, 1268.237, 0.46], [435.072, 1179.416, 0.685], [349.227, 1283.876, 0.743]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_55_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_55_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_55_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_55_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_55_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_55_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_55_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_55_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[498.649, 946.58, 0.483], [429.418, 1259.918, 0.62], [364.692, 1168.776, 0.667], [459.431, 959.241, 0.827]]\nB: [[455.692, 1030.221, 0.542], [455.48, 1267.707, 0.483], [500.554, 1295.934, 0.772], [397.8, 1198.962, 0.903]]\nC: [[424.598, 1092.173, 0.591], [424.547, 1092.198, 0.561], [424.495, 1092.222, 0.732], [424.504, 1092.223, 0.809]]\nD: [[442.794, 1098.547, 0.607], [348.384, 1268.237, 0.46], [435.072, 1179.416, 0.685], [349.227, 1283.876, 0.743]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1592.863, 1046.051, 0.228], [1890.036, 733.66, 0.198], [2193.82, 880.022, 0.24], [2269.051, 988.07, 0.246]]\nB: [[1799.697, 977.341, 0.188], [2255.207, 938.924, 0.229], [2189.995, 1042.344, 0.204], [1954.403, 990.03, 0.25]]\nC: [[1920.044, 873.356, 0.213], [1920.067, 873.333, 0.213], [1920.067, 873.333, 0.213], [1920.021, 873.38, 0.263]]\nD: [[2188.053, 984.479, 0.185], [1789.35, 823.078, 0.223], [2262.284, 775.407, 0.196], [2232.543, 929.3, 0.291]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_56_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_56_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_56_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_56_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_56_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_56_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_56_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_56_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1592.863, 1046.051, 0.228], [1890.036, 733.66, 0.198], [2193.82, 880.022, 0.24], [2269.051, 988.07, 0.246]]\nB: [[1799.697, 977.341, 0.188], [2255.207, 938.924, 0.229], [2189.995, 1042.344, 0.204], [1954.403, 990.03, 0.25]]\nC: [[1920.044, 873.356, 0.213], [1920.067, 873.333, 0.213], [1920.067, 873.333, 0.213], [1920.021, 873.38, 0.263]]\nD: [[2188.053, 984.479, 0.185], [1789.35, 823.078, 0.223], [2262.284, 775.407, 0.196], [2232.543, 929.3, 0.291]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1812.249, 2571.668, -0.038], [1816.741, 2566.203, 0.124], [1820.586, 2560.847, 0.325], [1825.127, 2555.039, 0.499]]\nB: [[1810.749, 2477.016, -0.044], [1526.117, 2495.829, 0.129], [2120.606, 2682.221, 0.369], [1940.674, 2177.131, 0.513]]\nC: [[1882.741, 2318.424, -0.045], [1487.68, 2321.211, 0.127], [2151.691, 2137.892, 0.264], [1751.426, 2963.026, 0.451]]\nD: [[1614.268, 2747.937, -0.04], [1694.976, 3075.224, 0.115], [1495.647, 3054.549, 0.349], [2186.702, 2819.745, 0.446]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_57_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_57_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_57_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_57_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_57_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_57_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_57_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_57_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1812.249, 2571.668, -0.038], [1816.741, 2566.203, 0.124], [1820.586, 2560.847, 0.325], [1825.127, 2555.039, 0.499]]\nB: [[1810.749, 2477.016, -0.044], [1526.117, 2495.829, 0.129], [2120.606, 2682.221, 0.369], [1940.674, 2177.131, 0.513]]\nC: [[1882.741, 2318.424, -0.045], [1487.68, 2321.211, 0.127], [2151.691, 2137.892, 0.264], [1751.426, 2963.026, 0.451]]\nD: [[1614.268, 2747.937, -0.04], [1694.976, 3075.224, 0.115], [1495.647, 3054.549, 0.349], [2186.702, 2819.745, 0.446]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1774.223, 2610.364, 0.996], [1774.053, 2610.352, 1.02], [1773.502, 2611.229, 1.045], [1773.029, 2612.151, 1.07]]\nB: [[1776.641, 2350.607, 1.067], [1644.964, 2423.862, 0.9], [1725.868, 3040.601, 1.103], [2124.437, 2193.747, 0.89]]\nC: [[1671.897, 2705.242, 1.029], [1872.101, 2819.316, 0.97], [1650.995, 2602.6, 1.046], [1436.928, 2842.091, 1.11]]\nD: [[1953.59, 2775.546, 0.952], [1810.746, 2706.189, 0.97], [1565.979, 2177.88, 1.084], [1805.0, 2120.155, 0.92]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_58_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_58_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_58_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_58_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_58_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_58_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_58_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_58_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1774.223, 2610.364, 0.996], [1774.053, 2610.352, 1.02], [1773.502, 2611.229, 1.045], [1773.029, 2612.151, 1.07]]\nB: [[1776.641, 2350.607, 1.067], [1644.964, 2423.862, 0.9], [1725.868, 3040.601, 1.103], [2124.437, 2193.747, 0.89]]\nC: [[1671.897, 2705.242, 1.029], [1872.101, 2819.316, 0.97], [1650.995, 2602.6, 1.046], [1436.928, 2842.091, 1.11]]\nD: [[1953.59, 2775.546, 0.952], [1810.746, 2706.189, 0.97], [1565.979, 2177.88, 1.084], [1805.0, 2120.155, 0.92]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1736.392, 873.361, 1.079], [1734.892, 874.826, 1.069], [1733.79, 876.382, 1.059], [1732.878, 880.029, 1.041]]\nB: [[1927.606, 821.613, 1.179], [1635.106, 838.612, 1.118], [1798.04, 779.408, 1.262], [1906.044, 824.489, 1.153]]\nC: [[1655.684, 967.021, 1.001], [2054.919, 792.455, 1.056], [1489.06, 752.973, 1.09], [1601.794, 1000.219, 1.236]]\nD: [[2074.836, 711.213, 0.881], [1659.637, 744.201, 1.258], [2073.69, 988.772, 1.118], [1777.227, 826.859, 1.096]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_59_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_59_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_59_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_59_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_59_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_59_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_59_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_59_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1736.392, 873.361, 1.079], [1734.892, 874.826, 1.069], [1733.79, 876.382, 1.059], [1732.878, 880.029, 1.041]]\nB: [[1927.606, 821.613, 1.179], [1635.106, 838.612, 1.118], [1798.04, 779.408, 1.262], [1906.044, 824.489, 1.153]]\nC: [[1655.684, 967.021, 1.001], [2054.919, 792.455, 1.056], [1489.06, 752.973, 1.09], [1601.794, 1000.219, 1.236]]\nD: [[2074.836, 711.213, 0.881], [1659.637, 744.201, 1.258], [2073.69, 988.772, 1.118], [1777.227, 826.859, 1.096]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[283.606, 696.753, 1.329], [383.644, 532.364, 1.363], [332.149, 525.401, 1.473], [385.71, 734.583, 1.097]]\nB: [[383.086, 639.489, 1.063], [279.173, 585.298, 1.388], [361.433, 701.65, 1.375], [295.925, 719.625, 1.188]]\nC: [[330.789, 641.074, 1.158], [330.789, 641.074, 1.212], [330.789, 641.074, 1.267], [330.789, 641.074, 1.322]]\nD: [[306.13, 719.717, 1.368], [345.491, 726.192, 0.98], [345.908, 519.046, 1.407], [318.14, 654.942, 1.306]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_60_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_60_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_60_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_60_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_60_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_60_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_60_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_60_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[283.606, 696.753, 1.329], [383.644, 532.364, 1.363], [332.149, 525.401, 1.473], [385.71, 734.583, 1.097]]\nB: [[383.086, 639.489, 1.063], [279.173, 585.298, 1.388], [361.433, 701.65, 1.375], [295.925, 719.625, 1.188]]\nC: [[330.789, 641.074, 1.158], [330.789, 641.074, 1.212], [330.789, 641.074, 1.267], [330.789, 641.074, 1.322]]\nD: [[306.13, 719.717, 1.368], [345.491, 726.192, 0.98], [345.908, 519.046, 1.407], [318.14, 654.942, 1.306]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[661.561, 1356.246, 0.07], [523.515, 1451.812, 0.242], [629.357, 1441.913, 0.46], [693.951, 1658.121, 0.495]]\nB: [[596.26, 1440.992, 0.068], [660.286, 1441.26, 0.267], [710.568, 1651.675, 0.41], [567.432, 1395.938, 0.5]]\nC: [[628.289, 1618.572, 0.075], [628.026, 1618.937, 0.252], [627.783, 1619.317, 0.43], [627.525, 1619.686, 0.607]]\nD: [[603.153, 1669.791, 0.074], [606.05, 1635.908, 0.212], [518.765, 1574.758, 0.36], [706.901, 1431.785, 0.612]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_61_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_61_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_61_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_61_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_61_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_61_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_61_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_61_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[661.561, 1356.246, 0.07], [523.515, 1451.812, 0.242], [629.357, 1441.913, 0.46], [693.951, 1658.121, 0.495]]\nB: [[596.26, 1440.992, 0.068], [660.286, 1441.26, 0.267], [710.568, 1651.675, 0.41], [567.432, 1395.938, 0.5]]\nC: [[628.289, 1618.572, 0.075], [628.026, 1618.937, 0.252], [627.783, 1619.317, 0.43], [627.525, 1619.686, 0.607]]\nD: [[603.153, 1669.791, 0.074], [606.05, 1635.908, 0.212], [518.765, 1574.758, 0.36], [706.901, 1431.785, 0.612]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[330.056, 1212.866, 0.652], [437.866, 986.828, 0.616], [373.47, 964.81, 0.657], [399.422, 1352.779, 0.635]]\nB: [[376.04, 1070.497, 0.645], [362.804, 1103.987, 0.583], [385.25, 1290.416, 0.524], [372.683, 1131.605, 0.686]]\nC: [[399.012, 1167.878, 0.547], [399.016, 1167.877, 0.567], [399.02, 1167.875, 0.588], [399.024, 1167.873, 0.609]]\nD: [[341.259, 1087.552, 0.588], [364.207, 1061.407, 0.495], [465.16, 1225.627, 0.505], [436.212, 1297.403, 0.597]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_62_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_62_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_62_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_62_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_62_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_62_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_62_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_62_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[330.056, 1212.866, 0.652], [437.866, 986.828, 0.616], [373.47, 964.81, 0.657], [399.422, 1352.779, 0.635]]\nB: [[376.04, 1070.497, 0.645], [362.804, 1103.987, 0.583], [385.25, 1290.416, 0.524], [372.683, 1131.605, 0.686]]\nC: [[399.012, 1167.878, 0.547], [399.016, 1167.877, 0.567], [399.02, 1167.875, 0.588], [399.024, 1167.873, 0.609]]\nD: [[341.259, 1087.552, 0.588], [364.207, 1061.407, 0.495], [465.16, 1225.627, 0.505], [436.212, 1297.403, 0.597]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1274.538, 1123.672, 0.2], [1346.842, 824.302, 0.2], [1292.774, 973.997, 0.26], [1054.281, 825.781, 0.247]]\nB: [[1328.749, 1065.512, 0.2], [1175.959, 833.053, 0.21], [1184.952, 824.911, 0.22], [1071.949, 1039.546, 0.188]]\nC: [[1098.549, 1020.422, 0.2], [1497.254, 1019.016, 0.22], [1382.864, 830.543, 0.21], [1100.12, 1050.184, 0.22]]\nD: [[1253.322, 1015.243, 0.2], [1253.424, 1015.978, 0.21], [1253.526, 1016.713, 0.22], [1253.637, 1017.522, 0.231]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_63_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_63_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_63_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_63_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_63_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_63_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_63_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_63_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1274.538, 1123.672, 0.2], [1346.842, 824.302, 0.2], [1292.774, 973.997, 0.26], [1054.281, 825.781, 0.247]]\nB: [[1328.749, 1065.512, 0.2], [1175.959, 833.053, 0.21], [1184.952, 824.911, 0.22], [1071.949, 1039.546, 0.188]]\nC: [[1098.549, 1020.422, 0.2], [1497.254, 1019.016, 0.22], [1382.864, 830.543, 0.21], [1100.12, 1050.184, 0.22]]\nD: [[1253.322, 1015.243, 0.2], [1253.424, 1015.978, 0.21], [1253.526, 1016.713, 0.22], [1253.637, 1017.522, 0.231]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1875.125, 875.415, 0.935], [1875.191, 875.416, 0.935], [1875.193, 875.318, 0.985], [1875.195, 875.264, 0.952]]\nB: [[2028.088, 883.086, 0.815], [1648.373, 903.844, 1.102], [1520.401, 917.022, 1.065], [1867.355, 949.61, 0.806]]\nC: [[1847.629, 795.419, 1.055], [2059.556, 721.579, 0.94], [1862.226, 915.16, 1.105], [1795.239, 771.992, 0.777]]\nD: [[1908.891, 972.995, 0.987], [1782.46, 894.481, 0.828], [1850.807, 745.233, 1.094], [1559.966, 967.549, 0.89]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_64_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_64_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_64_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_64_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_64_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_64_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_64_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_64_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1875.125, 875.415, 0.935], [1875.191, 875.416, 0.935], [1875.193, 875.318, 0.985], [1875.195, 875.264, 0.952]]\nB: [[2028.088, 883.086, 0.815], [1648.373, 903.844, 1.102], [1520.401, 917.022, 1.065], [1867.355, 949.61, 0.806]]\nC: [[1847.629, 795.419, 1.055], [2059.556, 721.579, 0.94], [1862.226, 915.16, 1.105], [1795.239, 771.992, 0.777]]\nD: [[1908.891, 972.995, 0.987], [1782.46, 894.481, 0.828], [1850.807, 745.233, 1.094], [1559.966, 967.549, 0.89]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[582.71, 1411.821, -0.354], [557.0, 1488.19, -0.247], [651.447, 1741.537, -0.237], [520.268, 1329.718, -0.188]]\nB: [[635.447, 1620.546, -0.326], [637.445, 1618.566, -0.238], [639.933, 1616.457, -0.267], [642.736, 1614.065, -0.196]]\nC: [[522.996, 1413.245, -0.379], [659.983, 1928.523, -0.204], [766.979, 1315.798, -0.304], [599.616, 1825.248, -0.224]]\nD: [[534.996, 1707.261, -0.292], [614.808, 1704.145, -0.211], [523.563, 1883.81, -0.302], [672.146, 1371.116, -0.218]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_65_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_65_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_65_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_65_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_65_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_65_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_65_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_65_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[582.71, 1411.821, -0.354], [557.0, 1488.19, -0.247], [651.447, 1741.537, -0.237], [520.268, 1329.718, -0.188]]\nB: [[635.447, 1620.546, -0.326], [637.445, 1618.566, -0.238], [639.933, 1616.457, -0.267], [642.736, 1614.065, -0.196]]\nC: [[522.996, 1413.245, -0.379], [659.983, 1928.523, -0.204], [766.979, 1315.798, -0.304], [599.616, 1825.248, -0.224]]\nD: [[534.996, 1707.261, -0.292], [614.808, 1704.145, -0.211], [523.563, 1883.81, -0.302], [672.146, 1371.116, -0.218]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1680.561, 2863.302, 2.542], [2169.563, 2794.924, 2.265], [1707.842, 2939.301, 2.152], [1632.945, 2507.032, 1.945]]\nB: [[1547.261, 2391.341, 2.475], [1833.795, 2236.842, 2.065], [1997.767, 2445.687, 1.715], [2056.371, 2356.627, 2.0]]\nC: [[1904.106, 2453.654, 2.215], [1897.838, 2460.219, 2.156], [1892.616, 2465.688, 2.107], [1887.387, 2471.164, 2.057]]\nD: [[2182.661, 2279.405, 1.852], [1930.577, 2763.231, 2.146], [1909.158, 2677.265, 2.297], [1596.829, 2331.093, 2.251]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_66_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_66_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_66_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_66_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_66_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_66_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_66_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_66_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1680.561, 2863.302, 2.542], [2169.563, 2794.924, 2.265], [1707.842, 2939.301, 2.152], [1632.945, 2507.032, 1.945]]\nB: [[1547.261, 2391.341, 2.475], [1833.795, 2236.842, 2.065], [1997.767, 2445.687, 1.715], [2056.371, 2356.627, 2.0]]\nC: [[1904.106, 2453.654, 2.215], [1897.838, 2460.219, 2.156], [1892.616, 2465.688, 2.107], [1887.387, 2471.164, 2.057]]\nD: [[2182.661, 2279.405, 1.852], [1930.577, 2763.231, 2.146], [1909.158, 2677.265, 2.297], [1596.829, 2331.093, 2.251]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[325.896, 989.839, 0.854], [326.517, 1140.353, 0.792], [331.478, 1339.384, 0.825], [382.05, 1348.154, 0.905]]\nB: [[393.357, 1149.173, 0.741], [392.945, 1148.426, 0.766], [392.836, 1148.208, 0.791], [392.641, 1147.242, 0.816]]\nC: [[349.533, 1155.715, 0.667], [378.661, 1084.815, 0.825], [431.355, 1125.036, 0.69], [366.861, 940.522, 0.728]]\nD: [[370.793, 1354.659, 0.611], [315.047, 1147.297, 0.791], [387.351, 947.719, 0.922], [465.223, 1022.515, 0.919]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_67_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_67_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_67_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_67_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_67_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_67_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_67_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_67_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[325.896, 989.839, 0.854], [326.517, 1140.353, 0.792], [331.478, 1339.384, 0.825], [382.05, 1348.154, 0.905]]\nB: [[393.357, 1149.173, 0.741], [392.945, 1148.426, 0.766], [392.836, 1148.208, 0.791], [392.641, 1147.242, 0.816]]\nC: [[349.533, 1155.715, 0.667], [378.661, 1084.815, 0.825], [431.355, 1125.036, 0.69], [366.861, 940.522, 0.728]]\nD: [[370.793, 1354.659, 0.611], [315.047, 1147.297, 0.791], [387.351, 947.719, 0.922], [465.223, 1022.515, 0.919]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2150.649, 802.858, 0.294], [2285.669, 868.672, 0.285], [1699.227, 714.027, 0.212], [1879.379, 823.249, 0.262]]\nB: [[2057.083, 982.769, 0.286], [2194.778, 989.034, 0.224], [1802.969, 943.191, 0.277], [2004.998, 886.268, 0.304]]\nC: [[1585.591, 914.605, 0.27], [1552.179, 1019.735, 0.316], [1997.522, 917.351, 0.271], [2167.86, 906.565, 0.376]]\nD: [[1926.398, 878.499, 0.267], [1926.397, 878.517, 0.277], [1926.355, 878.551, 0.259], [1926.373, 878.505, 0.317]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_68_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_68_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_68_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_68_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_68_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_68_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_68_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_68_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2150.649, 802.858, 0.294], [2285.669, 868.672, 0.285], [1699.227, 714.027, 0.212], [1879.379, 823.249, 0.262]]\nB: [[2057.083, 982.769, 0.286], [2194.778, 989.034, 0.224], [1802.969, 943.191, 0.277], [2004.998, 886.268, 0.304]]\nC: [[1585.591, 914.605, 0.27], [1552.179, 1019.735, 0.316], [1997.522, 917.351, 0.271], [2167.86, 906.565, 0.376]]\nD: [[1926.398, 878.499, 0.267], [1926.397, 878.517, 0.277], [1926.355, 878.551, 0.259], [1926.373, 878.505, 0.317]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[510.539, 1910.445, 0.57], [644.708, 1390.766, 0.487], [528.12, 1465.53, 0.743], [684.489, 1755.997, 0.708]]\nB: [[618.712, 1315.402, 0.58], [707.938, 1490.825, 0.655], [678.962, 1715.92, 0.704], [561.864, 1423.394, 0.869]]\nC: [[644.567, 1930.873, 0.501], [662.364, 1327.012, 0.538], [649.501, 1573.33, 0.633], [563.587, 1732.779, 0.975]]\nD: [[612.719, 1632.142, 0.491], [612.166, 1632.636, 0.566], [611.613, 1633.13, 0.641], [611.127, 1633.567, 0.816]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_69_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_69_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_69_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_69_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_69_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_69_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_69_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_69_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[510.539, 1910.445, 0.57], [644.708, 1390.766, 0.487], [528.12, 1465.53, 0.743], [684.489, 1755.997, 0.708]]\nB: [[618.712, 1315.402, 0.58], [707.938, 1490.825, 0.655], [678.962, 1715.92, 0.704], [561.864, 1423.394, 0.869]]\nC: [[644.567, 1930.873, 0.501], [662.364, 1327.012, 0.538], [649.501, 1573.33, 0.633], [563.587, 1732.779, 0.975]]\nD: [[612.719, 1632.142, 0.491], [612.166, 1632.636, 0.566], [611.613, 1633.13, 0.641], [611.127, 1633.567, 0.816]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[439.78, 1227.349, 1.05], [426.44, 1268.51, 1.02], [325.982, 1240.255, 1.035], [445.556, 1380.946, 1.053]]\nB: [[383.14, 979.995, 1.108], [307.405, 1164.897, 0.89], [394.307, 1087.049, 1.131], [410.908, 1074.81, 0.998]]\nC: [[310.31, 1005.482, 1.062], [353.18, 1020.342, 0.94], [407.431, 1247.448, 1.209], [366.856, 956.543, 1.01]]\nD: [[376.13, 1158.507, 0.938], [376.399, 1159.165, 0.98], [376.667, 1159.822, 1.022], [376.878, 1160.357, 1.013]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_70_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_70_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_70_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_70_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_70_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_70_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_70_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_70_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[439.78, 1227.349, 1.05], [426.44, 1268.51, 1.02], [325.982, 1240.255, 1.035], [445.556, 1380.946, 1.053]]\nB: [[383.14, 979.995, 1.108], [307.405, 1164.897, 0.89], [394.307, 1087.049, 1.131], [410.908, 1074.81, 0.998]]\nC: [[310.31, 1005.482, 1.062], [353.18, 1020.342, 0.94], [407.431, 1247.448, 1.209], [366.856, 956.543, 1.01]]\nD: [[376.13, 1158.507, 0.938], [376.399, 1159.165, 0.98], [376.667, 1159.822, 1.022], [376.878, 1160.357, 1.013]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1700.614, 979.18, 0.431], [1688.381, 1025.064, 0.508], [2269.256, 744.117, 0.545], [2084.454, 772.655, 0.492]]\nB: [[2210.59, 1003.05, 0.548], [1727.203, 861.604, 0.538], [1904.192, 830.147, 0.392], [1890.542, 842.708, 0.427]]\nC: [[1895.763, 879.04, 0.501], [1895.752, 879.076, 0.488], [1895.741, 879.112, 0.476], [1895.739, 879.116, 0.464]]\nD: [[1616.91, 819.62, 0.433], [2209.262, 739.792, 0.446], [2172.452, 852.21, 0.474], [2247.291, 966.129, 0.485]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_71_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_71_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_71_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_71_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_71_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_71_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_71_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_71_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1700.614, 979.18, 0.431], [1688.381, 1025.064, 0.508], [2269.256, 744.117, 0.545], [2084.454, 772.655, 0.492]]\nB: [[2210.59, 1003.05, 0.548], [1727.203, 861.604, 0.538], [1904.192, 830.147, 0.392], [1890.542, 842.708, 0.427]]\nC: [[1895.763, 879.04, 0.501], [1895.752, 879.076, 0.488], [1895.741, 879.112, 0.476], [1895.739, 879.116, 0.464]]\nD: [[1616.91, 819.62, 0.433], [2209.262, 739.792, 0.446], [2172.452, 852.21, 0.474], [2247.291, 966.129, 0.485]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[492.434, 1780.838, 1.16], [577.631, 1809.358, 1.725], [545.194, 1817.556, 1.603], [496.388, 1841.178, 2.172]]\nB: [[528.48, 1765.528, 1.61], [545.13, 1628.646, 1.326], [540.257, 1713.355, 1.75], [658.49, 1577.115, 2.147]]\nC: [[560.672, 1608.19, 1.54], [649.701, 1442.289, 1.318], [663.362, 1707.871, 2.131], [530.811, 1383.352, 2.114]]\nD: [[582.374, 1660.997, 1.38], [577.424, 1663.687, 1.585], [572.406, 1666.247, 1.789], [567.347, 1668.872, 2.039]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_72_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_72_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_72_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_72_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_72_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_72_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_72_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_72_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[492.434, 1780.838, 1.16], [577.631, 1809.358, 1.725], [545.194, 1817.556, 1.603], [496.388, 1841.178, 2.172]]\nB: [[528.48, 1765.528, 1.61], [545.13, 1628.646, 1.326], [540.257, 1713.355, 1.75], [658.49, 1577.115, 2.147]]\nC: [[560.672, 1608.19, 1.54], [649.701, 1442.289, 1.318], [663.362, 1707.871, 2.131], [530.811, 1383.352, 2.114]]\nD: [[582.374, 1660.997, 1.38], [577.424, 1663.687, 1.585], [572.406, 1666.247, 1.789], [567.347, 1668.872, 2.039]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[752.506, 1329.917, 0.448], [704.23, 1387.5, 0.595], [747.496, 1349.796, 0.742], [584.265, 1616.56, 0.8]]\nB: [[803.078, 1389.349, 0.388], [620.34, 1764.02, 0.522], [772.362, 1430.724, 0.537], [790.895, 1648.104, 0.8]]\nC: [[607.176, 1721.605, 0.459], [602.85, 1587.2, 0.548], [708.169, 1620.023, 0.647], [790.325, 1466.233, 0.7]]\nD: [[672.574, 1595.791, 0.388], [670.89, 1597.24, 0.625], [669.207, 1598.689, 0.663], [667.523, 1600.138, 0.7]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_73_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_73_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_73_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_73_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_73_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_73_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_73_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_73_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[752.506, 1329.917, 0.448], [704.23, 1387.5, 0.595], [747.496, 1349.796, 0.742], [584.265, 1616.56, 0.8]]\nB: [[803.078, 1389.349, 0.388], [620.34, 1764.02, 0.522], [772.362, 1430.724, 0.537], [790.895, 1648.104, 0.8]]\nC: [[607.176, 1721.605, 0.459], [602.85, 1587.2, 0.548], [708.169, 1620.023, 0.647], [790.325, 1466.233, 0.7]]\nD: [[672.574, 1595.791, 0.388], [670.89, 1597.24, 0.625], [669.207, 1598.689, 0.663], [667.523, 1600.138, 0.7]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[430.291, 1175.505, 0.588], [429.167, 1272.84, 0.836], [415.87, 1257.143, 0.882], [372.618, 1001.865, 0.689]]\nB: [[410.066, 1196.767, 0.656], [410.072, 1196.78, 0.706], [410.08, 1196.795, 0.756], [410.101, 1196.811, 0.756]]\nC: [[386.139, 1116.452, 0.72], [464.376, 1221.72, 0.778], [364.35, 1233.741, 0.755], [330.159, 1270.327, 0.687]]\nD: [[409.837, 984.781, 0.668], [440.225, 1048.51, 0.572], [446.2, 1257.02, 0.834], [482.338, 985.937, 0.624]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_74_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_74_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_74_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_74_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_74_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_74_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_74_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_74_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[430.291, 1175.505, 0.588], [429.167, 1272.84, 0.836], [415.87, 1257.143, 0.882], [372.618, 1001.865, 0.689]]\nB: [[410.066, 1196.767, 0.656], [410.072, 1196.78, 0.706], [410.08, 1196.795, 0.756], [410.101, 1196.811, 0.756]]\nC: [[386.139, 1116.452, 0.72], [464.376, 1221.72, 0.778], [364.35, 1233.741, 0.755], [330.159, 1270.327, 0.687]]\nD: [[409.837, 984.781, 0.668], [440.225, 1048.51, 0.572], [446.2, 1257.02, 0.834], [482.338, 985.937, 0.624]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[392.514, 1072.298, 0.999], [329.808, 1223.789, 1.084], [463.813, 1302.961, 0.914], [451.609, 1167.4, 1.154]]\nB: [[401.879, 1242.697, 0.728], [341.309, 990.577, 1.02], [450.947, 906.714, 1.117], [328.165, 960.327, 1.095]]\nC: [[342.127, 1107.321, 0.98], [447.285, 926.593, 0.964], [394.127, 898.801, 1.186], [403.682, 1324.015, 1.131]]\nD: [[391.204, 1112.576, 0.863], [391.204, 1112.576, 0.913], [391.208, 1112.586, 1.013], [391.212, 1112.595, 0.993]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_75_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_75_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_75_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_75_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_75_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_75_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_75_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_75_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[392.514, 1072.298, 0.999], [329.808, 1223.789, 1.084], [463.813, 1302.961, 0.914], [451.609, 1167.4, 1.154]]\nB: [[401.879, 1242.697, 0.728], [341.309, 990.577, 1.02], [450.947, 906.714, 1.117], [328.165, 960.327, 1.095]]\nC: [[342.127, 1107.321, 0.98], [447.285, 926.593, 0.964], [394.127, 898.801, 1.186], [403.682, 1324.015, 1.131]]\nD: [[391.204, 1112.576, 0.863], [391.204, 1112.576, 0.913], [391.208, 1112.586, 1.013], [391.212, 1112.595, 0.993]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1321.516, 1033.801, 1.008], [1321.517, 1033.801, 1.008], [1321.517, 1033.8, 1.008], [1321.518, 1033.8, 1.008]]\nB: [[1066.616, 1131.355, 0.921], [1219.6, 1098.492, 0.864], [1282.161, 961.0, 1.081], [1197.931, 1177.0, 1.016]]\nC: [[1190.352, 917.033, 1.028], [1155.143, 1161.133, 1.153], [1394.211, 959.1, 0.834], [1188.323, 1016.1, 1.08]]\nD: [[1067.426, 888.354, 0.843], [1441.448, 1176.105, 0.843], [1087.955, 967.6, 0.966], [1191.947, 906.7, 0.967]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_76_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_76_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_76_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_76_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_76_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_76_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_76_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_76_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1321.516, 1033.801, 1.008], [1321.517, 1033.801, 1.008], [1321.517, 1033.8, 1.008], [1321.518, 1033.8, 1.008]]\nB: [[1066.616, 1131.355, 0.921], [1219.6, 1098.492, 0.864], [1282.161, 961.0, 1.081], [1197.931, 1177.0, 1.016]]\nC: [[1190.352, 917.033, 1.028], [1155.143, 1161.133, 1.153], [1394.211, 959.1, 0.834], [1188.323, 1016.1, 1.08]]\nD: [[1067.426, 888.354, 0.843], [1441.448, 1176.105, 0.843], [1087.955, 967.6, 0.966], [1191.947, 906.7, 0.967]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[438.849, 951.711, 0.795], [314.542, 1143.97, 0.648], [362.865, 1209.907, 0.806], [438.018, 933.567, 0.84]]\nB: [[388.688, 1111.433, 0.677], [388.691, 1111.43, 0.695], [388.695, 1111.428, 0.713], [388.698, 1111.426, 0.716]]\nC: [[316.274, 909.79, 0.674], [451.235, 958.84, 0.605], [365.204, 1239.893, 0.608], [426.654, 1268.736, 0.816]]\nD: [[452.526, 1172.998, 0.585], [454.014, 1000.44, 0.724], [336.213, 1132.703, 0.811], [313.791, 1218.829, 0.612]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_77_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_77_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_77_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_77_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_77_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_77_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_77_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_77_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[438.849, 951.711, 0.795], [314.542, 1143.97, 0.648], [362.865, 1209.907, 0.806], [438.018, 933.567, 0.84]]\nB: [[388.688, 1111.433, 0.677], [388.691, 1111.43, 0.695], [388.695, 1111.428, 0.713], [388.698, 1111.426, 0.716]]\nC: [[316.274, 909.79, 0.674], [451.235, 958.84, 0.605], [365.204, 1239.893, 0.608], [426.654, 1268.736, 0.816]]\nD: [[452.526, 1172.998, 0.585], [454.014, 1000.44, 0.724], [336.213, 1132.703, 0.811], [313.791, 1218.829, 0.612]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[625.166, 1621.071, -0.136], [624.733, 1621.463, -0.074], [624.328, 1621.884, -0.011], [624.034, 1622.205, 0.176]]\nB: [[673.606, 1641.602, -0.151], [650.83, 1385.101, -0.066], [545.785, 1758.678, -0.013], [623.635, 1668.753, 0.151]]\nC: [[612.227, 1304.459, -0.119], [594.602, 1728.678, -0.08], [714.884, 1584.229, -0.012], [716.369, 1325.064, 0.2]]\nD: [[677.007, 1319.892, -0.145], [590.033, 1617.266, -0.079], [508.485, 1809.42, -0.012], [584.009, 1851.902, 0.196]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_78_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_78_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_78_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_78_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_78_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_78_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_78_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_78_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[625.166, 1621.071, -0.136], [624.733, 1621.463, -0.074], [624.328, 1621.884, -0.011], [624.034, 1622.205, 0.176]]\nB: [[673.606, 1641.602, -0.151], [650.83, 1385.101, -0.066], [545.785, 1758.678, -0.013], [623.635, 1668.753, 0.151]]\nC: [[612.227, 1304.459, -0.119], [594.602, 1728.678, -0.08], [714.884, 1584.229, -0.012], [716.369, 1325.064, 0.2]]\nD: [[677.007, 1319.892, -0.145], [590.033, 1617.266, -0.079], [508.485, 1809.42, -0.012], [584.009, 1851.902, 0.196]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[500.254, 1116.383, 0.109], [528.871, 1208.17, 0.09], [370.111, 1189.599, 0.128], [490.724, 1065.954, 0.15]]\nB: [[424.937, 1288.953, 0.124], [494.735, 1135.49, 0.095], [377.247, 1056.094, 0.129], [454.303, 1168.649, 0.16]]\nC: [[445.198, 1091.608, 0.107], [445.269, 1091.74, 0.084], [445.269, 1091.738, 0.117], [445.269, 1091.735, 0.15]]\nD: [[518.8, 1113.376, 0.123], [424.179, 929.73, 0.097], [415.562, 1089.363, 0.113], [387.889, 1032.784, 0.17]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_79_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_79_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_79_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_79_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_79_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_79_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_79_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_79_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[500.254, 1116.383, 0.109], [528.871, 1208.17, 0.09], [370.111, 1189.599, 0.128], [490.724, 1065.954, 0.15]]\nB: [[424.937, 1288.953, 0.124], [494.735, 1135.49, 0.095], [377.247, 1056.094, 0.129], [454.303, 1168.649, 0.16]]\nC: [[445.198, 1091.608, 0.107], [445.269, 1091.74, 0.084], [445.269, 1091.738, 0.117], [445.269, 1091.735, 0.15]]\nD: [[518.8, 1113.376, 0.123], [424.179, 929.73, 0.097], [415.562, 1089.363, 0.113], [387.889, 1032.784, 0.17]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1484.746, 911.994, 1.171], [1137.15, 1003.822, 1.118], [1492.05, 930.243, 1.121], [1124.198, 1141.212, 1.261]]\nB: [[1219.797, 981.822, 1.234], [1419.86, 1093.926, 0.969], [1395.832, 917.571, 1.104], [1330.08, 1062.03, 1.216]]\nC: [[1453.95, 988.704, 0.898], [1551.29, 1210.843, 1.28], [1428.034, 1104.909, 1.233], [1371.047, 908.624, 1.137]]\nD: [[1328.982, 1049.561, 1.089], [1328.99, 1049.562, 1.089], [1328.997, 1049.563, 1.089], [1329.005, 1049.565, 1.089]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_80_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_80_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_80_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_80_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_80_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_80_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_80_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_80_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1484.746, 911.994, 1.171], [1137.15, 1003.822, 1.118], [1492.05, 930.243, 1.121], [1124.198, 1141.212, 1.261]]\nB: [[1219.797, 981.822, 1.234], [1419.86, 1093.926, 0.969], [1395.832, 917.571, 1.104], [1330.08, 1062.03, 1.216]]\nC: [[1453.95, 988.704, 0.898], [1551.29, 1210.843, 1.28], [1428.034, 1104.909, 1.233], [1371.047, 908.624, 1.137]]\nD: [[1328.982, 1049.561, 1.089], [1328.99, 1049.562, 1.089], [1328.997, 1049.563, 1.089], [1329.005, 1049.565, 1.089]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[456.432, 1282.157, 0.891], [479.945, 1042.78, 0.822], [407.218, 1084.054, 0.7], [446.375, 997.916, 0.683]]\nB: [[462.967, 894.315, 0.986], [392.745, 966.59, 0.805], [391.102, 1018.399, 0.622], [493.885, 1286.081, 0.965]]\nC: [[466.287, 926.158, 0.882], [352.099, 1212.35, 0.658], [429.631, 1077.672, 0.822], [411.455, 1150.981, 0.801]]\nD: [[430.242, 1089.779, 1.026], [430.279, 1089.87, 0.776], [430.299, 1089.898, 0.776], [430.321, 1089.952, 0.817]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_81_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_81_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_81_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_81_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_81_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_81_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_81_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_81_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[456.432, 1282.157, 0.891], [479.945, 1042.78, 0.822], [407.218, 1084.054, 0.7], [446.375, 997.916, 0.683]]\nB: [[462.967, 894.315, 0.986], [392.745, 966.59, 0.805], [391.102, 1018.399, 0.622], [493.885, 1286.081, 0.965]]\nC: [[466.287, 926.158, 0.882], [352.099, 1212.35, 0.658], [429.631, 1077.672, 0.822], [411.455, 1150.981, 0.801]]\nD: [[430.242, 1089.779, 1.026], [430.279, 1089.87, 0.776], [430.299, 1089.898, 0.776], [430.321, 1089.952, 0.817]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1945.526, 876.296, 0.419], [1945.526, 876.242, 0.469], [1945.526, 876.177, 0.469], [1945.526, 876.26, 0.519]]\nB: [[2158.266, 800.911, 0.453], [2106.839, 1043.586, 0.501], [2049.112, 832.682, 0.434], [2030.483, 957.49, 0.61]]\nC: [[2028.562, 929.081, 0.457], [1728.295, 771.666, 0.406], [2125.198, 983.306, 0.535], [2151.856, 925.1, 0.483]]\nD: [[2333.669, 1007.109, 0.449], [1683.52, 730.695, 0.511], [2240.73, 776.757, 0.511], [1717.598, 731.99, 0.548]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_82_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_82_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_82_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_82_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_82_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_82_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_82_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_82_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1945.526, 876.296, 0.419], [1945.526, 876.242, 0.469], [1945.526, 876.177, 0.469], [1945.526, 876.26, 0.519]]\nB: [[2158.266, 800.911, 0.453], [2106.839, 1043.586, 0.501], [2049.112, 832.682, 0.434], [2030.483, 957.49, 0.61]]\nC: [[2028.562, 929.081, 0.457], [1728.295, 771.666, 0.406], [2125.198, 983.306, 0.535], [2151.856, 925.1, 0.483]]\nD: [[2333.669, 1007.109, 0.449], [1683.52, 730.695, 0.511], [2240.73, 776.757, 0.511], [1717.598, 731.99, 0.548]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1937.491, 914.639, 0.22], [1901.969, 968.51, 0.273], [1716.987, 958.003, 0.262], [1808.463, 962.345, 0.288]]\nB: [[2007.196, 817.175, 0.254], [1773.432, 871.003, 0.284], [1939.918, 1002.574, 0.304], [2252.629, 811.34, 0.262]]\nC: [[2285.927, 936.67, 0.263], [1604.253, 825.974, 0.25], [2118.153, 905.274, 0.26], [1884.079, 918.838, 0.296]]\nD: [[1926.631, 877.571, 0.228], [1926.631, 877.571, 0.252], [1926.626, 877.593, 0.255], [1926.638, 877.538, 0.303]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_83_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_83_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_83_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_83_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_83_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_83_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_83_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_83_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1937.491, 914.639, 0.22], [1901.969, 968.51, 0.273], [1716.987, 958.003, 0.262], [1808.463, 962.345, 0.288]]\nB: [[2007.196, 817.175, 0.254], [1773.432, 871.003, 0.284], [1939.918, 1002.574, 0.304], [2252.629, 811.34, 0.262]]\nC: [[2285.927, 936.67, 0.263], [1604.253, 825.974, 0.25], [2118.153, 905.274, 0.26], [1884.079, 918.838, 0.296]]\nD: [[1926.631, 877.571, 0.228], [1926.631, 877.571, 0.252], [1926.626, 877.593, 0.255], [1926.638, 877.538, 0.303]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[680.728, 1749.077, -0.375], [745.701, 1474.694, -0.299], [610.56, 1380.492, -0.27], [635.92, 1915.689, -0.136]]\nB: [[660.851, 1604.404, -0.423], [657.771, 1607.079, -0.332], [654.69, 1609.754, -0.24], [651.61, 1612.428, -0.148]]\nC: [[647.562, 1445.984, -0.429], [659.321, 1909.729, -0.283], [754.69, 1382.093, -0.26], [549.55, 1888.817, -0.122]]\nD: [[751.514, 1476.27, -0.49], [654.016, 1488.662, -0.332], [753.33, 1931.072, -0.2], [574.93, 1792.093, -0.171]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_84_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_84_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_84_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_84_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_84_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_84_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_84_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_84_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[680.728, 1749.077, -0.375], [745.701, 1474.694, -0.299], [610.56, 1380.492, -0.27], [635.92, 1915.689, -0.136]]\nB: [[660.851, 1604.404, -0.423], [657.771, 1607.079, -0.332], [654.69, 1609.754, -0.24], [651.61, 1612.428, -0.148]]\nC: [[647.562, 1445.984, -0.429], [659.321, 1909.729, -0.283], [754.69, 1382.093, -0.26], [549.55, 1888.817, -0.122]]\nD: [[751.514, 1476.27, -0.49], [654.016, 1488.662, -0.332], [753.33, 1931.072, -0.2], [574.93, 1792.093, -0.171]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[356.068, 1144.504, 0.82], [356.64, 1144.191, 0.795], [358.929, 1142.941, 0.822], [359.501, 1142.629, 0.839]]\nB: [[401.199, 1094.551, 0.83], [308.99, 1334.228, 0.943], [415.452, 921.574, 0.753], [392.805, 1225.338, 0.965]]\nC: [[395.4, 1321.544, 0.95], [322.87, 1045.667, 0.91], [342.828, 1295.35, 0.695], [397.067, 940.796, 0.768]]\nD: [[418.406, 1138.796, 0.82], [416.34, 1311.233, 0.684], [355.451, 1305.707, 0.882], [410.239, 1120.033, 0.971]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_85_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_85_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_85_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_85_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_85_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_85_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_85_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_85_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[356.068, 1144.504, 0.82], [356.64, 1144.191, 0.795], [358.929, 1142.941, 0.822], [359.501, 1142.629, 0.839]]\nB: [[401.199, 1094.551, 0.83], [308.99, 1334.228, 0.943], [415.452, 921.574, 0.753], [392.805, 1225.338, 0.965]]\nC: [[395.4, 1321.544, 0.95], [322.87, 1045.667, 0.91], [342.828, 1295.35, 0.695], [397.067, 940.796, 0.768]]\nD: [[418.406, 1138.796, 0.82], [416.34, 1311.233, 0.684], [355.451, 1305.707, 0.882], [410.239, 1120.033, 0.971]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1753.666, 1018.404, 0.365], [1524.0, 883.794, 0.327], [2021.935, 879.24, 0.307], [1882.989, 791.594, 0.35]]\nB: [[2170.362, 809.726, 0.373], [2168.605, 703.253, 0.314], [1918.642, 995.58, 0.329], [1602.549, 910.935, 0.29]]\nC: [[2178.785, 988.248, 0.285], [2227.998, 705.37, 0.287], [1566.17, 877.23, 0.318], [1931.258, 826.324, 0.31]]\nD: [[1902.434, 878.055, 0.343], [1902.434, 878.055, 0.293], [1902.429, 878.07, 0.302], [1902.423, 878.086, 0.31]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_86_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_86_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_86_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_86_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_86_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_86_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_86_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_86_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1753.666, 1018.404, 0.365], [1524.0, 883.794, 0.327], [2021.935, 879.24, 0.307], [1882.989, 791.594, 0.35]]\nB: [[2170.362, 809.726, 0.373], [2168.605, 703.253, 0.314], [1918.642, 995.58, 0.329], [1602.549, 910.935, 0.29]]\nC: [[2178.785, 988.248, 0.285], [2227.998, 705.37, 0.287], [1566.17, 877.23, 0.318], [1931.258, 826.324, 0.31]]\nD: [[1902.434, 878.055, 0.343], [1902.434, 878.055, 0.293], [1902.429, 878.07, 0.302], [1902.423, 878.086, 0.31]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[378.922, 1013.673, 0.734], [494.488, 1078.797, 0.456], [393.735, 912.986, 0.581], [419.965, 958.358, 0.767]]\nB: [[433.059, 1088.732, 0.713], [433.043, 1088.668, 0.553], [433.039, 1088.652, 0.513], [433.055, 1088.681, 0.703]]\nC: [[426.779, 1210.184, 0.683], [374.724, 1199.914, 0.579], [356.893, 998.508, 0.494], [512.758, 1067.304, 0.691]]\nD: [[351.961, 935.844, 0.571], [386.254, 1200.68, 0.61], [480.449, 1191.815, 0.483], [412.037, 930.978, 0.833]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_87_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_87_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_87_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_87_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_87_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_87_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_87_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_87_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[378.922, 1013.673, 0.734], [494.488, 1078.797, 0.456], [393.735, 912.986, 0.581], [419.965, 958.358, 0.767]]\nB: [[433.059, 1088.732, 0.713], [433.043, 1088.668, 0.553], [433.039, 1088.652, 0.513], [433.055, 1088.681, 0.703]]\nC: [[426.779, 1210.184, 0.683], [374.724, 1199.914, 0.579], [356.893, 998.508, 0.494], [512.758, 1067.304, 0.691]]\nD: [[351.961, 935.844, 0.571], [386.254, 1200.68, 0.61], [480.449, 1191.815, 0.483], [412.037, 930.978, 0.833]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2045.551, 727.654, 1.102], [1833.302, 769.345, 1.055], [1827.43, 963.776, 1.306], [1702.867, 738.416, 0.98]]\nB: [[1661.503, 737.862, 0.962], [1861.333, 965.185, 0.908], [1821.87, 992.03, 0.919], [2075.341, 874.239, 1.077]]\nC: [[1741.077, 864.895, 1.109], [1745.181, 865.139, 1.105], [1748.91, 865.361, 1.102], [1752.336, 865.549, 1.096]]\nD: [[1791.757, 846.823, 0.925], [1982.741, 721.703, 1.059], [1406.89, 829.542, 1.078], [1577.41, 813.096, 1.053]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_88_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_88_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_88_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_88_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_88_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_88_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_88_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_88_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2045.551, 727.654, 1.102], [1833.302, 769.345, 1.055], [1827.43, 963.776, 1.306], [1702.867, 738.416, 0.98]]\nB: [[1661.503, 737.862, 0.962], [1861.333, 965.185, 0.908], [1821.87, 992.03, 0.919], [2075.341, 874.239, 1.077]]\nC: [[1741.077, 864.895, 1.109], [1745.181, 865.139, 1.105], [1748.91, 865.361, 1.102], [1752.336, 865.549, 1.096]]\nD: [[1791.757, 846.823, 0.925], [1982.741, 721.703, 1.059], [1406.89, 829.542, 1.078], [1577.41, 813.096, 1.053]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[505.05, 877.226, 0.203], [445.953, 1244.021, 0.202], [392.706, 1145.406, 0.277], [366.753, 1183.669, 0.29]]\nB: [[457.03, 1257.26, 0.228], [463.22, 1274.147, 0.225], [370.296, 997.948, 0.259], [365.28, 1022.072, 0.29]]\nC: [[434.02, 1096.492, 0.241], [434.019, 1096.492, 0.222], [434.019, 1096.492, 0.236], [434.018, 1096.493, 0.25]]\nD: [[422.09, 929.091, 0.197], [407.921, 1195.795, 0.198], [469.259, 1267.695, 0.23], [354.798, 1155.602, 0.26]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_89_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_89_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_89_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_89_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_89_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_89_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_89_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_89_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[505.05, 877.226, 0.203], [445.953, 1244.021, 0.202], [392.706, 1145.406, 0.277], [366.753, 1183.669, 0.29]]\nB: [[457.03, 1257.26, 0.228], [463.22, 1274.147, 0.225], [370.296, 997.948, 0.259], [365.28, 1022.072, 0.29]]\nC: [[434.02, 1096.492, 0.241], [434.019, 1096.492, 0.222], [434.019, 1096.492, 0.236], [434.018, 1096.493, 0.25]]\nD: [[422.09, 929.091, 0.197], [407.921, 1195.795, 0.198], [469.259, 1267.695, 0.23], [354.798, 1155.602, 0.26]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[382.439, 944.308, 1.097], [282.072, 1346.475, 1.314], [317.768, 1056.456, 0.981], [328.142, 1067.671, 0.969]]\nB: [[348.689, 1130.152, 1.122], [348.689, 1130.152, 1.122], [348.689, 1130.152, 1.122], [348.689, 1130.152, 1.122]]\nC: [[290.351, 1111.854, 1.019], [383.245, 975.676, 1.11], [292.501, 1319.267, 0.953], [293.662, 1130.698, 0.975]]\nD: [[344.597, 1317.41, 1.004], [391.599, 1063.24, 1.128], [415.864, 1014.121, 0.9], [383.217, 1223.267, 1.321]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_90_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_90_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_90_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_90_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_90_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_90_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_90_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_90_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[382.439, 944.308, 1.097], [282.072, 1346.475, 1.314], [317.768, 1056.456, 0.981], [328.142, 1067.671, 0.969]]\nB: [[348.689, 1130.152, 1.122], [348.689, 1130.152, 1.122], [348.689, 1130.152, 1.122], [348.689, 1130.152, 1.122]]\nC: [[290.351, 1111.854, 1.019], [383.245, 975.676, 1.11], [292.501, 1319.267, 0.953], [293.662, 1130.698, 0.975]]\nD: [[344.597, 1317.41, 1.004], [391.599, 1063.24, 1.128], [415.864, 1014.121, 0.9], [383.217, 1223.267, 1.321]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[445.635, 1070.359, 0.563], [380.207, 1209.252, 0.55], [503.803, 1202.552, 0.618], [402.841, 878.242, 0.916]]\nB: [[355.09, 926.566, 0.532], [425.031, 931.852, 0.428], [396.378, 1283.26, 0.637], [419.782, 1021.681, 0.797]]\nC: [[353.198, 1052.673, 0.552], [387.581, 1075.215, 0.55], [453.134, 889.143, 0.766], [432.989, 976.738, 0.712]]\nD: [[435.434, 1087.782, 0.612], [435.403, 1087.706, 0.533], [435.405, 1087.711, 0.695], [435.407, 1087.716, 0.846]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_91_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_91_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_91_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_91_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_91_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_91_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_91_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_91_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[445.635, 1070.359, 0.563], [380.207, 1209.252, 0.55], [503.803, 1202.552, 0.618], [402.841, 878.242, 0.916]]\nB: [[355.09, 926.566, 0.532], [425.031, 931.852, 0.428], [396.378, 1283.26, 0.637], [419.782, 1021.681, 0.797]]\nC: [[353.198, 1052.673, 0.552], [387.581, 1075.215, 0.55], [453.134, 889.143, 0.766], [432.989, 976.738, 0.712]]\nD: [[435.434, 1087.782, 0.612], [435.403, 1087.706, 0.533], [435.405, 1087.711, 0.695], [435.407, 1087.716, 0.846]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1858.977, 3005.32, 0.085], [1613.167, 2545.921, 0.166], [1534.084, 2595.697, 0.482], [1872.638, 2228.595, 0.519]]\nB: [[1516.054, 2338.945, 0.097], [1891.297, 2428.151, 0.236], [1796.827, 2149.677, 0.559], [1658.932, 2766.556, 0.382]]\nC: [[2005.599, 2965.349, 0.12], [1626.186, 2645.937, 0.181], [1937.717, 2253.069, 0.541], [1779.108, 2893.005, 0.435]]\nD: [[1824.199, 2571.318, 0.101], [1824.516, 2570.899, 0.205], [1825.469, 2569.639, 0.518], [1825.787, 2569.219, 0.434]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_92_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_92_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_92_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_92_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_92_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_92_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_92_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_92_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1858.977, 3005.32, 0.085], [1613.167, 2545.921, 0.166], [1534.084, 2595.697, 0.482], [1872.638, 2228.595, 0.519]]\nB: [[1516.054, 2338.945, 0.097], [1891.297, 2428.151, 0.236], [1796.827, 2149.677, 0.559], [1658.932, 2766.556, 0.382]]\nC: [[2005.599, 2965.349, 0.12], [1626.186, 2645.937, 0.181], [1937.717, 2253.069, 0.541], [1779.108, 2893.005, 0.435]]\nD: [[1824.199, 2571.318, 0.101], [1824.516, 2570.899, 0.205], [1825.469, 2569.639, 0.518], [1825.787, 2569.219, 0.434]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[598.103, 1642.075, 1.029], [598.744, 1641.829, 1.029], [599.384, 1641.583, 1.029], [600.026, 1641.338, 1.179]]\nB: [[701.368, 1416.38, 0.896], [530.801, 1778.726, 1.056], [579.309, 1558.364, 0.977], [683.542, 1838.774, 1.325]]\nC: [[715.233, 1896.029, 0.968], [530.58, 1520.538, 0.944], [596.209, 1472.502, 0.856], [536.626, 1453.346, 1.289]]\nD: [[626.221, 1751.17, 1.049], [568.701, 1547.296, 1.076], [640.532, 1458.354, 1.122], [626.284, 1959.943, 1.094]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_93_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_93_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_93_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_93_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_93_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_93_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_93_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_93_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[598.103, 1642.075, 1.029], [598.744, 1641.829, 1.029], [599.384, 1641.583, 1.029], [600.026, 1641.338, 1.179]]\nB: [[701.368, 1416.38, 0.896], [530.801, 1778.726, 1.056], [579.309, 1558.364, 0.977], [683.542, 1838.774, 1.325]]\nC: [[715.233, 1896.029, 0.968], [530.58, 1520.538, 0.944], [596.209, 1472.502, 0.856], [536.626, 1453.346, 1.289]]\nD: [[626.221, 1751.17, 1.049], [568.701, 1547.296, 1.076], [640.532, 1458.354, 1.122], [626.284, 1959.943, 1.094]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[413.954, 1081.236, 0.717], [451.481, 1023.032, 0.495], [397.92, 951.148, 0.604], [328.441, 1181.445, 0.598]]\nB: [[457.154, 1119.531, 0.668], [402.874, 923.594, 0.435], [447.684, 1012.752, 0.547], [341.799, 1237.225, 0.728]]\nC: [[354.36, 1174.911, 0.52], [371.321, 1042.76, 0.471], [448.856, 1142.068, 0.628], [427.925, 1261.83, 0.519]]\nD: [[389.399, 1112.311, 0.629], [389.356, 1112.334, 0.529], [389.379, 1112.321, 0.579], [389.403, 1112.309, 0.629]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_94_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_94_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_94_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_94_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_94_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_94_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_94_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_94_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[413.954, 1081.236, 0.717], [451.481, 1023.032, 0.495], [397.92, 951.148, 0.604], [328.441, 1181.445, 0.598]]\nB: [[457.154, 1119.531, 0.668], [402.874, 923.594, 0.435], [447.684, 1012.752, 0.547], [341.799, 1237.225, 0.728]]\nC: [[354.36, 1174.911, 0.52], [371.321, 1042.76, 0.471], [448.856, 1142.068, 0.628], [427.925, 1261.83, 0.519]]\nD: [[389.399, 1112.311, 0.629], [389.356, 1112.334, 0.529], [389.379, 1112.321, 0.579], [389.403, 1112.309, 0.629]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[611.892, 1806.578, 0.268], [743.567, 1822.952, 0.264], [555.945, 1619.632, 0.282], [736.456, 1610.791, 0.275]]\nB: [[647.522, 1603.835, 0.243], [647.522, 1603.835, 0.293], [647.522, 1603.835, 0.318], [647.522, 1603.835, 0.343]]\nC: [[613.269, 1753.144, 0.259], [701.513, 1670.735, 0.335], [698.245, 1622.138, 0.321], [547.954, 1706.296, 0.366]]\nD: [[518.452, 1481.659, 0.23], [579.958, 1410.188, 0.243], [523.377, 1912.789, 0.276], [661.654, 1701.9, 0.34]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_95_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_95_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_95_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_95_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_95_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_95_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_95_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_95_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[611.892, 1806.578, 0.268], [743.567, 1822.952, 0.264], [555.945, 1619.632, 0.282], [736.456, 1610.791, 0.275]]\nB: [[647.522, 1603.835, 0.243], [647.522, 1603.835, 0.293], [647.522, 1603.835, 0.318], [647.522, 1603.835, 0.343]]\nC: [[613.269, 1753.144, 0.259], [701.513, 1670.735, 0.335], [698.245, 1622.138, 0.321], [547.954, 1706.296, 0.366]]\nD: [[518.452, 1481.659, 0.23], [579.958, 1410.188, 0.243], [523.377, 1912.789, 0.276], [661.654, 1701.9, 0.34]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[631.815, 1636.973, 0.074], [631.58, 1636.905, 0.174], [631.313, 1636.901, 0.224], [631.183, 1636.842, 0.29]]\nB: [[559.113, 1943.842, 0.076], [518.03, 1864.19, 0.151], [546.229, 1683.354, 0.205], [539.475, 1389.243, 0.24]]\nC: [[689.175, 1624.485, 0.066], [688.09, 1571.158, 0.178], [563.905, 1790.085, 0.19], [581.151, 1421.06, 0.29]]\nD: [[705.525, 1667.251, 0.065], [604.36, 1921.35, 0.181], [722.147, 1476.341, 0.225], [572.745, 1584.256, 0.3]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_96_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_96_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_96_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_96_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_96_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_96_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_96_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_96_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[631.815, 1636.973, 0.074], [631.58, 1636.905, 0.174], [631.313, 1636.901, 0.224], [631.183, 1636.842, 0.29]]\nB: [[559.113, 1943.842, 0.076], [518.03, 1864.19, 0.151], [546.229, 1683.354, 0.205], [539.475, 1389.243, 0.24]]\nC: [[689.175, 1624.485, 0.066], [688.09, 1571.158, 0.178], [563.905, 1790.085, 0.19], [581.151, 1421.06, 0.29]]\nD: [[705.525, 1667.251, 0.065], [604.36, 1921.35, 0.181], [722.147, 1476.341, 0.225], [572.745, 1584.256, 0.3]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1782.298, 752.391, 0.81], [2097.954, 882.859, 0.769], [1957.145, 911.959, 1.057], [1675.514, 745.849, 0.876]]\nB: [[1869.593, 872.653, 0.94], [1517.943, 982.023, 0.755], [2107.729, 753.584, 0.782], [1585.194, 934.333, 0.784]]\nC: [[1792.225, 846.971, 0.887], [1792.225, 846.971, 0.887], [1792.225, 846.971, 0.887], [1792.225, 846.971, 0.887]]\nD: [[1767.979, 682.569, 0.962], [2081.075, 907.416, 0.768], [2002.738, 790.434, 0.955], [1720.834, 852.507, 1.032]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_97_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_97_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_97_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_97_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_97_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_97_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_97_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_97_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1782.298, 752.391, 0.81], [2097.954, 882.859, 0.769], [1957.145, 911.959, 1.057], [1675.514, 745.849, 0.876]]\nB: [[1869.593, 872.653, 0.94], [1517.943, 982.023, 0.755], [2107.729, 753.584, 0.782], [1585.194, 934.333, 0.784]]\nC: [[1792.225, 846.971, 0.887], [1792.225, 846.971, 0.887], [1792.225, 846.971, 0.887], [1792.225, 846.971, 0.887]]\nD: [[1767.979, 682.569, 0.962], [2081.075, 907.416, 0.768], [2002.738, 790.434, 0.955], [1720.834, 852.507, 1.032]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[724.326, 1495.604, -0.327], [676.702, 1884.979, 0.087], [764.302, 1335.092, 0.381], [548.436, 1731.853, 0.371]]\nB: [[651.934, 1624.096, -0.297], [652.686, 1623.474, 0.103], [653.181, 1623.053, 0.328], [653.687, 1622.622, 0.353]]\nC: [[607.922, 1820.17, -0.295], [753.684, 1433.034, 0.106], [678.243, 1447.392, 0.296], [570.652, 1420.87, 0.385]]\nD: [[751.414, 1685.804, -0.244], [578.476, 1684.213, 0.096], [590.679, 1441.453, 0.28], [536.506, 1378.646, 0.32]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_98_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_98_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_98_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_98_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_98_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_98_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_98_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_98_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[724.326, 1495.604, -0.327], [676.702, 1884.979, 0.087], [764.302, 1335.092, 0.381], [548.436, 1731.853, 0.371]]\nB: [[651.934, 1624.096, -0.297], [652.686, 1623.474, 0.103], [653.181, 1623.053, 0.328], [653.687, 1622.622, 0.353]]\nC: [[607.922, 1820.17, -0.295], [753.684, 1433.034, 0.106], [678.243, 1447.392, 0.296], [570.652, 1420.87, 0.385]]\nD: [[751.414, 1685.804, -0.244], [578.476, 1684.213, 0.096], [590.679, 1441.453, 0.28], [536.506, 1378.646, 0.32]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1335.014, 862.316, 0.287], [1100.87, 1037.094, 0.23], [1485.551, 931.776, 0.26], [1291.897, 1074.94, 0.272]]\nB: [[1414.862, 952.185, 0.246], [1191.79, 1180.934, 0.227], [1337.485, 931.666, 0.225], [1205.041, 976.826, 0.257]]\nC: [[1365.108, 1014.952, 0.254], [1365.101, 1014.929, 0.254], [1365.094, 1014.907, 0.254], [1365.086, 1014.885, 0.254]]\nD: [[1286.094, 1146.653, 0.233], [1369.879, 1146.619, 0.278], [1377.756, 963.077, 0.259], [1621.927, 877.672, 0.256]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_99_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_99_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_99_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_99_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_99_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_99_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_99_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_99_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1335.014, 862.316, 0.287], [1100.87, 1037.094, 0.23], [1485.551, 931.776, 0.26], [1291.897, 1074.94, 0.272]]\nB: [[1414.862, 952.185, 0.246], [1191.79, 1180.934, 0.227], [1337.485, 931.666, 0.225], [1205.041, 976.826, 0.257]]\nC: [[1365.108, 1014.952, 0.254], [1365.101, 1014.929, 0.254], [1365.094, 1014.907, 0.254], [1365.086, 1014.885, 0.254]]\nD: [[1286.094, 1146.653, 0.233], [1369.879, 1146.619, 0.278], [1377.756, 963.077, 0.259], [1621.927, 877.672, 0.256]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[365.047, 598.348, 1.241], [286.54, 738.564, 1.05], [387.446, 604.928, 1.198], [391.006, 671.391, 1.378]]\nB: [[377.966, 628.41, 1.434], [361.11, 614.334, 1.35], [284.927, 755.854, 1.174], [403.902, 539.249, 1.302]]\nC: [[341.337, 715.12, 1.189], [372.63, 619.563, 1.39], [402.819, 670.746, 1.313], [340.745, 536.458, 1.343]]\nD: [[345.848, 655.799, 1.196], [343.13, 656.562, 1.18], [340.412, 657.325, 1.165], [337.693, 658.088, 1.149]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_100_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_100_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_100_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_100_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_100_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_100_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_100_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_100_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[365.047, 598.348, 1.241], [286.54, 738.564, 1.05], [387.446, 604.928, 1.198], [391.006, 671.391, 1.378]]\nB: [[377.966, 628.41, 1.434], [361.11, 614.334, 1.35], [284.927, 755.854, 1.174], [403.902, 539.249, 1.302]]\nC: [[341.337, 715.12, 1.189], [372.63, 619.563, 1.39], [402.819, 670.746, 1.313], [340.745, 536.458, 1.343]]\nD: [[345.848, 655.799, 1.196], [343.13, 656.562, 1.18], [340.412, 657.325, 1.165], [337.693, 658.088, 1.149]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[423.365, 1081.357, 1.92], [358.32, 1065.751, 2.221], [393.86, 1013.258, 1.856], [426.12, 1121.332, 2.203]]\nB: [[374.803, 1125.969, 1.58], [323.517, 1358.518, 1.869], [421.71, 1325.966, 2.205], [374.867, 1307.159, 2.25]]\nC: [[345.831, 1321.961, 2.14], [438.53, 1243.074, 1.588], [406.44, 1418.879, 2.198], [347.216, 1381.306, 1.978]]\nD: [[382.736, 1209.839, 1.88], [383.093, 1209.198, 1.931], [383.45, 1208.557, 1.982], [383.786, 1207.915, 1.982]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_101_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_101_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_101_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_101_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_101_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_101_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_101_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_101_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[423.365, 1081.357, 1.92], [358.32, 1065.751, 2.221], [393.86, 1013.258, 1.856], [426.12, 1121.332, 2.203]]\nB: [[374.803, 1125.969, 1.58], [323.517, 1358.518, 1.869], [421.71, 1325.966, 2.205], [374.867, 1307.159, 2.25]]\nC: [[345.831, 1321.961, 2.14], [438.53, 1243.074, 1.588], [406.44, 1418.879, 2.198], [347.216, 1381.306, 1.978]]\nD: [[382.736, 1209.839, 1.88], [383.093, 1209.198, 1.931], [383.45, 1208.557, 1.982], [383.786, 1207.915, 1.982]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[633.909, 1608.489, 1.038], [670.109, 1891.19, 1.125], [522.041, 1396.622, 1.098], [541.68, 1745.168, 1.117]]\nB: [[635.642, 1415.988, 1.081], [633.621, 1654.57, 0.946], [601.731, 1438.83, 1.36], [652.901, 1593.526, 1.066]]\nC: [[555.337, 1356.247, 1.199], [627.708, 1668.4, 0.904], [707.793, 1894.062, 1.109], [480.816, 1651.213, 1.309]]\nD: [[583.549, 1656.391, 1.267], [587.422, 1654.32, 1.126], [591.257, 1652.222, 1.146], [594.995, 1650.206, 1.166]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_102_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_102_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_102_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_102_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_102_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_102_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_102_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_102_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[633.909, 1608.489, 1.038], [670.109, 1891.19, 1.125], [522.041, 1396.622, 1.098], [541.68, 1745.168, 1.117]]\nB: [[635.642, 1415.988, 1.081], [633.621, 1654.57, 0.946], [601.731, 1438.83, 1.36], [652.901, 1593.526, 1.066]]\nC: [[555.337, 1356.247, 1.199], [627.708, 1668.4, 0.904], [707.793, 1894.062, 1.109], [480.816, 1651.213, 1.309]]\nD: [[583.549, 1656.391, 1.267], [587.422, 1654.32, 1.126], [591.257, 1652.222, 1.146], [594.995, 1650.206, 1.166]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[697.83, 1848.354, 0.41], [586.56, 1505.654, 0.534], [677.42, 1687.731, 0.544], [605.12, 1621.517, 0.808]]\nB: [[519.11, 1562.82, 0.383], [612.23, 1842.267, 0.582], [524.07, 1920.47, 0.561], [598.47, 1708.973, 0.9]]\nC: [[723.89, 1578.062, 0.473], [519.71, 1405.785, 0.584], [581.29, 1953.42, 0.735], [668.56, 1675.091, 0.868]]\nD: [[619.03, 1648.941, 0.413], [618.43, 1649.273, 0.538], [617.83, 1649.605, 0.663], [617.17, 1649.888, 0.813]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_103_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_103_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_103_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_103_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_103_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_103_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_103_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_103_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[697.83, 1848.354, 0.41], [586.56, 1505.654, 0.534], [677.42, 1687.731, 0.544], [605.12, 1621.517, 0.808]]\nB: [[519.11, 1562.82, 0.383], [612.23, 1842.267, 0.582], [524.07, 1920.47, 0.561], [598.47, 1708.973, 0.9]]\nC: [[723.89, 1578.062, 0.473], [519.71, 1405.785, 0.584], [581.29, 1953.42, 0.735], [668.56, 1675.091, 0.868]]\nD: [[619.03, 1648.941, 0.413], [618.43, 1649.273, 0.538], [617.83, 1649.605, 0.663], [617.17, 1649.888, 0.813]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[403.885, 998.16, 0.544], [424.075, 1007.073, 0.82], [461.305, 1015.923, 0.726], [400.111, 1095.775, 0.878]]\nB: [[391.661, 1114.07, 0.663], [391.696, 1114.047, 0.738], [391.688, 1114.052, 0.813], [391.697, 1114.047, 0.818]]\nC: [[386.99, 1297.58, 0.743], [409.207, 1135.586, 0.659], [357.708, 1116.073, 0.868], [392.676, 1300.061, 0.744]]\nD: [[402.241, 1225.3, 0.56], [375.81, 983.912, 0.615], [412.224, 1111.715, 0.708], [393.052, 1232.005, 0.961]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_104_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_104_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_104_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_104_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_104_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_104_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_104_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_104_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[403.885, 998.16, 0.544], [424.075, 1007.073, 0.82], [461.305, 1015.923, 0.726], [400.111, 1095.775, 0.878]]\nB: [[391.661, 1114.07, 0.663], [391.696, 1114.047, 0.738], [391.688, 1114.052, 0.813], [391.697, 1114.047, 0.818]]\nC: [[386.99, 1297.58, 0.743], [409.207, 1135.586, 0.659], [357.708, 1116.073, 0.868], [392.676, 1300.061, 0.744]]\nD: [[402.241, 1225.3, 0.56], [375.81, 983.912, 0.615], [412.224, 1111.715, 0.708], [393.052, 1232.005, 0.961]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1277.031, 1033.186, 0.322], [1277.662, 1033.929, 0.322], [1279.057, 1035.823, 0.322], [1280.749, 1038.066, 0.372]]\nB: [[1421.88, 1233.909, 0.31], [1157.401, 1129.096, 0.349], [1356.001, 893.496, 0.351], [1288.688, 983.139, 0.356]]\nC: [[1125.382, 1211.613, 0.317], [1176.913, 1001.679, 0.291], [1346.252, 1080.898, 0.373], [1066.545, 1136.811, 0.352]]\nD: [[1059.6, 1024.583, 0.258], [1367.51, 878.274, 0.29], [1278.315, 1180.834, 0.347], [1136.279, 1162.583, 0.374]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_105_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_105_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_105_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_105_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_105_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_105_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_105_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_105_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1277.031, 1033.186, 0.322], [1277.662, 1033.929, 0.322], [1279.057, 1035.823, 0.322], [1280.749, 1038.066, 0.372]]\nB: [[1421.88, 1233.909, 0.31], [1157.401, 1129.096, 0.349], [1356.001, 893.496, 0.351], [1288.688, 983.139, 0.356]]\nC: [[1125.382, 1211.613, 0.317], [1176.913, 1001.679, 0.291], [1346.252, 1080.898, 0.373], [1066.545, 1136.811, 0.352]]\nD: [[1059.6, 1024.583, 0.258], [1367.51, 878.274, 0.29], [1278.315, 1180.834, 0.347], [1136.279, 1162.583, 0.374]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[415.331, 1104.242, 0.613], [415.326, 1104.24, 0.64], [415.316, 1104.236, 0.695], [415.31, 1104.234, 0.723]]\nB: [[345.131, 1192.788, 0.505], [371.883, 1269.14, 0.69], [447.888, 1224.599, 0.614], [392.44, 1170.864, 0.783]]\nC: [[454.914, 1278.297, 0.71], [434.859, 1136.62, 0.63], [416.021, 1211.093, 0.643], [337.73, 960.185, 0.669]]\nD: [[480.222, 1069.404, 0.56], [365.517, 970.37, 0.54], [386.514, 975.732, 0.743], [393.7, 923.805, 0.642]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_106_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_106_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_106_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_106_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_106_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_106_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_106_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_106_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[415.331, 1104.242, 0.613], [415.326, 1104.24, 0.64], [415.316, 1104.236, 0.695], [415.31, 1104.234, 0.723]]\nB: [[345.131, 1192.788, 0.505], [371.883, 1269.14, 0.69], [447.888, 1224.599, 0.614], [392.44, 1170.864, 0.783]]\nC: [[454.914, 1278.297, 0.71], [434.859, 1136.62, 0.63], [416.021, 1211.093, 0.643], [337.73, 960.185, 0.669]]\nD: [[480.222, 1069.404, 0.56], [365.517, 970.37, 0.54], [386.514, 975.732, 0.743], [393.7, 923.805, 0.642]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[407.576, 1163.308, 0.729], [407.573, 1163.324, 0.746], [407.57, 1163.34, 0.762], [407.569, 1163.357, 0.779]]\nB: [[387.473, 1137.771, 0.644], [384.287, 1365.683, 0.681], [390.89, 1137.17, 0.617], [457.784, 1284.967, 0.839]]\nC: [[360.164, 1015.983, 0.678], [381.237, 1053.29, 0.859], [457.22, 1360.88, 0.63], [408.603, 1334.048, 0.816]]\nD: [[392.585, 1372.768, 0.686], [426.374, 1363.72, 0.752], [443.3, 955.82, 0.704], [326.364, 1211.631, 0.769]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_107_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_107_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_107_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_107_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_107_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_107_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_107_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_107_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[407.576, 1163.308, 0.729], [407.573, 1163.324, 0.746], [407.57, 1163.34, 0.762], [407.569, 1163.357, 0.779]]\nB: [[387.473, 1137.771, 0.644], [384.287, 1365.683, 0.681], [390.89, 1137.17, 0.617], [457.784, 1284.967, 0.839]]\nC: [[360.164, 1015.983, 0.678], [381.237, 1053.29, 0.859], [457.22, 1360.88, 0.63], [408.603, 1334.048, 0.816]]\nD: [[392.585, 1372.768, 0.686], [426.374, 1363.72, 0.752], [443.3, 955.82, 0.704], [326.364, 1211.631, 0.769]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1183.549, 1091.134, 0.407], [1449.921, 972.434, 0.409], [1115.763, 1023.251, 0.366], [1402.669, 992.802, 0.432]]\nB: [[1243.405, 864.452, 0.467], [1368.17, 1085.65, 0.361], [1076.736, 1221.575, 0.333], [1435.133, 1172.523, 0.468]]\nC: [[1295.125, 1032.757, 0.415], [1295.611, 1033.251, 0.415], [1296.187, 1033.665, 0.415], [1296.747, 1033.991, 0.415]]\nD: [[1335.953, 913.089, 0.398], [1461.39, 864.58, 0.459], [1483.452, 900.406, 0.383], [1264.928, 1038.725, 0.375]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_108_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_108_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_108_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_108_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_108_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_108_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_108_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_108_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1183.549, 1091.134, 0.407], [1449.921, 972.434, 0.409], [1115.763, 1023.251, 0.366], [1402.669, 992.802, 0.432]]\nB: [[1243.405, 864.452, 0.467], [1368.17, 1085.65, 0.361], [1076.736, 1221.575, 0.333], [1435.133, 1172.523, 0.468]]\nC: [[1295.125, 1032.757, 0.415], [1295.611, 1033.251, 0.415], [1296.187, 1033.665, 0.415], [1296.747, 1033.991, 0.415]]\nD: [[1335.953, 913.089, 0.398], [1461.39, 864.58, 0.459], [1483.452, 900.406, 0.383], [1264.928, 1038.725, 0.375]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[339.449, 659.894, 0.573], [339.446, 659.895, 0.607], [339.443, 659.896, 0.64], [339.44, 659.897, 0.674]]\nB: [[398.935, 645.734, 0.599], [298.581, 729.947, 0.634], [401.409, 592.555, 0.67], [389.37, 745.064, 0.776]]\nC: [[317.88, 666.567, 0.669], [318.154, 636.677, 0.526], [319.442, 702.387, 0.7], [331.82, 647.551, 0.682]]\nD: [[348.987, 658.876, 0.645], [370.001, 591.87, 0.551], [346.212, 591.313, 0.75], [291.32, 620.068, 0.565]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_109_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_109_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_109_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_109_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_109_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_109_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_109_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_109_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[339.449, 659.894, 0.573], [339.446, 659.895, 0.607], [339.443, 659.896, 0.64], [339.44, 659.897, 0.674]]\nB: [[398.935, 645.734, 0.599], [298.581, 729.947, 0.634], [401.409, 592.555, 0.67], [389.37, 745.064, 0.776]]\nC: [[317.88, 666.567, 0.669], [318.154, 636.677, 0.526], [319.442, 702.387, 0.7], [331.82, 647.551, 0.682]]\nD: [[348.987, 658.876, 0.645], [370.001, 591.87, 0.551], [346.212, 591.313, 0.75], [291.32, 620.068, 0.565]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1348.14, 1074.93, 0.373], [1537.25, 1072.09, 0.5], [1196.975, 870.732, 0.55], [1269.511, 1097.657, 0.609]]\nB: [[1369.98, 1210.47, 0.291], [1383.17, 1209.316, 0.44], [1098.297, 933.023, 0.49], [1055.724, 1184.093, 0.615]]\nC: [[1279.19, 1030.84, 0.349], [1282.49, 1034.214, 0.43], [1285.285, 1037.189, 0.51], [1288.217, 1040.319, 0.591]]\nD: [[1424.53, 1145.06, 0.417], [1294.63, 1198.674, 0.47], [1368.216, 886.452, 0.51], [1389.846, 1124.768, 0.48]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_110_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_110_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_110_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_110_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_110_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_110_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_110_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_110_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1348.14, 1074.93, 0.373], [1537.25, 1072.09, 0.5], [1196.975, 870.732, 0.55], [1269.511, 1097.657, 0.609]]\nB: [[1369.98, 1210.47, 0.291], [1383.17, 1209.316, 0.44], [1098.297, 933.023, 0.49], [1055.724, 1184.093, 0.615]]\nC: [[1279.19, 1030.84, 0.349], [1282.49, 1034.214, 0.43], [1285.285, 1037.189, 0.51], [1288.217, 1040.319, 0.591]]\nD: [[1424.53, 1145.06, 0.417], [1294.63, 1198.674, 0.47], [1368.216, 886.452, 0.51], [1389.846, 1124.768, 0.48]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1180.699, 1025.35, 0.352], [1360.818, 1139.597, 0.397], [1152.166, 1159.568, 0.296], [1106.717, 1234.187, 0.313]]\nB: [[1378.182, 1100.4, 0.333], [1294.85, 1232.299, 0.398], [1173.547, 969.988, 0.388], [1171.591, 1158.384, 0.396]]\nC: [[1086.537, 1116.193, 0.36], [1109.417, 1116.907, 0.31], [1478.169, 1103.822, 0.341], [1122.704, 957.886, 0.337]]\nD: [[1275.412, 1026.886, 0.336], [1278.054, 1029.742, 0.336], [1280.696, 1032.599, 0.336], [1283.018, 1035.321, 0.336]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_111_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_111_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_111_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_111_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_111_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_111_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_111_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_111_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1180.699, 1025.35, 0.352], [1360.818, 1139.597, 0.397], [1152.166, 1159.568, 0.296], [1106.717, 1234.187, 0.313]]\nB: [[1378.182, 1100.4, 0.333], [1294.85, 1232.299, 0.398], [1173.547, 969.988, 0.388], [1171.591, 1158.384, 0.396]]\nC: [[1086.537, 1116.193, 0.36], [1109.417, 1116.907, 0.31], [1478.169, 1103.822, 0.341], [1122.704, 957.886, 0.337]]\nD: [[1275.412, 1026.886, 0.336], [1278.054, 1029.742, 0.336], [1280.696, 1032.599, 0.336], [1283.018, 1035.321, 0.336]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1567.635, 999.11, 0.216], [1581.604, 1034.59, 0.201], [2215.085, 945.14, 0.266], [1822.791, 878.16, 0.212]]\nB: [[1547.391, 1001.36, 0.216], [1923.868, 772.06, 0.229], [1775.081, 857.84, 0.257], [1976.165, 741.51, 0.198]]\nC: [[1924.297, 873.96, 0.189], [1924.297, 873.96, 0.206], [1924.297, 873.96, 0.223], [1924.297, 873.96, 0.239]]\nD: [[1739.859, 831.14, 0.169], [1930.99, 1015.96, 0.221], [1891.889, 1021.13, 0.241], [2233.369, 854.3, 0.277]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_112_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_112_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_112_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_112_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_112_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_112_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_112_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_112_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1567.635, 999.11, 0.216], [1581.604, 1034.59, 0.201], [2215.085, 945.14, 0.266], [1822.791, 878.16, 0.212]]\nB: [[1547.391, 1001.36, 0.216], [1923.868, 772.06, 0.229], [1775.081, 857.84, 0.257], [1976.165, 741.51, 0.198]]\nC: [[1924.297, 873.96, 0.189], [1924.297, 873.96, 0.206], [1924.297, 873.96, 0.223], [1924.297, 873.96, 0.239]]\nD: [[1739.859, 831.14, 0.169], [1930.99, 1015.96, 0.221], [1891.889, 1021.13, 0.241], [2233.369, 854.3, 0.277]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[389.455, 1221.754, 1.957], [389.017, 1221.738, 1.957], [388.246, 1221.696, 2.082], [387.407, 1221.65, 2.007]]\nB: [[430.695, 1244.614, 1.598], [332.727, 1219.984, 2.062], [451.568, 1172.545, 2.304], [431.932, 1447.12, 2.075]]\nC: [[434.759, 1360.34, 1.798], [320.818, 1065.151, 2.275], [403.374, 995.774, 1.782], [399.338, 1318.27, 2.25]]\nD: [[338.306, 1065.478, 2.175], [359.176, 1170.276, 2.145], [422.221, 1295.741, 2.146], [318.234, 1189.1, 1.616]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_113_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_113_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_113_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_113_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_113_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_113_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_113_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_113_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[389.455, 1221.754, 1.957], [389.017, 1221.738, 1.957], [388.246, 1221.696, 2.082], [387.407, 1221.65, 2.007]]\nB: [[430.695, 1244.614, 1.598], [332.727, 1219.984, 2.062], [451.568, 1172.545, 2.304], [431.932, 1447.12, 2.075]]\nC: [[434.759, 1360.34, 1.798], [320.818, 1065.151, 2.275], [403.374, 995.774, 1.782], [399.338, 1318.27, 2.25]]\nD: [[338.306, 1065.478, 2.175], [359.176, 1170.276, 2.145], [422.221, 1295.741, 2.146], [318.234, 1189.1, 1.616]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[236.0, 747.95, 0.854], [256.234, 709.537, 0.697], [304.474, 800.037, 0.56], [331.877, 561.326, 0.514]]\nB: [[314.28, 598.25, 0.849], [236.191, 740.031, 0.728], [250.825, 688.597, 0.635], [341.366, 613.896, 0.475]]\nC: [[246.64, 693.8, 0.858], [290.257, 567.854, 0.783], [293.745, 750.544, 0.62], [309.807, 562.559, 0.531]]\nD: [[289.28, 669.01, 0.775], [291.627, 672.377, 0.668], [293.977, 675.748, 0.562], [296.214, 678.956, 0.455]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_114_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_114_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_114_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_114_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_114_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_114_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_114_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_114_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[236.0, 747.95, 0.854], [256.234, 709.537, 0.697], [304.474, 800.037, 0.56], [331.877, 561.326, 0.514]]\nB: [[314.28, 598.25, 0.849], [236.191, 740.031, 0.728], [250.825, 688.597, 0.635], [341.366, 613.896, 0.475]]\nC: [[246.64, 693.8, 0.858], [290.257, 567.854, 0.783], [293.745, 750.544, 0.62], [309.807, 562.559, 0.531]]\nD: [[289.28, 669.01, 0.775], [291.627, 672.377, 0.668], [293.977, 675.748, 0.562], [296.214, 678.956, 0.455]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[533.8, 1735.728, -0.009], [518.05, 1383.369, 0.045], [743.409, 1439.406, 0.374], [553.203, 1712.789, 0.68]]\nB: [[653.646, 1831.884, -0.01], [745.339, 1445.929, 0.044], [684.645, 1812.914, 0.333], [569.065, 1458.696, 0.754]]\nC: [[572.656, 1841.565, -0.01], [747.719, 1494.494, 0.038], [688.766, 1558.475, 0.402], [740.666, 1414.102, 0.689]]\nD: [[637.791, 1636.674, -0.011], [637.381, 1637.067, 0.039], [636.158, 1638.241, 0.389], [635.756, 1638.659, 0.689]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_115_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_115_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_115_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_115_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_115_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_115_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_115_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_115_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[533.8, 1735.728, -0.009], [518.05, 1383.369, 0.045], [743.409, 1439.406, 0.374], [553.203, 1712.789, 0.68]]\nB: [[653.646, 1831.884, -0.01], [745.339, 1445.929, 0.044], [684.645, 1812.914, 0.333], [569.065, 1458.696, 0.754]]\nC: [[572.656, 1841.565, -0.01], [747.719, 1494.494, 0.038], [688.766, 1558.475, 0.402], [740.666, 1414.102, 0.689]]\nD: [[637.791, 1636.674, -0.011], [637.381, 1637.067, 0.039], [636.158, 1638.241, 0.389], [635.756, 1638.659, 0.689]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[357.199, 1105.26, 0.925], [357.199, 1105.26, 0.874], [357.199, 1105.26, 0.901], [357.199, 1105.26, 1.083]]\nB: [[405.104, 1231.8, 0.941], [321.418, 916.12, 1.011], [382.371, 913.36, 0.794], [428.391, 1299.88, 1.177]]\nC: [[352.491, 1140.82, 0.829], [377.607, 964.69, 0.939], [341.493, 1094.81, 0.997], [329.979, 894.62, 0.879]]\nD: [[368.993, 920.78, 0.953], [328.671, 1054.8, 1.001], [426.057, 1241.84, 0.874], [319.642, 1019.55, 1.122]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_116_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_116_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_116_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_116_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_116_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_116_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_116_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_116_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[357.199, 1105.26, 0.925], [357.199, 1105.26, 0.874], [357.199, 1105.26, 0.901], [357.199, 1105.26, 1.083]]\nB: [[405.104, 1231.8, 0.941], [321.418, 916.12, 1.011], [382.371, 913.36, 0.794], [428.391, 1299.88, 1.177]]\nC: [[352.491, 1140.82, 0.829], [377.607, 964.69, 0.939], [341.493, 1094.81, 0.997], [329.979, 894.62, 0.879]]\nD: [[368.993, 920.78, 0.953], [328.671, 1054.8, 1.001], [426.057, 1241.84, 0.874], [319.642, 1019.55, 1.122]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[390.292, 1280.196, 0.538], [390.276, 1322.072, 0.606], [337.427, 1327.052, 0.501], [388.832, 1049.397, 0.585]]\nB: [[441.456, 1017.908, 0.543], [440.359, 1177.164, 0.491], [371.894, 1012.041, 0.514], [347.529, 1236.907, 0.616]]\nC: [[398.584, 1179.211, 0.555], [316.744, 1033.547, 0.547], [377.513, 1090.27, 0.445], [332.149, 1080.471, 0.473]]\nD: [[393.298, 1155.018, 0.485], [393.298, 1155.017, 0.514], [393.298, 1155.016, 0.542], [393.297, 1155.015, 0.571]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_117_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_117_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_117_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_117_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_117_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_117_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_117_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_117_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[390.292, 1280.196, 0.538], [390.276, 1322.072, 0.606], [337.427, 1327.052, 0.501], [388.832, 1049.397, 0.585]]\nB: [[441.456, 1017.908, 0.543], [440.359, 1177.164, 0.491], [371.894, 1012.041, 0.514], [347.529, 1236.907, 0.616]]\nC: [[398.584, 1179.211, 0.555], [316.744, 1033.547, 0.547], [377.513, 1090.27, 0.445], [332.149, 1080.471, 0.473]]\nD: [[393.298, 1155.018, 0.485], [393.298, 1155.017, 0.514], [393.298, 1155.016, 0.542], [393.297, 1155.015, 0.571]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[436.159, 952.778, 0.85], [380.614, 1063.82, 0.774], [459.351, 1286.857, 0.672], [356.13, 1196.862, 0.73]]\nB: [[393.174, 1367.574, 0.683], [466.835, 1298.26, 0.635], [356.883, 1226.503, 0.681], [446.634, 1121.248, 0.813]]\nC: [[399.863, 1143.574, 0.738], [398.996, 1141.132, 0.738], [398.116, 1138.632, 0.738], [397.624, 1136.322, 0.738]]\nD: [[344.514, 1172.922, 0.671], [413.852, 1079.671, 0.613], [361.577, 1132.234, 0.863], [334.055, 1043.733, 0.866]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_118_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_118_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_118_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_118_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_118_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_118_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_118_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_118_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[436.159, 952.778, 0.85], [380.614, 1063.82, 0.774], [459.351, 1286.857, 0.672], [356.13, 1196.862, 0.73]]\nB: [[393.174, 1367.574, 0.683], [466.835, 1298.26, 0.635], [356.883, 1226.503, 0.681], [446.634, 1121.248, 0.813]]\nC: [[399.863, 1143.574, 0.738], [398.996, 1141.132, 0.738], [398.116, 1138.632, 0.738], [397.624, 1136.322, 0.738]]\nD: [[344.514, 1172.922, 0.671], [413.852, 1079.671, 0.613], [361.577, 1132.234, 0.863], [334.055, 1043.733, 0.866]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[379.59, 1225.256, 1.8], [411.498, 1212.145, 1.432], [439.173, 1051.415, 1.512], [378.757, 1170.606, 1.817]]\nB: [[385.93, 1201.138, 1.613], [385.521, 1201.641, 1.663], [384.966, 1202.306, 1.763], [384.443, 1202.903, 1.763]]\nC: [[447.45, 996.224, 1.641], [321.511, 1225.058, 1.654], [320.686, 1029.24, 1.737], [312.326, 1161.223, 1.53]]\nD: [[395.57, 1047.807, 1.499], [340.373, 1260.222, 1.497], [439.995, 1104.894, 1.86], [369.975, 1070.189, 1.508]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_119_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_119_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_119_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_119_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_119_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_119_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_119_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_119_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[379.59, 1225.256, 1.8], [411.498, 1212.145, 1.432], [439.173, 1051.415, 1.512], [378.757, 1170.606, 1.817]]\nB: [[385.93, 1201.138, 1.613], [385.521, 1201.641, 1.663], [384.966, 1202.306, 1.763], [384.443, 1202.903, 1.763]]\nC: [[447.45, 996.224, 1.641], [321.511, 1225.058, 1.654], [320.686, 1029.24, 1.737], [312.326, 1161.223, 1.53]]\nD: [[395.57, 1047.807, 1.499], [340.373, 1260.222, 1.497], [439.995, 1104.894, 1.86], [369.975, 1070.189, 1.508]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2078.323, 958.565, 0.161], [1559.705, 959.242, 0.151], [2197.62, 763.907, 0.152], [1804.38, 767.638, 0.197]]\nB: [[2138.05, 913.396, 0.132], [2178.71, 1013.596, 0.176], [1703.36, 733.089, 0.191], [1798.969, 734.79, 0.2]]\nC: [[1835.477, 1025.448, 0.137], [1705.515, 1015.928, 0.153], [1802.73, 873.747, 0.182], [1809.594, 823.179, 0.19]]\nD: [[1926.648, 875.886, 0.141], [1926.639, 875.864, 0.154], [1926.63, 875.841, 0.166], [1926.627, 875.833, 0.179]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_120_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_120_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_120_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_120_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_120_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_120_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_120_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_120_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2078.323, 958.565, 0.161], [1559.705, 959.242, 0.151], [2197.62, 763.907, 0.152], [1804.38, 767.638, 0.197]]\nB: [[2138.05, 913.396, 0.132], [2178.71, 1013.596, 0.176], [1703.36, 733.089, 0.191], [1798.969, 734.79, 0.2]]\nC: [[1835.477, 1025.448, 0.137], [1705.515, 1015.928, 0.153], [1802.73, 873.747, 0.182], [1809.594, 823.179, 0.19]]\nD: [[1926.648, 875.886, 0.141], [1926.639, 875.864, 0.154], [1926.63, 875.841, 0.166], [1926.627, 875.833, 0.179]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1640.64, 2308.693, 0.719], [2017.236, 2113.119, 0.967], [1994.76, 2513.033, 1.14], [1810.618, 2225.686, 1.546]]\nB: [[1973.19, 2747.385, 0.918], [1843.455, 2503.49, 1.052], [1630.78, 2460.524, 1.38], [1987.593, 2630.677, 1.375]]\nC: [[1576.98, 2536.869, 0.66], [2075.147, 2055.992, 1.144], [1827.84, 2639.901, 1.45], [2070.073, 2767.351, 1.167]]\nD: [[1866.27, 2481.021, 0.817], [1865.675, 2481.739, 1.031], [1865.18, 2482.337, 1.21], [1864.684, 2482.936, 1.389]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_121_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_121_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_121_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_121_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_121_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_121_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_121_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_121_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1640.64, 2308.693, 0.719], [2017.236, 2113.119, 0.967], [1994.76, 2513.033, 1.14], [1810.618, 2225.686, 1.546]]\nB: [[1973.19, 2747.385, 0.918], [1843.455, 2503.49, 1.052], [1630.78, 2460.524, 1.38], [1987.593, 2630.677, 1.375]]\nC: [[1576.98, 2536.869, 0.66], [2075.147, 2055.992, 1.144], [1827.84, 2639.901, 1.45], [2070.073, 2767.351, 1.167]]\nD: [[1866.27, 2481.021, 0.817], [1865.675, 2481.739, 1.031], [1865.18, 2482.337, 1.21], [1864.684, 2482.936, 1.389]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1323.534, 1070.968, 0.039], [1242.872, 853.737, 0.042], [1492.766, 1030.756, 0.057], [1309.345, 1096.471, 0.046]]\nB: [[1556.573, 856.346, 0.057], [1108.062, 1183.213, 0.047], [1303.053, 903.29, 0.05], [1529.898, 1191.182, 0.045]]\nC: [[1351.279, 1022.468, 0.048], [1351.279, 1022.468, 0.048], [1351.279, 1022.468, 0.048], [1351.279, 1022.468, 0.048]]\nD: [[1160.276, 1004.554, 0.048], [1454.931, 1040.919, 0.039], [1167.504, 987.892, 0.046], [1457.735, 818.113, 0.042]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_122_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_122_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_122_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_122_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_122_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_122_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_122_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_122_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1323.534, 1070.968, 0.039], [1242.872, 853.737, 0.042], [1492.766, 1030.756, 0.057], [1309.345, 1096.471, 0.046]]\nB: [[1556.573, 856.346, 0.057], [1108.062, 1183.213, 0.047], [1303.053, 903.29, 0.05], [1529.898, 1191.182, 0.045]]\nC: [[1351.279, 1022.468, 0.048], [1351.279, 1022.468, 0.048], [1351.279, 1022.468, 0.048], [1351.279, 1022.468, 0.048]]\nD: [[1160.276, 1004.554, 0.048], [1454.931, 1040.919, 0.039], [1167.504, 987.892, 0.046], [1457.735, 818.113, 0.042]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[398.992, 1279.087, 0.118], [456.215, 1152.193, 0.133], [407.776, 1279.429, 0.095], [527.113, 1246.616, 0.136]]\nB: [[448.696, 1090.248, 0.117], [448.695, 1090.246, 0.117], [448.686, 1090.224, 0.115], [448.685, 1090.222, 0.114]]\nC: [[435.77, 875.144, 0.115], [440.962, 1303.479, 0.129], [413.225, 1290.42, 0.105], [511.071, 1036.309, 0.122]]\nD: [[438.596, 955.475, 0.137], [464.97, 1295.34, 0.118], [386.42, 1095.841, 0.125], [437.592, 1200.522, 0.127]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_123_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_123_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_123_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_123_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_123_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_123_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_123_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_123_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[398.992, 1279.087, 0.118], [456.215, 1152.193, 0.133], [407.776, 1279.429, 0.095], [527.113, 1246.616, 0.136]]\nB: [[448.696, 1090.248, 0.117], [448.695, 1090.246, 0.117], [448.686, 1090.224, 0.115], [448.685, 1090.222, 0.114]]\nC: [[435.77, 875.144, 0.115], [440.962, 1303.479, 0.129], [413.225, 1290.42, 0.105], [511.071, 1036.309, 0.122]]\nD: [[438.596, 955.475, 0.137], [464.97, 1295.34, 0.118], [386.42, 1095.841, 0.125], [437.592, 1200.522, 0.127]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1835.491, 834.002, 0.48], [2125.794, 969.393, 0.677], [1614.306, 1027.608, 0.583], [1983.247, 970.603, 0.541]]\nB: [[1651.575, 740.269, 0.56], [1919.293, 887.545, 0.629], [1867.876, 908.887, 0.565], [1937.748, 943.609, 0.511]]\nC: [[1784.634, 874.597, 0.596], [1784.597, 874.576, 0.596], [1784.564, 874.558, 0.596], [1784.764, 874.582, 0.596]]\nD: [[1674.888, 950.802, 0.589], [2065.024, 902.619, 0.528], [2130.173, 1019.966, 0.552], [2067.829, 931.775, 0.63]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_124_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_124_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_124_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_124_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_124_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_124_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_124_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_124_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1835.491, 834.002, 0.48], [2125.794, 969.393, 0.677], [1614.306, 1027.608, 0.583], [1983.247, 970.603, 0.541]]\nB: [[1651.575, 740.269, 0.56], [1919.293, 887.545, 0.629], [1867.876, 908.887, 0.565], [1937.748, 943.609, 0.511]]\nC: [[1784.634, 874.597, 0.596], [1784.597, 874.576, 0.596], [1784.564, 874.558, 0.596], [1784.764, 874.582, 0.596]]\nD: [[1674.888, 950.802, 0.589], [2065.024, 902.619, 0.528], [2130.173, 1019.966, 0.552], [2067.829, 931.775, 0.63]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[276.129, 765.803, 0.706], [239.259, 629.205, 0.8], [265.678, 619.0, 0.624], [272.857, 563.448, 0.52]]\nB: [[267.858, 571.717, 0.774], [277.532, 772.037, 0.819], [265.677, 626.589, 0.69], [303.026, 678.599, 0.635]]\nC: [[307.434, 646.641, 0.793], [279.193, 720.372, 0.75], [342.062, 733.991, 0.756], [275.316, 788.349, 0.594]]\nD: [[287.863, 668.522, 0.723], [289.106, 670.134, 0.687], [290.511, 671.955, 0.702], [292.583, 674.718, 0.593]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_125_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_125_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_125_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_125_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_125_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_125_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_125_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_125_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[276.129, 765.803, 0.706], [239.259, 629.205, 0.8], [265.678, 619.0, 0.624], [272.857, 563.448, 0.52]]\nB: [[267.858, 571.717, 0.774], [277.532, 772.037, 0.819], [265.677, 626.589, 0.69], [303.026, 678.599, 0.635]]\nC: [[307.434, 646.641, 0.793], [279.193, 720.372, 0.75], [342.062, 733.991, 0.756], [275.316, 788.349, 0.594]]\nD: [[287.863, 668.522, 0.723], [289.106, 670.134, 0.687], [290.511, 671.955, 0.702], [292.583, 674.718, 0.593]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[622.35, 1624.018, -0.016], [621.599, 1624.59, 0.068], [620.967, 1625.056, 0.201], [620.312, 1625.598, 0.284]]\nB: [[715.37, 1646.519, -0.015], [571.805, 1818.41, 0.061], [556.199, 1828.057, 0.206], [719.663, 1568.216, 0.242]]\nC: [[619.74, 1639.913, -0.014], [696.763, 1306.55, 0.056], [512.97, 1560.484, 0.186], [567.584, 1424.02, 0.237]]\nD: [[676.88, 1409.501, -0.016], [537.018, 1735.64, 0.057], [546.621, 1339.978, 0.22], [568.0, 1888.129, 0.245]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_126_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_126_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_126_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_126_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_126_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_126_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_126_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_126_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[622.35, 1624.018, -0.016], [621.599, 1624.59, 0.068], [620.967, 1625.056, 0.201], [620.312, 1625.598, 0.284]]\nB: [[715.37, 1646.519, -0.015], [571.805, 1818.41, 0.061], [556.199, 1828.057, 0.206], [719.663, 1568.216, 0.242]]\nC: [[619.74, 1639.913, -0.014], [696.763, 1306.55, 0.056], [512.97, 1560.484, 0.186], [567.584, 1424.02, 0.237]]\nD: [[676.88, 1409.501, -0.016], [537.018, 1735.64, 0.057], [546.621, 1339.978, 0.22], [568.0, 1888.129, 0.245]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[627.008, 1617.877, -0.387], [626.7, 1618.657, -0.137], [626.332, 1619.431, 0.163], [626.034, 1619.837, 0.363]]\nB: [[514.156, 1782.736, -0.333], [575.6, 1636.318, -0.131], [743.355, 1576.589, 0.179], [505.541, 1559.477, 0.32]]\nC: [[712.578, 1866.613, -0.344], [558.1, 1427.09, -0.154], [677.337, 1665.044, 0.133], [550.249, 1826.976, 0.376]]\nD: [[618.87, 1499.776, -0.427], [647.6, 1861.481, -0.148], [699.281, 1872.065, 0.164], [640.722, 1817.452, 0.342]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_127_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_127_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_127_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_127_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_127_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_127_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_127_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_127_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[627.008, 1617.877, -0.387], [626.7, 1618.657, -0.137], [626.332, 1619.431, 0.163], [626.034, 1619.837, 0.363]]\nB: [[514.156, 1782.736, -0.333], [575.6, 1636.318, -0.131], [743.355, 1576.589, 0.179], [505.541, 1559.477, 0.32]]\nC: [[712.578, 1866.613, -0.344], [558.1, 1427.09, -0.154], [677.337, 1665.044, 0.133], [550.249, 1826.976, 0.376]]\nD: [[618.87, 1499.776, -0.427], [647.6, 1861.481, -0.148], [699.281, 1872.065, 0.164], [640.722, 1817.452, 0.342]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[353.535, 1093.572, 0.8], [325.884, 1175.321, 0.708], [298.264, 1160.539, 1.06], [403.515, 1196.443, 0.896]]\nB: [[298.277, 1279.808, 0.8], [334.872, 990.495, 0.719], [317.907, 1145.582, 0.785], [428.226, 1134.096, 1.096]]\nC: [[361.234, 1127.159, 0.743], [361.244, 1127.193, 0.761], [361.254, 1127.227, 0.979], [361.252, 1127.231, 1.019]]\nD: [[351.808, 979.748, 0.733], [299.091, 972.477, 0.91], [422.591, 1328.277, 1.109], [373.924, 1003.202, 0.826]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_128_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_128_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_128_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_128_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_128_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_128_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_128_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_128_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[353.535, 1093.572, 0.8], [325.884, 1175.321, 0.708], [298.264, 1160.539, 1.06], [403.515, 1196.443, 0.896]]\nB: [[298.277, 1279.808, 0.8], [334.872, 990.495, 0.719], [317.907, 1145.582, 0.785], [428.226, 1134.096, 1.096]]\nC: [[361.234, 1127.159, 0.743], [361.244, 1127.193, 0.761], [361.254, 1127.227, 0.979], [361.252, 1127.231, 1.019]]\nD: [[351.808, 979.748, 0.733], [299.091, 972.477, 0.91], [422.591, 1328.277, 1.109], [373.924, 1003.202, 0.826]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[467.084, 1013.697, 1.484], [410.637, 1087.555, 1.47], [503.896, 994.189, 1.833], [375.598, 1172.16, 1.996]]\nB: [[403.645, 1031.052, 1.679], [463.451, 943.028, 1.451], [499.44, 1242.514, 2.094], [468.957, 1220.61, 2.107]]\nC: [[479.961, 1178.515, 1.846], [421.912, 1195.377, 1.945], [395.807, 904.258, 1.58], [479.041, 963.66, 1.573]]\nD: [[443.949, 1116.592, 1.729], [443.607, 1116.621, 1.729], [442.518, 1116.448, 1.879], [442.143, 1116.34, 1.929]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_129_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_129_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_129_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_129_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_129_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_129_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_129_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_129_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[467.084, 1013.697, 1.484], [410.637, 1087.555, 1.47], [503.896, 994.189, 1.833], [375.598, 1172.16, 1.996]]\nB: [[403.645, 1031.052, 1.679], [463.451, 943.028, 1.451], [499.44, 1242.514, 2.094], [468.957, 1220.61, 2.107]]\nC: [[479.961, 1178.515, 1.846], [421.912, 1195.377, 1.945], [395.807, 904.258, 1.58], [479.041, 963.66, 1.573]]\nD: [[443.949, 1116.592, 1.729], [443.607, 1116.621, 1.729], [442.518, 1116.448, 1.879], [442.143, 1116.34, 1.929]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1044.891, 1237.212, 0.684], [1071.2, 1248.461, 0.639], [1210.008, 933.973, 0.707], [1328.735, 877.082, 0.748]]\nB: [[1117.371, 1205.206, 0.822], [1089.2, 940.984, 0.629], [1072.282, 905.107, 0.824], [1173.176, 946.517, 0.885]]\nC: [[1227.559, 936.208, 0.663], [1471.6, 1143.386, 0.863], [1177.563, 842.525, 0.712], [1310.648, 1103.801, 0.83]]\nD: [[1267.451, 1047.078, 0.822], [1266.5, 1047.564, 0.754], [1265.609, 1047.998, 0.762], [1257.032, 1054.386, 0.759]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_130_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_130_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_130_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_130_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_130_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_130_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_130_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_130_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1044.891, 1237.212, 0.684], [1071.2, 1248.461, 0.639], [1210.008, 933.973, 0.707], [1328.735, 877.082, 0.748]]\nB: [[1117.371, 1205.206, 0.822], [1089.2, 940.984, 0.629], [1072.282, 905.107, 0.824], [1173.176, 946.517, 0.885]]\nC: [[1227.559, 936.208, 0.663], [1471.6, 1143.386, 0.863], [1177.563, 842.525, 0.712], [1310.648, 1103.801, 0.83]]\nD: [[1267.451, 1047.078, 0.822], [1266.5, 1047.564, 0.754], [1265.609, 1047.998, 0.762], [1257.032, 1054.386, 0.759]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1275.825, 1026.459, 0.275], [1278.063, 1029.09, 0.375], [1280.981, 1032.367, 0.325], [1283.902, 1035.648, 0.374]]\nB: [[1030.171, 1170.182, 0.27], [1123.271, 830.3, 0.339], [1164.96, 938.971, 0.375], [1317.327, 864.217, 0.318]]\nC: [[1058.02, 1197.606, 0.255], [1412.723, 1041.94, 0.385], [1413.334, 1081.562, 0.344], [1284.333, 1092.197, 0.438]]\nD: [[1469.903, 1189.502, 0.313], [1314.332, 1032.81, 0.399], [1118.592, 1102.621, 0.281], [1269.138, 1091.852, 0.359]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_131_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_131_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_131_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_131_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_131_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_131_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_131_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_131_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1275.825, 1026.459, 0.275], [1278.063, 1029.09, 0.375], [1280.981, 1032.367, 0.325], [1283.902, 1035.648, 0.374]]\nB: [[1030.171, 1170.182, 0.27], [1123.271, 830.3, 0.339], [1164.96, 938.971, 0.375], [1317.327, 864.217, 0.318]]\nC: [[1058.02, 1197.606, 0.255], [1412.723, 1041.94, 0.385], [1413.334, 1081.562, 0.344], [1284.333, 1092.197, 0.438]]\nD: [[1469.903, 1189.502, 0.313], [1314.332, 1032.81, 0.399], [1118.592, 1102.621, 0.281], [1269.138, 1091.852, 0.359]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[421.972, 1212.966, 0.431], [433.3, 1273.552, 0.507], [464.634, 1128.046, 0.702], [349.601, 1228.731, 0.578]]\nB: [[400.984, 1376.129, 0.581], [391.113, 1165.646, 0.7], [457.469, 1280.832, 0.616], [442.522, 1062.927, 0.701]]\nC: [[449.392, 986.304, 0.601], [473.649, 1081.286, 0.52], [358.026, 1320.626, 0.568], [395.395, 1377.932, 0.573]]\nD: [[399.773, 1169.799, 0.536], [399.773, 1169.799, 0.586], [399.773, 1169.799, 0.636], [399.773, 1169.799, 0.681]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_132_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_132_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_132_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_132_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_132_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_132_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_132_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_132_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[421.972, 1212.966, 0.431], [433.3, 1273.552, 0.507], [464.634, 1128.046, 0.702], [349.601, 1228.731, 0.578]]\nB: [[400.984, 1376.129, 0.581], [391.113, 1165.646, 0.7], [457.469, 1280.832, 0.616], [442.522, 1062.927, 0.701]]\nC: [[449.392, 986.304, 0.601], [473.649, 1081.286, 0.52], [358.026, 1320.626, 0.568], [395.395, 1377.932, 0.573]]\nD: [[399.773, 1169.799, 0.536], [399.773, 1169.799, 0.586], [399.773, 1169.799, 0.636], [399.773, 1169.799, 0.681]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[417.658, 1171.585, 1.02], [344.402, 1146.41, 1.185], [367.351, 1178.349, 1.028], [487.83, 1043.697, 0.98]]\nB: [[445.605, 1419.113, 0.98], [352.2, 1288.12, 1.051], [459.196, 983.633, 1.107], [368.22, 1292.263, 1.32]]\nC: [[450.048, 1344.51, 1.16], [403.323, 1079.3, 1.248], [335.599, 1292.674, 1.335], [385.64, 1056.834, 1.11]]\nD: [[419.296, 1191.476, 1.11], [418.846, 1191.58, 1.143], [418.293, 1191.727, 1.176], [417.52, 1191.939, 1.21]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_133_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_133_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_133_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_133_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_133_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_133_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_133_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_133_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[417.658, 1171.585, 1.02], [344.402, 1146.41, 1.185], [367.351, 1178.349, 1.028], [487.83, 1043.697, 0.98]]\nB: [[445.605, 1419.113, 0.98], [352.2, 1288.12, 1.051], [459.196, 983.633, 1.107], [368.22, 1292.263, 1.32]]\nC: [[450.048, 1344.51, 1.16], [403.323, 1079.3, 1.248], [335.599, 1292.674, 1.335], [385.64, 1056.834, 1.11]]\nD: [[419.296, 1191.476, 1.11], [418.846, 1191.58, 1.143], [418.293, 1191.727, 1.176], [417.52, 1191.939, 1.21]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[364.455, 946.84, 0.64], [334.117, 1088.62, 0.558], [343.529, 1215.1, 0.571], [332.561, 991.64, 0.55]]\nB: [[398.222, 1166.03, 0.56], [398.222, 1166.03, 0.577], [398.222, 1166.03, 0.594], [398.222, 1166.03, 0.61]]\nC: [[452.892, 1109.11, 0.63], [362.882, 1081.56, 0.574], [328.005, 1052.37, 0.65], [326.765, 997.91, 0.68]]\nD: [[389.913, 1383.18, 0.51], [334.65, 1310.36, 0.682], [445.091, 1036.45, 0.591], [404.94, 1152.47, 0.57]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_134_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_134_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_134_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_134_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_134_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_134_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_134_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_134_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[364.455, 946.84, 0.64], [334.117, 1088.62, 0.558], [343.529, 1215.1, 0.571], [332.561, 991.64, 0.55]]\nB: [[398.222, 1166.03, 0.56], [398.222, 1166.03, 0.577], [398.222, 1166.03, 0.594], [398.222, 1166.03, 0.61]]\nC: [[452.892, 1109.11, 0.63], [362.882, 1081.56, 0.574], [328.005, 1052.37, 0.65], [326.765, 997.91, 0.68]]\nD: [[389.913, 1383.18, 0.51], [334.65, 1310.36, 0.682], [445.091, 1036.45, 0.591], [404.94, 1152.47, 0.57]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[627.271, 1619.557, -0.161], [627.006, 1619.87, -0.051], [626.747, 1620.187, 0.11], [626.52, 1620.528, 0.17]]\nB: [[569.336, 1657.23, -0.136], [526.963, 1384.47, -0.061], [669.247, 1891.64, 0.11], [671.16, 1857.428, 0.15]]\nC: [[684.005, 1527.275, -0.146], [739.824, 1494.52, -0.06], [521.003, 1884.978, 0.09], [553.11, 1840.593, 0.19]]\nD: [[532.728, 1841.748, -0.144], [536.854, 1368.26, -0.059], [622.506, 1400.948, 0.12], [562.38, 1942.023, 0.18]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_135_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_135_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_135_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_135_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_135_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_135_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_135_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_135_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[627.271, 1619.557, -0.161], [627.006, 1619.87, -0.051], [626.747, 1620.187, 0.11], [626.52, 1620.528, 0.17]]\nB: [[569.336, 1657.23, -0.136], [526.963, 1384.47, -0.061], [669.247, 1891.64, 0.11], [671.16, 1857.428, 0.15]]\nC: [[684.005, 1527.275, -0.146], [739.824, 1494.52, -0.06], [521.003, 1884.978, 0.09], [553.11, 1840.593, 0.19]]\nD: [[532.728, 1841.748, -0.144], [536.854, 1368.26, -0.059], [622.506, 1400.948, 0.12], [562.38, 1942.023, 0.18]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1276.426, 1070.932, 0.876], [1276.425, 1070.932, 0.877], [1276.424, 1070.932, 0.878], [1276.423, 1070.932, 0.879]]\nB: [[1298.613, 1048.861, 0.963], [1211.744, 1284.0, 0.977], [1133.349, 1252.098, 0.958], [1442.465, 1081.694, 0.942]]\nC: [[1136.57, 1184.933, 0.959], [1263.407, 1137.283, 0.74], [1237.716, 1079.234, 0.996], [1254.286, 1092.816, 1.0]]\nD: [[1156.908, 984.436, 0.862], [1293.574, 1008.462, 0.755], [1072.394, 1109.853, 0.763], [1158.181, 1086.592, 0.975]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_136_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_136_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_136_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_136_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_136_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_136_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_136_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_136_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1276.426, 1070.932, 0.876], [1276.425, 1070.932, 0.877], [1276.424, 1070.932, 0.878], [1276.423, 1070.932, 0.879]]\nB: [[1298.613, 1048.861, 0.963], [1211.744, 1284.0, 0.977], [1133.349, 1252.098, 0.958], [1442.465, 1081.694, 0.942]]\nC: [[1136.57, 1184.933, 0.959], [1263.407, 1137.283, 0.74], [1237.716, 1079.234, 0.996], [1254.286, 1092.816, 1.0]]\nD: [[1156.908, 984.436, 0.862], [1293.574, 1008.462, 0.755], [1072.394, 1109.853, 0.763], [1158.181, 1086.592, 0.975]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1863.527, 866.104, 1.229], [1863.553, 866.65, 1.085], [1863.585, 867.332, 1.016], [1863.611, 868.023, 1.0]]\nB: [[1741.116, 973.52, 1.473], [1586.126, 927.91, 1.219], [1837.83, 816.557, 1.029], [1765.354, 1012.863, 0.8]]\nC: [[2109.608, 749.973, 1.352], [2151.463, 723.35, 1.155], [2081.946, 774.988, 1.039], [1584.067, 819.061, 1.0]]\nD: [[1619.868, 705.702, 1.426], [2015.736, 882.4, 1.072], [1611.742, 1030.157, 1.001], [1809.71, 882.281, 0.9]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_137_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_137_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_137_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_137_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_137_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_137_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_137_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_137_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1863.527, 866.104, 1.229], [1863.553, 866.65, 1.085], [1863.585, 867.332, 1.016], [1863.611, 868.023, 1.0]]\nB: [[1741.116, 973.52, 1.473], [1586.126, 927.91, 1.219], [1837.83, 816.557, 1.029], [1765.354, 1012.863, 0.8]]\nC: [[2109.608, 749.973, 1.352], [2151.463, 723.35, 1.155], [2081.946, 774.988, 1.039], [1584.067, 819.061, 1.0]]\nD: [[1619.868, 705.702, 1.426], [2015.736, 882.4, 1.072], [1611.742, 1030.157, 1.001], [1809.71, 882.281, 0.9]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[370.828, 1093.568, 0.573], [438.229, 1239.617, 0.497], [355.298, 1015.971, 0.498], [469.743, 1216.196, 0.622]]\nB: [[334.534, 1298.472, 0.487], [330.759, 1369.516, 0.441], [394.543, 1079.174, 0.619], [471.577, 1146.247, 0.639]]\nC: [[394.842, 1158.711, 0.487], [394.842, 1158.711, 0.521], [394.842, 1158.711, 0.554], [394.842, 1158.711, 0.587]]\nD: [[370.596, 976.597, 0.509], [364.598, 996.341, 0.435], [427.969, 1274.101, 0.549], [391.146, 1206.744, 0.606]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_138_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_138_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_138_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_138_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_138_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_138_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_138_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_138_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[370.828, 1093.568, 0.573], [438.229, 1239.617, 0.497], [355.298, 1015.971, 0.498], [469.743, 1216.196, 0.622]]\nB: [[334.534, 1298.472, 0.487], [330.759, 1369.516, 0.441], [394.543, 1079.174, 0.619], [471.577, 1146.247, 0.639]]\nC: [[394.842, 1158.711, 0.487], [394.842, 1158.711, 0.521], [394.842, 1158.711, 0.554], [394.842, 1158.711, 0.587]]\nD: [[370.596, 976.597, 0.509], [364.598, 996.341, 0.435], [427.969, 1274.101, 0.549], [391.146, 1206.744, 0.606]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[350.427, 1144.305, 0.623], [349.868, 1144.535, 0.69], [349.308, 1144.766, 0.756], [348.749, 1144.996, 0.823]]\nB: [[364.104, 961.597, 0.533], [321.289, 1034.564, 0.81], [289.738, 1178.466, 0.654], [369.278, 927.402, 0.746]]\nC: [[301.407, 1085.027, 0.561], [353.922, 1230.167, 0.74], [385.078, 1056.365, 0.831], [353.967, 1321.653, 0.933]]\nD: [[332.499, 1323.247, 0.603], [328.44, 1217.95, 0.71], [304.408, 1248.393, 0.704], [312.725, 1041.977, 0.788]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_139_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_139_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_139_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_139_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_139_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_139_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_139_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_139_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[350.427, 1144.305, 0.623], [349.868, 1144.535, 0.69], [349.308, 1144.766, 0.756], [348.749, 1144.996, 0.823]]\nB: [[364.104, 961.597, 0.533], [321.289, 1034.564, 0.81], [289.738, 1178.466, 0.654], [369.278, 927.402, 0.746]]\nC: [[301.407, 1085.027, 0.561], [353.922, 1230.167, 0.74], [385.078, 1056.365, 0.831], [353.967, 1321.653, 0.933]]\nD: [[332.499, 1323.247, 0.603], [328.44, 1217.95, 0.71], [304.408, 1248.393, 0.704], [312.725, 1041.977, 0.788]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1098.816, 1040.956, 2.25], [1517.027, 1201.267, 2.159], [1287.68, 1114.311, 1.84], [1553.384, 936.614, 1.891]]\nB: [[1537.939, 1003.593, 1.619], [1107.39, 826.486, 1.866], [1160.283, 877.892, 2.283], [1427.746, 1058.395, 2.165]]\nC: [[1325.647, 1026.158, 1.972], [1325.647, 1026.158, 1.972], [1325.647, 1026.158, 1.972], [1325.647, 1026.158, 1.972]]\nD: [[1454.649, 935.993, 2.155], [1581.869, 969.794, 1.581], [1203.456, 996.196, 2.063], [1385.658, 925.079, 2.322]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_140_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_140_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_140_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_140_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_140_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_140_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_140_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_140_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1098.816, 1040.956, 2.25], [1517.027, 1201.267, 2.159], [1287.68, 1114.311, 1.84], [1553.384, 936.614, 1.891]]\nB: [[1537.939, 1003.593, 1.619], [1107.39, 826.486, 1.866], [1160.283, 877.892, 2.283], [1427.746, 1058.395, 2.165]]\nC: [[1325.647, 1026.158, 1.972], [1325.647, 1026.158, 1.972], [1325.647, 1026.158, 1.972], [1325.647, 1026.158, 1.972]]\nD: [[1454.649, 935.993, 2.155], [1581.869, 969.794, 1.581], [1203.456, 996.196, 2.063], [1385.658, 925.079, 2.322]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[436.144, 1095.893, 0.64], [378.481, 1266.824, 0.483], [426.128, 1098.819, 0.65], [324.34, 949.634, 0.625]]\nB: [[397.389, 1164.192, 0.54], [397.389, 1164.192, 0.565], [397.389, 1164.192, 0.59], [397.389, 1164.192, 0.615]]\nC: [[380.365, 1356.637, 0.44], [339.063, 1111.83, 0.512], [337.584, 979.936, 0.64], [437.254, 1203.389, 0.683]]\nD: [[323.527, 1041.167, 0.63], [470.94, 1158.877, 0.637], [366.836, 1001.327, 0.61], [420.137, 1320.47, 0.577]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_141_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_141_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_141_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_141_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_141_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_141_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_141_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_141_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[436.144, 1095.893, 0.64], [378.481, 1266.824, 0.483], [426.128, 1098.819, 0.65], [324.34, 949.634, 0.625]]\nB: [[397.389, 1164.192, 0.54], [397.389, 1164.192, 0.565], [397.389, 1164.192, 0.59], [397.389, 1164.192, 0.615]]\nC: [[380.365, 1356.637, 0.44], [339.063, 1111.83, 0.512], [337.584, 979.936, 0.64], [437.254, 1203.389, 0.683]]\nD: [[323.527, 1041.167, 0.63], [470.94, 1158.877, 0.637], [366.836, 1001.327, 0.61], [420.137, 1320.47, 0.577]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[345.303, 927.986, 0.6], [322.702, 926.62, 0.836], [322.388, 1098.752, 0.715], [411.956, 1203.533, 1.062]]\nB: [[360.095, 1122.376, 0.7], [360.061, 1122.39, 0.773], [360.027, 1122.404, 0.846], [359.993, 1122.417, 0.918]]\nC: [[325.719, 970.767, 0.8], [352.264, 988.9, 0.891], [370.173, 1212.852, 0.847], [306.427, 1052.878, 0.831]]\nD: [[408.76, 1154.201, 0.8], [384.285, 1027.05, 0.647], [381.462, 1131.647, 0.827], [348.63, 1106.215, 0.947]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_142_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_142_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_142_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_142_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_142_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_142_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_142_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_142_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[345.303, 927.986, 0.6], [322.702, 926.62, 0.836], [322.388, 1098.752, 0.715], [411.956, 1203.533, 1.062]]\nB: [[360.095, 1122.376, 0.7], [360.061, 1122.39, 0.773], [360.027, 1122.404, 0.846], [359.993, 1122.417, 0.918]]\nC: [[325.719, 970.767, 0.8], [352.264, 988.9, 0.891], [370.173, 1212.852, 0.847], [306.427, 1052.878, 0.831]]\nD: [[408.76, 1154.201, 0.8], [384.285, 1027.05, 0.647], [381.462, 1131.647, 0.827], [348.63, 1106.215, 0.947]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[342.004, 1040.88, 0.744], [329.607, 1045.03, 0.684], [379.212, 1250.647, 0.868], [326.815, 1264.503, 0.847]]\nB: [[356.724, 1113.785, 0.625], [356.749, 1113.855, 0.775], [356.778, 1113.889, 0.975], [356.785, 1113.897, 1.025]]\nC: [[412.74, 910.82, 0.676], [365.411, 1210.523, 0.664], [303.937, 1114.862, 0.873], [419.175, 1333.448, 0.84]]\nD: [[295.038, 1240.739, 0.504], [341.219, 1044.42, 0.812], [352.463, 1064.815, 1.125], [371.869, 1069.702, 0.977]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_143_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_143_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_143_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_143_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_143_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_143_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_143_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_143_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[342.004, 1040.88, 0.744], [329.607, 1045.03, 0.684], [379.212, 1250.647, 0.868], [326.815, 1264.503, 0.847]]\nB: [[356.724, 1113.785, 0.625], [356.749, 1113.855, 0.775], [356.778, 1113.889, 0.975], [356.785, 1113.897, 1.025]]\nC: [[412.74, 910.82, 0.676], [365.411, 1210.523, 0.664], [303.937, 1114.862, 0.873], [419.175, 1333.448, 0.84]]\nD: [[295.038, 1240.739, 0.504], [341.219, 1044.42, 0.812], [352.463, 1064.815, 1.125], [371.869, 1069.702, 0.977]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1394.419, 833.646, 0.583], [1328.905, 1140.762, 0.779], [1153.453, 1205.926, 0.657], [1146.98, 1211.025, 0.624]]\nB: [[1313.096, 1036.989, 0.652], [1313.096, 1036.989, 0.652], [1313.096, 1036.989, 0.652], [1313.096, 1036.989, 0.652]]\nC: [[1268.974, 982.967, 0.746], [1564.315, 845.479, 0.727], [1340.978, 1034.082, 0.715], [1495.256, 1214.335, 0.578]]\nD: [[1459.081, 1060.013, 0.734], [1278.902, 880.579, 0.542], [1345.294, 988.27, 0.706], [1466.974, 993.897, 0.65]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_144_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_144_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_144_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_144_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_144_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_144_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_144_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_144_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1394.419, 833.646, 0.583], [1328.905, 1140.762, 0.779], [1153.453, 1205.926, 0.657], [1146.98, 1211.025, 0.624]]\nB: [[1313.096, 1036.989, 0.652], [1313.096, 1036.989, 0.652], [1313.096, 1036.989, 0.652], [1313.096, 1036.989, 0.652]]\nC: [[1268.974, 982.967, 0.746], [1564.315, 845.479, 0.727], [1340.978, 1034.082, 0.715], [1495.256, 1214.335, 0.578]]\nD: [[1459.081, 1060.013, 0.734], [1278.902, 880.579, 0.542], [1345.294, 988.27, 0.706], [1466.974, 993.897, 0.65]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[483.59, 928.44, 0.641], [429.525, 1137.919, 0.585], [431.636, 1075.281, 0.417], [368.098, 942.18, 0.409]]\nB: [[506.828, 1075.62, 0.78], [523.808, 941.549, 0.581], [482.43, 968.916, 0.331], [419.206, 1128.103, 0.389]]\nC: [[440.798, 1086.59, 0.718], [440.809, 1086.616, 0.568], [440.809, 1086.616, 0.368], [440.809, 1086.616, 0.368]]\nD: [[495.844, 942.38, 0.674], [400.153, 884.092, 0.599], [357.631, 872.728, 0.373], [508.512, 889.858, 0.32]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_145_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_145_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_145_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_145_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_145_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_145_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_145_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_145_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[483.59, 928.44, 0.641], [429.525, 1137.919, 0.585], [431.636, 1075.281, 0.417], [368.098, 942.18, 0.409]]\nB: [[506.828, 1075.62, 0.78], [523.808, 941.549, 0.581], [482.43, 968.916, 0.331], [419.206, 1128.103, 0.389]]\nC: [[440.798, 1086.59, 0.718], [440.809, 1086.616, 0.568], [440.809, 1086.616, 0.368], [440.809, 1086.616, 0.368]]\nD: [[495.844, 942.38, 0.674], [400.153, 884.092, 0.599], [357.631, 872.728, 0.373], [508.512, 889.858, 0.32]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[431.795, 1089.293, 0.732], [431.79, 1089.298, 0.611], [431.763, 1089.236, 0.548], [431.76, 1089.235, 0.741]]\nB: [[430.055, 1135.006, 0.834], [362.83, 1266.13, 0.535], [510.725, 963.311, 0.449], [354.76, 1199.812, 0.852]]\nC: [[374.034, 1232.506, 0.835], [446.62, 1198.857, 0.654], [454.385, 1036.14, 0.539], [461.62, 1215.977, 0.65]]\nD: [[426.107, 1134.166, 0.869], [469.51, 941.329, 0.702], [490.047, 990.374, 0.543], [356.18, 1025.654, 0.728]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_146_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_146_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_146_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_146_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_146_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_146_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_146_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_146_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[431.795, 1089.293, 0.732], [431.79, 1089.298, 0.611], [431.763, 1089.236, 0.548], [431.76, 1089.235, 0.741]]\nB: [[430.055, 1135.006, 0.834], [362.83, 1266.13, 0.535], [510.725, 963.311, 0.449], [354.76, 1199.812, 0.852]]\nC: [[374.034, 1232.506, 0.835], [446.62, 1198.857, 0.654], [454.385, 1036.14, 0.539], [461.62, 1215.977, 0.65]]\nD: [[426.107, 1134.166, 0.869], [469.51, 941.329, 0.702], [490.047, 990.374, 0.543], [356.18, 1025.654, 0.728]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1042.405, 896.49, 0.459], [1248.26, 1026.303, 0.519], [1554.895, 994.696, 0.5], [1480.656, 1055.338, 0.476]]\nB: [[1168.242, 1176.462, 0.406], [1219.42, 1198.448, 0.487], [1280.042, 1223.429, 0.5], [1301.768, 1040.639, 0.468]]\nC: [[1293.229, 1033.246, 0.388], [1296.13, 1035.001, 0.465], [1296.744, 1035.285, 0.5], [1297.358, 1035.569, 0.535]]\nD: [[1205.688, 1036.352, 0.443], [1283.41, 852.956, 0.509], [1152.749, 895.819, 0.4], [1317.874, 1012.868, 0.584]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_147_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_147_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_147_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_147_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_147_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_147_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_147_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_147_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1042.405, 896.49, 0.459], [1248.26, 1026.303, 0.519], [1554.895, 994.696, 0.5], [1480.656, 1055.338, 0.476]]\nB: [[1168.242, 1176.462, 0.406], [1219.42, 1198.448, 0.487], [1280.042, 1223.429, 0.5], [1301.768, 1040.639, 0.468]]\nC: [[1293.229, 1033.246, 0.388], [1296.13, 1035.001, 0.465], [1296.744, 1035.285, 0.5], [1297.358, 1035.569, 0.535]]\nD: [[1205.688, 1036.352, 0.443], [1283.41, 852.956, 0.509], [1152.749, 895.819, 0.4], [1317.874, 1012.868, 0.584]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2143.23, 880.092, 1.463], [2237.79, 816.687, 1.792], [2021.67, 1004.854, 1.441], [2013.58, 762.901, 1.623]]\nB: [[2030.68, 939.082, 1.556], [1707.64, 827.801, 1.543], [2187.71, 957.754, 1.703], [1734.52, 730.487, 1.539]]\nC: [[2157.8, 869.883, 1.527], [1960.08, 1008.147, 1.759], [1779.88, 929.643, 1.772], [1538.45, 884.771, 1.889]]\nD: [[1907.37, 864.452, 1.647], [1907.37, 864.452, 1.647], [1907.37, 864.452, 1.647], [1907.37, 864.452, 1.647]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_148_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_148_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_148_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_148_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_148_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_148_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_148_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_148_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2143.23, 880.092, 1.463], [2237.79, 816.687, 1.792], [2021.67, 1004.854, 1.441], [2013.58, 762.901, 1.623]]\nB: [[2030.68, 939.082, 1.556], [1707.64, 827.801, 1.543], [2187.71, 957.754, 1.703], [1734.52, 730.487, 1.539]]\nC: [[2157.8, 869.883, 1.527], [1960.08, 1008.147, 1.759], [1779.88, 929.643, 1.772], [1538.45, 884.771, 1.889]]\nD: [[1907.37, 864.452, 1.647], [1907.37, 864.452, 1.647], [1907.37, 864.452, 1.647], [1907.37, 864.452, 1.647]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[333.441, 1111.848, 0.881], [358.68, 1279.096, 0.848], [365.642, 1024.723, 1.135], [416.842, 1221.204, 1.061]]\nB: [[359.553, 1105.178, 0.934], [359.553, 1105.178, 1.045], [359.553, 1105.178, 1.082], [359.553, 1105.178, 1.005]]\nC: [[401.411, 1302.101, 0.9], [401.716, 1050.934, 1.205], [369.114, 1004.72, 0.951], [298.807, 954.194, 1.162]]\nD: [[326.721, 1063.94, 1.102], [316.366, 1058.028, 0.884], [321.691, 954.121, 1.273], [352.284, 1064.278, 1.172]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_149_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_149_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_149_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_149_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_149_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_149_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_149_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_149_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[333.441, 1111.848, 0.881], [358.68, 1279.096, 0.848], [365.642, 1024.723, 1.135], [416.842, 1221.204, 1.061]]\nB: [[359.553, 1105.178, 0.934], [359.553, 1105.178, 1.045], [359.553, 1105.178, 1.082], [359.553, 1105.178, 1.005]]\nC: [[401.411, 1302.101, 0.9], [401.716, 1050.934, 1.205], [369.114, 1004.72, 0.951], [298.807, 954.194, 1.162]]\nD: [[326.721, 1063.94, 1.102], [316.366, 1058.028, 0.884], [321.691, 954.121, 1.273], [352.284, 1064.278, 1.172]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1914.957, 873.014, 0.241], [1914.951, 872.993, 0.241], [1914.944, 872.972, 0.241], [1914.937, 872.951, 0.241]]\nB: [[2029.589, 880.527, 0.258], [1539.423, 812.215, 0.227], [2219.176, 984.894, 0.264], [2240.588, 737.815, 0.2]]\nC: [[2084.127, 771.252, 0.23], [1889.253, 1036.727, 0.264], [1713.036, 984.106, 0.254], [1867.742, 808.403, 0.199]]\nD: [[1951.845, 910.003, 0.272], [2067.414, 726.492, 0.226], [1574.866, 827.537, 0.223], [2080.281, 1029.475, 0.221]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_150_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_150_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_150_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_150_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_150_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_150_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_150_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_150_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1914.957, 873.014, 0.241], [1914.951, 872.993, 0.241], [1914.944, 872.972, 0.241], [1914.937, 872.951, 0.241]]\nB: [[2029.589, 880.527, 0.258], [1539.423, 812.215, 0.227], [2219.176, 984.894, 0.264], [2240.588, 737.815, 0.2]]\nC: [[2084.127, 771.252, 0.23], [1889.253, 1036.727, 0.264], [1713.036, 984.106, 0.254], [1867.742, 808.403, 0.199]]\nD: [[1951.845, 910.003, 0.272], [2067.414, 726.492, 0.226], [1574.866, 827.537, 0.223], [2080.281, 1029.475, 0.221]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[775.145, 1609.526, 0.275], [728.049, 1548.593, 0.225], [726.281, 1889.554, 0.225], [701.777, 1569.883, 0.285]]\nB: [[635.753, 1733.477, 0.272], [720.552, 1845.796, 0.237], [810.826, 1848.81, 0.272], [673.334, 1345.738, 0.236]]\nC: [[563.331, 1620.514, 0.207], [692.265, 1578.918, 0.262], [633.015, 1756.886, 0.242], [605.445, 1415.589, 0.246]]\nD: [[696.721, 1578.786, 0.244], [696.693, 1578.758, 0.244], [696.674, 1578.723, 0.244], [696.664, 1578.684, 0.244]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_151_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_151_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_151_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_151_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_151_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_151_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_151_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_151_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[775.145, 1609.526, 0.275], [728.049, 1548.593, 0.225], [726.281, 1889.554, 0.225], [701.777, 1569.883, 0.285]]\nB: [[635.753, 1733.477, 0.272], [720.552, 1845.796, 0.237], [810.826, 1848.81, 0.272], [673.334, 1345.738, 0.236]]\nC: [[563.331, 1620.514, 0.207], [692.265, 1578.918, 0.262], [633.015, 1756.886, 0.242], [605.445, 1415.589, 0.246]]\nD: [[696.721, 1578.786, 0.244], [696.693, 1578.758, 0.244], [696.674, 1578.723, 0.244], [696.664, 1578.684, 0.244]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[650.646, 1603.342, 0.03], [650.62, 1603.362, 0.13], [650.688, 1603.329, 0.308], [650.756, 1603.295, 0.485]]\nB: [[567.786, 1547.911, 0.04], [679.87, 1773.392, 0.15], [682.07, 1467.32, 0.353], [776.72, 1507.964, 0.528]]\nC: [[765.242, 1777.09, 0.03], [719.37, 1669.37, 0.15], [581.778, 1910.816, 0.336], [602.521, 1760.712, 0.392]]\nD: [[522.813, 1797.126, 0.03], [721.02, 1459.791, 0.11], [758.676, 1452.614, 0.344], [628.48, 1519.176, 0.474]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_152_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_152_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_152_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_152_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_152_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_152_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_152_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_152_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[650.646, 1603.342, 0.03], [650.62, 1603.362, 0.13], [650.688, 1603.329, 0.308], [650.756, 1603.295, 0.485]]\nB: [[567.786, 1547.911, 0.04], [679.87, 1773.392, 0.15], [682.07, 1467.32, 0.353], [776.72, 1507.964, 0.528]]\nC: [[765.242, 1777.09, 0.03], [719.37, 1669.37, 0.15], [581.778, 1910.816, 0.336], [602.521, 1760.712, 0.392]]\nD: [[522.813, 1797.126, 0.03], [721.02, 1459.791, 0.11], [758.676, 1452.614, 0.344], [628.48, 1519.176, 0.474]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1344.72, 1281.191, 3.09], [1075.39, 972.846, 3.969], [1227.93, 967.953, 2.882], [1313.85, 1191.672, 2.766]]\nB: [[1340.58, 1102.095, 3.358], [1340.58, 1102.095, 3.358], [1340.56, 1102.069, 3.358], [1340.56, 1102.069, 3.358]]\nC: [[1595.5, 1117.589, 3.115], [1363.06, 1051.692, 3.599], [1333.05, 1281.644, 3.54], [1459.61, 1297.063, 3.45]]\nD: [[1406.68, 1037.893, 3.818], [1555.01, 1107.104, 3.069], [1222.57, 1178.434, 3.387], [1139.88, 1136.126, 3.194]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_153_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_153_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_153_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_153_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_153_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_153_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_153_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_153_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1344.72, 1281.191, 3.09], [1075.39, 972.846, 3.969], [1227.93, 967.953, 2.882], [1313.85, 1191.672, 2.766]]\nB: [[1340.58, 1102.095, 3.358], [1340.58, 1102.095, 3.358], [1340.56, 1102.069, 3.358], [1340.56, 1102.069, 3.358]]\nC: [[1595.5, 1117.589, 3.115], [1363.06, 1051.692, 3.599], [1333.05, 1281.644, 3.54], [1459.61, 1297.063, 3.45]]\nD: [[1406.68, 1037.893, 3.818], [1555.01, 1107.104, 3.069], [1222.57, 1178.434, 3.387], [1139.88, 1136.126, 3.194]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[366.659, 1011.101, 1.22], [364.315, 1040.812, 1.078], [399.107, 1299.752, 1.185], [296.881, 916.886, 1.47]]\nB: [[363.259, 1094.238, 1.247], [363.277, 1094.229, 1.276], [363.296, 1094.221, 1.306], [363.315, 1094.212, 1.335]]\nC: [[349.227, 1179.598, 1.249], [388.454, 911.85, 1.32], [338.754, 1093.699, 1.127], [427.119, 948.416, 1.102]]\nD: [[420.809, 960.294, 1.148], [382.372, 1303.064, 1.394], [429.821, 1184.841, 1.121], [362.615, 1271.967, 1.2]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_154_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_154_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_154_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_154_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_154_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_154_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_154_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_154_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[366.659, 1011.101, 1.22], [364.315, 1040.812, 1.078], [399.107, 1299.752, 1.185], [296.881, 916.886, 1.47]]\nB: [[363.259, 1094.238, 1.247], [363.277, 1094.229, 1.276], [363.296, 1094.221, 1.306], [363.315, 1094.212, 1.335]]\nC: [[349.227, 1179.598, 1.249], [388.454, 911.85, 1.32], [338.754, 1093.699, 1.127], [427.119, 948.416, 1.102]]\nD: [[420.809, 960.294, 1.148], [382.372, 1303.064, 1.394], [429.821, 1184.841, 1.121], [362.615, 1271.967, 1.2]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1954.841, 997.5, 0.236], [1604.826, 1018.173, 0.282], [2056.441, 825.549, 0.202], [2053.148, 806.675, 0.206]]\nB: [[1911.473, 872.92, 0.247], [1911.473, 872.927, 0.247], [1911.473, 872.935, 0.247], [1911.473, 872.912, 0.247]]\nC: [[1627.59, 704.51, 0.221], [1743.247, 820.718, 0.239], [1954.356, 974.622, 0.27], [1682.124, 823.985, 0.25]]\nD: [[1859.848, 855.57, 0.227], [1677.344, 943.885, 0.289], [1603.535, 883.7, 0.263], [2177.549, 800.573, 0.251]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_155_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_155_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_155_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_155_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_155_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_155_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_155_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_155_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1954.841, 997.5, 0.236], [1604.826, 1018.173, 0.282], [2056.441, 825.549, 0.202], [2053.148, 806.675, 0.206]]\nB: [[1911.473, 872.92, 0.247], [1911.473, 872.927, 0.247], [1911.473, 872.935, 0.247], [1911.473, 872.912, 0.247]]\nC: [[1627.59, 704.51, 0.221], [1743.247, 820.718, 0.239], [1954.356, 974.622, 0.27], [1682.124, 823.985, 0.25]]\nD: [[1859.848, 855.57, 0.227], [1677.344, 943.885, 0.289], [1603.535, 883.7, 0.263], [2177.549, 800.573, 0.251]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[405.086, 1259.825, 0.988], [341.109, 1024.661, 1.196], [400.072, 1270.26, 0.973], [361.016, 1307.675, 1.222]]\nB: [[493.579, 972.185, 0.834], [476.692, 1075.558, 0.912], [401.433, 969.577, 0.981], [403.596, 1269.671, 1.032]]\nC: [[431.9, 1414.092, 1.036], [482.304, 1353.899, 0.879], [441.439, 1311.735, 0.916], [400.616, 1079.275, 1.136]]\nD: [[417.374, 1192.132, 0.961], [416.718, 1192.286, 1.011], [416.058, 1192.412, 1.061], [415.392, 1192.512, 1.111]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_156_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_156_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_156_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_156_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_156_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_156_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_156_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_156_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[405.086, 1259.825, 0.988], [341.109, 1024.661, 1.196], [400.072, 1270.26, 0.973], [361.016, 1307.675, 1.222]]\nB: [[493.579, 972.185, 0.834], [476.692, 1075.558, 0.912], [401.433, 969.577, 0.981], [403.596, 1269.671, 1.032]]\nC: [[431.9, 1414.092, 1.036], [482.304, 1353.899, 0.879], [441.439, 1311.735, 0.916], [400.616, 1079.275, 1.136]]\nD: [[417.374, 1192.132, 0.961], [416.718, 1192.286, 1.011], [416.058, 1192.412, 1.061], [415.392, 1192.512, 1.111]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[326.56, 1024.467, 0.519], [375.34, 1251.894, 0.454], [324.82, 1119.168, 0.508], [372.44, 1074.004, 0.7]]\nB: [[446.37, 1235.831, 0.437], [419.37, 934.045, 0.644], [380.01, 1072.298, 0.649], [417.35, 1264.432, 0.693]]\nC: [[438.81, 1043.645, 0.444], [435.77, 1350.631, 0.617], [421.85, 1208.417, 0.602], [334.9, 991.841, 0.557]]\nD: [[387.52, 1143.568, 0.508], [387.52, 1143.568, 0.541], [387.52, 1143.568, 0.575], [387.52, 1143.568, 0.608]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_157_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_157_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_157_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_157_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_157_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_157_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_157_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_157_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[326.56, 1024.467, 0.519], [375.34, 1251.894, 0.454], [324.82, 1119.168, 0.508], [372.44, 1074.004, 0.7]]\nB: [[446.37, 1235.831, 0.437], [419.37, 934.045, 0.644], [380.01, 1072.298, 0.649], [417.35, 1264.432, 0.693]]\nC: [[438.81, 1043.645, 0.444], [435.77, 1350.631, 0.617], [421.85, 1208.417, 0.602], [334.9, 991.841, 0.557]]\nD: [[387.52, 1143.568, 0.508], [387.52, 1143.568, 0.541], [387.52, 1143.568, 0.575], [387.52, 1143.568, 0.608]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[482.206, 1161.483, 0.923], [362.768, 1002.877, 1.176], [464.329, 933.744, 1.058], [365.042, 1087.466, 0.59]]\nB: [[395.933, 960.032, 0.91], [390.823, 1104.508, 1.171], [437.592, 1073.569, 0.945], [481.154, 1034.808, 0.54]]\nC: [[418.967, 1094.306, 1.038], [418.951, 1094.348, 1.008], [418.987, 1094.368, 1.068], [418.873, 1094.555, 0.56]]\nD: [[425.42, 1279.316, 1.07], [448.65, 1259.388, 1.115], [445.464, 1156.231, 1.185], [492.603, 965.675, 0.64]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_158_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_158_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_158_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_158_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_158_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_158_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_158_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_158_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[482.206, 1161.483, 0.923], [362.768, 1002.877, 1.176], [464.329, 933.744, 1.058], [365.042, 1087.466, 0.59]]\nB: [[395.933, 960.032, 0.91], [390.823, 1104.508, 1.171], [437.592, 1073.569, 0.945], [481.154, 1034.808, 0.54]]\nC: [[418.967, 1094.306, 1.038], [418.951, 1094.348, 1.008], [418.987, 1094.368, 1.068], [418.873, 1094.555, 0.56]]\nD: [[425.42, 1279.316, 1.07], [448.65, 1259.388, 1.115], [445.464, 1156.231, 1.185], [492.603, 965.675, 0.64]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2133.097, 774.816, 1.225], [1748.933, 832.577, 1.031], [1567.379, 834.66, 1.265], [1502.433, 691.935, 1.105]]\nB: [[1806.535, 861.266, 1.066], [1807.133, 859.654, 1.066], [1807.563, 858.13, 1.066], [1807.849, 856.657, 1.021]]\nC: [[2099.2, 882.41, 1.014], [2094.1, 791.51, 0.857], [1724.174, 770.83, 0.904], [1636.953, 895.976, 1.042]]\nD: [[1853.024, 785.737, 0.887], [1793.267, 868.356, 1.224], [1854.012, 828.75, 1.266], [2127.184, 793.379, 1.141]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_159_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_159_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_159_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_159_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_159_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_159_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_159_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_159_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2133.097, 774.816, 1.225], [1748.933, 832.577, 1.031], [1567.379, 834.66, 1.265], [1502.433, 691.935, 1.105]]\nB: [[1806.535, 861.266, 1.066], [1807.133, 859.654, 1.066], [1807.563, 858.13, 1.066], [1807.849, 856.657, 1.021]]\nC: [[2099.2, 882.41, 1.014], [2094.1, 791.51, 0.857], [1724.174, 770.83, 0.904], [1636.953, 895.976, 1.042]]\nD: [[1853.024, 785.737, 0.887], [1793.267, 868.356, 1.224], [1854.012, 828.75, 1.266], [2127.184, 793.379, 1.141]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1039.96, 1235.697, 1.849], [1056.893, 1292.385, 1.963], [1073.252, 1262.199, 2.07], [1057.534, 1122.452, 1.706]]\nB: [[1248.78, 1187.636, 2.366], [1189.102, 1066.566, 2.081], [1235.361, 1032.631, 1.92], [1111.357, 1241.726, 2.171]]\nC: [[1147.17, 1172.553, 1.994], [1344.188, 1301.314, 1.937], [974.406, 1174.168, 1.55], [1159.003, 1238.282, 2.257]]\nD: [[1181.87, 1122.359, 2.116], [1185.363, 1119.943, 2.248], [1188.856, 1117.528, 1.93], [1192.349, 1115.113, 1.954]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_160_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_160_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_160_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_160_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_160_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_160_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_160_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_160_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1039.96, 1235.697, 1.849], [1056.893, 1292.385, 1.963], [1073.252, 1262.199, 2.07], [1057.534, 1122.452, 1.706]]\nB: [[1248.78, 1187.636, 2.366], [1189.102, 1066.566, 2.081], [1235.361, 1032.631, 1.92], [1111.357, 1241.726, 2.171]]\nC: [[1147.17, 1172.553, 1.994], [1344.188, 1301.314, 1.937], [974.406, 1174.168, 1.55], [1159.003, 1238.282, 2.257]]\nD: [[1181.87, 1122.359, 2.116], [1185.363, 1119.943, 2.248], [1188.856, 1117.528, 1.93], [1192.349, 1115.113, 1.954]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[407.79, 1169.713, 1.047], [438.535, 914.451, 0.817], [474.944, 1174.579, 0.711], [407.788, 1311.155, 0.697]]\nB: [[411.096, 1097.949, 1.026], [411.096, 1097.949, 0.806], [411.096, 1097.949, 0.756], [411.181, 1097.914, 0.706]]\nC: [[380.059, 1294.905, 1.061], [462.302, 957.203, 0.843], [360.056, 1036.95, 0.635], [377.308, 1174.326, 0.803]]\nD: [[453.527, 1100.533, 1.002], [399.919, 928.877, 0.842], [458.797, 1077.795, 0.803], [333.443, 1294.271, 0.8]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_161_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_161_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_161_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_161_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_161_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_161_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_161_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_161_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[407.79, 1169.713, 1.047], [438.535, 914.451, 0.817], [474.944, 1174.579, 0.711], [407.788, 1311.155, 0.697]]\nB: [[411.096, 1097.949, 1.026], [411.096, 1097.949, 0.806], [411.096, 1097.949, 0.756], [411.181, 1097.914, 0.706]]\nC: [[380.059, 1294.905, 1.061], [462.302, 957.203, 0.843], [360.056, 1036.95, 0.635], [377.308, 1174.326, 0.803]]\nD: [[453.527, 1100.533, 1.002], [399.919, 928.877, 0.842], [458.797, 1077.795, 0.803], [333.443, 1294.271, 0.8]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[377.184, 977.334, 0.742], [375.911, 1030.014, 0.731], [428.729, 1127.517, 0.56], [402.816, 1027.37, 0.555]]\nB: [[447.851, 943.564, 0.851], [507.44, 1079.2, 0.691], [496.497, 1228.261, 0.42], [422.118, 1234.56, 0.566]]\nC: [[427.284, 1091.127, 0.774], [427.286, 1091.126, 0.707], [427.292, 1091.122, 0.507], [427.294, 1091.12, 0.628]]\nD: [[480.047, 995.531, 0.924], [418.676, 923.092, 0.59], [412.858, 1042.957, 0.421], [343.915, 1040.26, 0.623]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_162_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_162_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_162_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_162_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_162_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_162_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_162_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_162_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[377.184, 977.334, 0.742], [375.911, 1030.014, 0.731], [428.729, 1127.517, 0.56], [402.816, 1027.37, 0.555]]\nB: [[447.851, 943.564, 0.851], [507.44, 1079.2, 0.691], [496.497, 1228.261, 0.42], [422.118, 1234.56, 0.566]]\nC: [[427.284, 1091.127, 0.774], [427.286, 1091.126, 0.707], [427.292, 1091.122, 0.507], [427.294, 1091.12, 0.628]]\nD: [[480.047, 995.531, 0.924], [418.676, 923.092, 0.59], [412.858, 1042.957, 0.421], [343.915, 1040.26, 0.623]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1470.303, 1200.503, -0.117], [1211.565, 906.028, -0.109], [1265.671, 860.24, -0.139], [1225.185, 861.066, -0.166]]\nB: [[1121.534, 848.02, -0.101], [1300.088, 882.168, -0.116], [1372.338, 987.41, -0.161], [1121.468, 1062.994, -0.117]]\nC: [[1239.215, 1012.078, -0.106], [1239.169, 1012.039, -0.126], [1239.146, 1012.02, -0.136], [1239.123, 1012.001, -0.146]]\nD: [[1353.598, 917.259, -0.096], [1218.603, 1014.25, -0.105], [1110.323, 1116.23, -0.13], [1224.972, 1200.395, -0.138]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_163_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_163_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_163_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_163_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_163_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_163_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_163_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_163_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1470.303, 1200.503, -0.117], [1211.565, 906.028, -0.109], [1265.671, 860.24, -0.139], [1225.185, 861.066, -0.166]]\nB: [[1121.534, 848.02, -0.101], [1300.088, 882.168, -0.116], [1372.338, 987.41, -0.161], [1121.468, 1062.994, -0.117]]\nC: [[1239.215, 1012.078, -0.106], [1239.169, 1012.039, -0.126], [1239.146, 1012.02, -0.136], [1239.123, 1012.001, -0.146]]\nD: [[1353.598, 917.259, -0.096], [1218.603, 1014.25, -0.105], [1110.323, 1116.23, -0.13], [1224.972, 1200.395, -0.138]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[390.311, 1121.642, 0.818], [389.934, 1120.974, 0.748], [389.624, 1120.4, 0.778], [389.38, 1119.804, 0.858]]\nB: [[315.313, 963.621, 0.732], [377.113, 912.889, 0.734], [351.418, 1317.0, 0.875], [422.52, 1203.28, 0.858]]\nC: [[416.232, 1151.901, 0.879], [421.322, 1109.197, 0.81], [452.884, 1049.1, 0.63], [415.15, 1098.518, 0.99]]\nD: [[432.466, 1326.016, 0.887], [398.73, 1272.899, 0.773], [453.388, 964.3, 0.838], [398.14, 1308.497, 0.972]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_164_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_164_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_164_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_164_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_164_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_164_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_164_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_164_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[390.311, 1121.642, 0.818], [389.934, 1120.974, 0.748], [389.624, 1120.4, 0.778], [389.38, 1119.804, 0.858]]\nB: [[315.313, 963.621, 0.732], [377.113, 912.889, 0.734], [351.418, 1317.0, 0.875], [422.52, 1203.28, 0.858]]\nC: [[416.232, 1151.901, 0.879], [421.322, 1109.197, 0.81], [452.884, 1049.1, 0.63], [415.15, 1098.518, 0.99]]\nD: [[432.466, 1326.016, 0.887], [398.73, 1272.899, 0.773], [453.388, 964.3, 0.838], [398.14, 1308.497, 0.972]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[288.389, 823.36, 1.04], [243.336, 770.263, 1.17], [329.163, 556.194, 0.924], [278.167, 724.049, 1.175]]\nB: [[303.148, 635.853, 0.872], [255.423, 572.035, 1.073], [262.191, 559.079, 0.89], [295.029, 679.27, 0.862]]\nC: [[250.538, 640.039, 0.892], [355.139, 799.298, 1.133], [280.545, 705.593, 1.08], [285.606, 739.161, 1.054]]\nD: [[301.073, 691.921, 1.008], [300.166, 690.701, 1.008], [299.259, 689.481, 1.008], [298.351, 688.262, 1.008]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_165_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_165_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_165_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_165_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_165_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_165_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_165_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_165_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[288.389, 823.36, 1.04], [243.336, 770.263, 1.17], [329.163, 556.194, 0.924], [278.167, 724.049, 1.175]]\nB: [[303.148, 635.853, 0.872], [255.423, 572.035, 1.073], [262.191, 559.079, 0.89], [295.029, 679.27, 0.862]]\nC: [[250.538, 640.039, 0.892], [355.139, 799.298, 1.133], [280.545, 705.593, 1.08], [285.606, 739.161, 1.054]]\nD: [[301.073, 691.921, 1.008], [300.166, 690.701, 1.008], [299.259, 689.481, 1.008], [298.351, 688.262, 1.008]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[374.966, 918.231, 1.798], [317.815, 1228.212, 1.721], [398.89, 875.569, 1.814], [428.79, 1016.473, 1.875]]\nB: [[360.439, 1086.932, 1.56], [381.745, 1179.649, 1.447], [395.985, 1168.142, 1.848], [396.108, 960.271, 1.709]]\nC: [[358.757, 1084.221, 1.582], [358.757, 1084.221, 1.575], [358.757, 1084.221, 1.585], [358.757, 1084.221, 1.664]]\nD: [[320.557, 873.472, 1.863], [409.377, 874.584, 1.304], [376.574, 953.049, 1.86], [336.743, 903.665, 1.608]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_166_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_166_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_166_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_166_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_166_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_166_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_166_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_166_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[374.966, 918.231, 1.798], [317.815, 1228.212, 1.721], [398.89, 875.569, 1.814], [428.79, 1016.473, 1.875]]\nB: [[360.439, 1086.932, 1.56], [381.745, 1179.649, 1.447], [395.985, 1168.142, 1.848], [396.108, 960.271, 1.709]]\nC: [[358.757, 1084.221, 1.582], [358.757, 1084.221, 1.575], [358.757, 1084.221, 1.585], [358.757, 1084.221, 1.664]]\nD: [[320.557, 873.472, 1.863], [409.377, 874.584, 1.304], [376.574, 953.049, 1.86], [336.743, 903.665, 1.608]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2192.318, 842.128, 0.202], [2028.569, 1002.104, 0.362], [1823.353, 915.088, 0.434], [1848.897, 1015.376, 0.66]]\nB: [[2200.193, 887.932, 0.279], [2035.641, 762.88, 0.308], [1865.863, 809.786, 0.584], [1497.967, 1016.346, 0.579]]\nC: [[1842.467, 871.854, 0.249], [1837.266, 871.727, 0.362], [1831.484, 871.587, 0.487], [1825.702, 871.447, 0.612]]\nD: [[1510.791, 938.627, 0.271], [1853.106, 942.739, 0.426], [1927.734, 739.554, 0.479], [1666.087, 996.74, 0.708]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_167_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_167_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_167_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_167_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_167_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_167_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_167_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_167_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2192.318, 842.128, 0.202], [2028.569, 1002.104, 0.362], [1823.353, 915.088, 0.434], [1848.897, 1015.376, 0.66]]\nB: [[2200.193, 887.932, 0.279], [2035.641, 762.88, 0.308], [1865.863, 809.786, 0.584], [1497.967, 1016.346, 0.579]]\nC: [[1842.467, 871.854, 0.249], [1837.266, 871.727, 0.362], [1831.484, 871.587, 0.487], [1825.702, 871.447, 0.612]]\nD: [[1510.791, 938.627, 0.271], [1853.106, 942.739, 0.426], [1927.734, 739.554, 0.479], [1666.087, 996.74, 0.708]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[342.032, 761.086, -0.593], [347.646, 679.25, -0.401], [364.231, 569.092, -0.328], [289.053, 634.842, -0.205]]\nB: [[301.298, 665.695, -0.633], [284.255, 584.96, -0.505], [255.024, 783.231, -0.315], [245.543, 830.729, -0.175]]\nC: [[311.284, 836.779, -0.649], [370.864, 573.4, -0.497], [360.873, 715.839, -0.301], [249.474, 613.179, -0.21]]\nD: [[312.445, 705.589, -0.612], [310.539, 703.33, -0.479], [308.633, 701.071, -0.346], [306.729, 698.815, -0.214]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_168_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_168_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_168_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_168_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_168_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_168_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_168_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_168_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[342.032, 761.086, -0.593], [347.646, 679.25, -0.401], [364.231, 569.092, -0.328], [289.053, 634.842, -0.205]]\nB: [[301.298, 665.695, -0.633], [284.255, 584.96, -0.505], [255.024, 783.231, -0.315], [245.543, 830.729, -0.175]]\nC: [[311.284, 836.779, -0.649], [370.864, 573.4, -0.497], [360.873, 715.839, -0.301], [249.474, 613.179, -0.21]]\nD: [[312.445, 705.589, -0.612], [310.539, 703.33, -0.479], [308.633, 701.071, -0.346], [306.729, 698.815, -0.214]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[363.209, 1123.747, 1.102], [363.143, 1123.593, 1.085], [363.102, 1123.498, 1.068], [363.137, 1123.58, 1.052]]\nB: [[378.885, 1276.718, 1.19], [435.59, 1222.123, 1.009], [430.113, 989.283, 1.158], [375.367, 990.91, 0.956]]\nC: [[413.521, 1104.065, 1.115], [428.494, 917.554, 1.251], [320.18, 1021.737, 0.933], [318.09, 1005.13, 1.022]]\nD: [[427.259, 1339.216, 1.291], [315.569, 1079.127, 0.936], [304.042, 1194.504, 1.038], [323.022, 982.56, 0.905]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_169_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_169_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_169_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_169_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_169_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_169_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_169_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_169_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[363.209, 1123.747, 1.102], [363.143, 1123.593, 1.085], [363.102, 1123.498, 1.068], [363.137, 1123.58, 1.052]]\nB: [[378.885, 1276.718, 1.19], [435.59, 1222.123, 1.009], [430.113, 989.283, 1.158], [375.367, 990.91, 0.956]]\nC: [[413.521, 1104.065, 1.115], [428.494, 917.554, 1.251], [320.18, 1021.737, 0.933], [318.09, 1005.13, 1.022]]\nD: [[427.259, 1339.216, 1.291], [315.569, 1079.127, 0.936], [304.042, 1194.504, 1.038], [323.022, 982.56, 0.905]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1333.186, 1040.597, 0.635], [1333.186, 1040.597, 0.635], [1333.186, 1040.597, 0.635], [1333.186, 1040.597, 0.635]]\nB: [[1514.635, 950.684, 0.564], [1066.743, 1156.105, 0.658], [1204.49, 1019.642, 0.74], [1089.793, 1017.943, 0.569]]\nC: [[1321.867, 926.129, 0.741], [1261.374, 1241.754, 0.725], [1359.131, 1028.017, 0.564], [1440.895, 941.11, 0.737]]\nD: [[1471.054, 874.495, 0.591], [1148.049, 1089.103, 0.508], [1489.63, 929.92, 0.603], [1503.98, 1037.54, 0.682]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_170_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_170_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_170_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_170_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_170_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_170_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_170_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_170_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1333.186, 1040.597, 0.635], [1333.186, 1040.597, 0.635], [1333.186, 1040.597, 0.635], [1333.186, 1040.597, 0.635]]\nB: [[1514.635, 950.684, 0.564], [1066.743, 1156.105, 0.658], [1204.49, 1019.642, 0.74], [1089.793, 1017.943, 0.569]]\nC: [[1321.867, 926.129, 0.741], [1261.374, 1241.754, 0.725], [1359.131, 1028.017, 0.564], [1440.895, 941.11, 0.737]]\nD: [[1471.054, 874.495, 0.591], [1148.049, 1089.103, 0.508], [1489.63, 929.92, 0.603], [1503.98, 1037.54, 0.682]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[587.007, 1451.429, 1.011], [592.203, 1759.483, 1.117], [647.108, 1795.963, 1.03], [638.355, 1540.243, 0.93]]\nB: [[640.141, 1888.738, 0.846], [726.767, 1525.298, 0.951], [759.989, 1292.629, 0.84], [736.789, 1660.505, 0.979]]\nC: [[666.472, 1849.208, 0.854], [731.041, 1481.554, 0.835], [803.226, 1800.553, 1.04], [593.45, 1762.889, 1.042]]\nD: [[671.323, 1578.674, 0.957], [671.316, 1578.671, 0.964], [671.308, 1578.668, 0.97], [671.301, 1578.665, 0.976]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_171_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_171_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_171_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_171_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_171_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_171_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_171_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_171_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[587.007, 1451.429, 1.011], [592.203, 1759.483, 1.117], [647.108, 1795.963, 1.03], [638.355, 1540.243, 0.93]]\nB: [[640.141, 1888.738, 0.846], [726.767, 1525.298, 0.951], [759.989, 1292.629, 0.84], [736.789, 1660.505, 0.979]]\nC: [[666.472, 1849.208, 0.854], [731.041, 1481.554, 0.835], [803.226, 1800.553, 1.04], [593.45, 1762.889, 1.042]]\nD: [[671.323, 1578.674, 0.957], [671.316, 1578.671, 0.964], [671.308, 1578.668, 0.97], [671.301, 1578.665, 0.976]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[291.787, 679.069, -0.469], [277.905, 732.394, -0.336], [252.638, 766.267, -0.161], [332.611, 636.857, -0.009]]\nB: [[309.014, 658.598, -0.45], [335.072, 790.615, -0.333], [250.147, 699.934, -0.154], [283.681, 656.435, -0.007]]\nC: [[307.569, 699.998, -0.459], [305.228, 697.097, -0.309], [302.888, 694.195, -0.159], [300.547, 691.293, -0.008]]\nD: [[306.127, 672.26, -0.442], [332.611, 777.126, -0.361], [293.54, 777.55, -0.16], [277.878, 827.191, -0.008]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_172_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_172_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_172_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_172_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_172_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_172_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_172_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_172_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[291.787, 679.069, -0.469], [277.905, 732.394, -0.336], [252.638, 766.267, -0.161], [332.611, 636.857, -0.009]]\nB: [[309.014, 658.598, -0.45], [335.072, 790.615, -0.333], [250.147, 699.934, -0.154], [283.681, 656.435, -0.007]]\nC: [[307.569, 699.998, -0.459], [305.228, 697.097, -0.309], [302.888, 694.195, -0.159], [300.547, 691.293, -0.008]]\nD: [[306.127, 672.26, -0.442], [332.611, 777.126, -0.361], [293.54, 777.55, -0.16], [277.878, 827.191, -0.008]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[410.021, 1418.263, 1.006], [335.263, 1143.6, 0.762], [373.402, 1388.34, 1.144], [360.742, 1297.733, 1.1]]\nB: [[401.282, 1193.478, 0.862], [402.619, 1193.23, 0.782], [404.132, 1193.11, 1.001], [405.307, 1192.971, 1.1]]\nC: [[476.438, 1152.023, 0.752], [338.739, 1127.79, 0.816], [436.589, 1274.03, 1.097], [419.84, 1106.209, 1.0]]\nD: [[421.529, 1380.4, 0.797], [464.928, 1028.31, 0.794], [411.148, 983.52, 1.058], [384.373, 1245.097, 1.2]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_173_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_173_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_173_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_173_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_173_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_173_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_173_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_173_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[410.021, 1418.263, 1.006], [335.263, 1143.6, 0.762], [373.402, 1388.34, 1.144], [360.742, 1297.733, 1.1]]\nB: [[401.282, 1193.478, 0.862], [402.619, 1193.23, 0.782], [404.132, 1193.11, 1.001], [405.307, 1192.971, 1.1]]\nC: [[476.438, 1152.023, 0.752], [338.739, 1127.79, 0.816], [436.589, 1274.03, 1.097], [419.84, 1106.209, 1.0]]\nD: [[421.529, 1380.4, 0.797], [464.928, 1028.31, 0.794], [411.148, 983.52, 1.058], [384.373, 1245.097, 1.2]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[720.915, 1310.639, 0.073], [578.541, 1908.08, -0.015], [537.43, 1833.644, 0.24], [531.48, 1353.149, 0.504]]\nB: [[621.807, 1622.951, 0.061], [622.306, 1622.44, -0.014], [623.24, 1621.439, 0.236], [623.71, 1620.935, 0.436]]\nC: [[724.675, 1443.65, 0.065], [727.047, 1304.33, -0.013], [681.05, 1445.626, 0.193], [593.86, 1727.459, 0.372]]\nD: [[667.376, 1736.543, 0.069], [736.258, 1753.41, -0.016], [704.18, 1743.04, 0.25], [644.2, 1366.127, 0.492]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_174_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_174_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_174_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_174_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_174_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_174_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_174_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_174_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[720.915, 1310.639, 0.073], [578.541, 1908.08, -0.015], [537.43, 1833.644, 0.24], [531.48, 1353.149, 0.504]]\nB: [[621.807, 1622.951, 0.061], [622.306, 1622.44, -0.014], [623.24, 1621.439, 0.236], [623.71, 1620.935, 0.436]]\nC: [[724.675, 1443.65, 0.065], [727.047, 1304.33, -0.013], [681.05, 1445.626, 0.193], [593.86, 1727.459, 0.372]]\nD: [[667.376, 1736.543, 0.069], [736.258, 1753.41, -0.016], [704.18, 1743.04, 0.25], [644.2, 1366.127, 0.492]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[2009.229, 1014.34, 0.361], [1586.06, 754.207, 0.245], [2016.84, 904.343, 0.311], [2259.58, 852.389, 0.226]]\nB: [[1902.189, 877.268, 0.309], [1902.179, 877.284, 0.296], [1902.17, 877.299, 0.284], [1902.16, 877.315, 0.271]]\nC: [[1742.017, 880.394, 0.345], [2183.098, 837.873, 0.305], [2257.96, 877.436, 0.227], [1592.71, 1021.048, 0.257]]\nD: [[1584.307, 954.942, 0.354], [1730.467, 891.446, 0.254], [1805.84, 870.388, 0.252], [2140.35, 875.505, 0.278]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_175_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_175_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_175_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_175_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_175_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_175_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_175_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_175_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[2009.229, 1014.34, 0.361], [1586.06, 754.207, 0.245], [2016.84, 904.343, 0.311], [2259.58, 852.389, 0.226]]\nB: [[1902.189, 877.268, 0.309], [1902.179, 877.284, 0.296], [1902.17, 877.299, 0.284], [1902.16, 877.315, 0.271]]\nC: [[1742.017, 880.394, 0.345], [2183.098, 837.873, 0.305], [2257.96, 877.436, 0.227], [1592.71, 1021.048, 0.257]]\nD: [[1584.307, 954.942, 0.354], [1730.467, 891.446, 0.254], [1805.84, 870.388, 0.252], [2140.35, 875.505, 0.278]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[318.894, 1055.632, 0.551], [364.825, 1096.642, 0.53], [434.434, 1265.049, 0.623], [381.624, 937.181, 0.532]]\nB: [[473.765, 1023.067, 0.591], [358.411, 1214.0, 0.58], [399.844, 969.318, 0.568], [346.878, 1295.895, 0.701]]\nC: [[396.557, 1112.412, 0.545], [396.557, 1112.412, 0.57], [396.557, 1112.412, 0.595], [396.559, 1112.411, 0.612]]\nD: [[448.687, 1322.501, 0.469], [405.927, 1315.306, 0.58], [394.036, 899.204, 0.523], [445.892, 1047.925, 0.674]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_176_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_176_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_176_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_176_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_176_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_176_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_176_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_176_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[318.894, 1055.632, 0.551], [364.825, 1096.642, 0.53], [434.434, 1265.049, 0.623], [381.624, 937.181, 0.532]]\nB: [[473.765, 1023.067, 0.591], [358.411, 1214.0, 0.58], [399.844, 969.318, 0.568], [346.878, 1295.895, 0.701]]\nC: [[396.557, 1112.412, 0.545], [396.557, 1112.412, 0.57], [396.557, 1112.412, 0.595], [396.559, 1112.411, 0.612]]\nD: [[448.687, 1322.501, 0.469], [405.927, 1315.306, 0.58], [394.036, 899.204, 0.523], [445.892, 1047.925, 0.674]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[616.635, 1647.01, 0.068], [712.669, 1327.311, 0.03], [653.979, 1570.408, -0.012], [669.754, 1866.231, 0.168]]\nB: [[575.735, 1633.265, 0.067], [652.707, 1894.197, 0.04], [585.605, 1401.243, -0.011], [517.999, 1818.195, 0.152]]\nC: [[619.603, 1624.655, 0.071], [620.215, 1624.227, 0.03], [620.828, 1623.798, -0.012], [621.449, 1623.383, 0.146]]\nD: [[697.585, 1370.261, 0.08], [634.839, 1685.003, 0.02], [620.085, 1806.807, -0.014], [537.801, 1756.717, 0.169]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_177_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_177_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_177_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_177_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_177_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_177_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_177_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_177_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[616.635, 1647.01, 0.068], [712.669, 1327.311, 0.03], [653.979, 1570.408, -0.012], [669.754, 1866.231, 0.168]]\nB: [[575.735, 1633.265, 0.067], [652.707, 1894.197, 0.04], [585.605, 1401.243, -0.011], [517.999, 1818.195, 0.152]]\nC: [[619.603, 1624.655, 0.071], [620.215, 1624.227, 0.03], [620.828, 1623.798, -0.012], [621.449, 1623.383, 0.146]]\nD: [[697.585, 1370.261, 0.08], [634.839, 1685.003, 0.02], [620.085, 1806.807, -0.014], [537.801, 1756.717, 0.169]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[321.608, 1134.254, 0.47], [376.579, 1092.364, 0.561], [328.646, 1331.008, 0.5], [429.422, 1013.513, 0.542]]\nB: [[366.01, 1353.528, 0.41], [346.536, 1032.294, 0.437], [456.295, 1314.242, 0.49], [462.86, 1106.077, 0.501]]\nC: [[394.87, 1020.543, 0.46], [447.385, 1022.557, 0.479], [328.398, 1293.308, 0.63], [370.045, 1320.086, 0.474]]\nD: [[395.651, 1160.538, 0.51], [395.651, 1160.538, 0.535], [395.651, 1160.538, 0.56], [395.651, 1160.538, 0.585]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_178_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_178_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_178_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_178_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_178_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_178_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_178_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_178_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[321.608, 1134.254, 0.47], [376.579, 1092.364, 0.561], [328.646, 1331.008, 0.5], [429.422, 1013.513, 0.542]]\nB: [[366.01, 1353.528, 0.41], [346.536, 1032.294, 0.437], [456.295, 1314.242, 0.49], [462.86, 1106.077, 0.501]]\nC: [[394.87, 1020.543, 0.46], [447.385, 1022.557, 0.479], [328.398, 1293.308, 0.63], [370.045, 1320.086, 0.474]]\nD: [[395.651, 1160.538, 0.51], [395.651, 1160.538, 0.535], [395.651, 1160.538, 0.56], [395.651, 1160.538, 0.585]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[705.601, 1670.358, 1.653], [759.064, 1611.115, 1.64], [821.344, 1702.722, 1.626], [808.425, 1696.718, 1.401]]\nB: [[729.457, 1296.83, 1.225], [673.831, 1849.549, 1.6], [822.222, 1475.002, 1.59], [568.262, 1723.288, 1.529]]\nC: [[780.777, 1370.477, 1.602], [703.571, 1274.284, 1.37], [714.428, 1801.829, 1.419], [746.711, 1436.885, 1.447]]\nD: [[710.384, 1565.299, 1.507], [708.536, 1567.037, 1.44], [706.623, 1568.784, 1.524], [704.701, 1570.522, 1.457]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_179_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_179_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_179_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_179_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_179_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_179_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_179_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_179_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[705.601, 1670.358, 1.653], [759.064, 1611.115, 1.64], [821.344, 1702.722, 1.626], [808.425, 1696.718, 1.401]]\nB: [[729.457, 1296.83, 1.225], [673.831, 1849.549, 1.6], [822.222, 1475.002, 1.59], [568.262, 1723.288, 1.529]]\nC: [[780.777, 1370.477, 1.602], [703.571, 1274.284, 1.37], [714.428, 1801.829, 1.419], [746.711, 1436.885, 1.447]]\nD: [[710.384, 1565.299, 1.507], [708.536, 1567.037, 1.44], [706.623, 1568.784, 1.524], [704.701, 1570.522, 1.457]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[320.577, 1284.53, 0.932], [414.562, 975.368, 0.94], [402.331, 1119.979, 0.929], [395.463, 906.274, 0.959]]\nB: [[360.972, 1107.73, 0.916], [361.016, 1107.522, 0.816], [361.016, 1107.522, 0.816], [360.975, 1107.716, 1.016]]\nC: [[398.669, 1187.71, 1.007], [399.141, 1252.975, 0.956], [420.325, 1170.467, 0.694], [429.664, 938.089, 1.209]]\nD: [[297.101, 1040.56, 0.77], [316.568, 1235.213, 0.681], [321.404, 1279.995, 0.669], [348.852, 1148.111, 0.837]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_180_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_180_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_180_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_180_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_180_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_180_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_180_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_180_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[320.577, 1284.53, 0.932], [414.562, 975.368, 0.94], [402.331, 1119.979, 0.929], [395.463, 906.274, 0.959]]\nB: [[360.972, 1107.73, 0.916], [361.016, 1107.522, 0.816], [361.016, 1107.522, 0.816], [360.975, 1107.716, 1.016]]\nC: [[398.669, 1187.71, 1.007], [399.141, 1252.975, 0.956], [420.325, 1170.467, 0.694], [429.664, 938.089, 1.209]]\nD: [[297.101, 1040.56, 0.77], [316.568, 1235.213, 0.681], [321.404, 1279.995, 0.669], [348.852, 1148.111, 0.837]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[438.16, 1086.715, 0.696], [438.167, 1086.722, 0.834], [438.221, 1086.776, 0.858], [438.222, 1086.778, 0.851]]\nB: [[507.3, 1112.979, 0.562], [428.69, 1220.639, 0.948], [389.443, 1140.615, 0.714], [399.989, 1095.621, 0.887]]\nC: [[354.14, 1079.897, 0.71], [359.261, 923.891, 0.834], [367.923, 883.883, 0.807], [495.344, 1285.646, 0.707]]\nD: [[403.04, 1151.47, 0.76], [405.751, 1286.822, 0.742], [364.368, 898.997, 0.983], [413.957, 1207.042, 0.986]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_181_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_181_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_181_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_181_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_181_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_181_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_181_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_181_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[438.16, 1086.715, 0.696], [438.167, 1086.722, 0.834], [438.221, 1086.776, 0.858], [438.222, 1086.778, 0.851]]\nB: [[507.3, 1112.979, 0.562], [428.69, 1220.639, 0.948], [389.443, 1140.615, 0.714], [399.989, 1095.621, 0.887]]\nC: [[354.14, 1079.897, 0.71], [359.261, 923.891, 0.834], [367.923, 883.883, 0.807], [495.344, 1285.646, 0.707]]\nD: [[403.04, 1151.47, 0.76], [405.751, 1286.822, 0.742], [364.368, 898.997, 0.983], [413.957, 1207.042, 0.986]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1355.191, 1159.351, -0.246], [1395.66, 1152.84, -0.228], [1240.863, 981.042, -0.233], [1262.588, 994.473, -0.268]]\nB: [[1217.511, 1111.827, -0.25], [1078.13, 1020.65, -0.204], [1571.618, 1018.832, -0.231], [1379.544, 1182.701, -0.209]]\nC: [[1345.406, 1027.941, -0.246], [1345.406, 1027.941, -0.246], [1345.406, 1027.941, -0.246], [1345.406, 1027.941, -0.246]]\nD: [[1421.753, 850.708, -0.231], [1362.386, 1149.899, -0.264], [1385.927, 906.596, -0.27], [1544.629, 853.12, -0.225]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_182_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_182_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_182_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_182_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_182_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_182_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_182_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_182_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1355.191, 1159.351, -0.246], [1395.66, 1152.84, -0.228], [1240.863, 981.042, -0.233], [1262.588, 994.473, -0.268]]\nB: [[1217.511, 1111.827, -0.25], [1078.13, 1020.65, -0.204], [1571.618, 1018.832, -0.231], [1379.544, 1182.701, -0.209]]\nC: [[1345.406, 1027.941, -0.246], [1345.406, 1027.941, -0.246], [1345.406, 1027.941, -0.246], [1345.406, 1027.941, -0.246]]\nD: [[1421.753, 850.708, -0.231], [1362.386, 1149.899, -0.264], [1385.927, 906.596, -0.27], [1544.629, 853.12, -0.225]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1187.979, 1094.431, 0.392], [1347.424, 1034.148, 0.436], [1538.003, 902.017, 0.373], [1212.196, 1182.796, 0.356]]\nB: [[1108.77, 1168.85, 0.455], [1257.172, 1008.091, 0.364], [1442.783, 1234.413, 0.459], [1312.575, 918.52, 0.361]]\nC: [[1079.007, 1042.862, 0.339], [1523.203, 1091.192, 0.449], [1147.362, 1204.835, 0.348], [1308.995, 1207.074, 0.355]]\nD: [[1335.042, 1037.999, 0.407], [1335.042, 1037.999, 0.407], [1335.042, 1037.999, 0.407], [1335.042, 1037.999, 0.407]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_183_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_183_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_183_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_183_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_183_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_183_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_183_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_183_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1187.979, 1094.431, 0.392], [1347.424, 1034.148, 0.436], [1538.003, 902.017, 0.373], [1212.196, 1182.796, 0.356]]\nB: [[1108.77, 1168.85, 0.455], [1257.172, 1008.091, 0.364], [1442.783, 1234.413, 0.459], [1312.575, 918.52, 0.361]]\nC: [[1079.007, 1042.862, 0.339], [1523.203, 1091.192, 0.449], [1147.362, 1204.835, 0.348], [1308.995, 1207.074, 0.355]]\nD: [[1335.042, 1037.999, 0.407], [1335.042, 1037.999, 0.407], [1335.042, 1037.999, 0.407], [1335.042, 1037.999, 0.407]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[390.593, 1112.966, 0.519], [390.606, 1112.958, 0.557], [390.618, 1112.95, 0.594], [390.631, 1112.942, 0.632]]\nB: [[391.396, 942.794, 0.438], [401.44, 920.349, 0.468], [426.581, 916.13, 0.599], [338.619, 1261.56, 0.586]]\nC: [[374.135, 1327.311, 0.499], [345.631, 969.283, 0.639], [364.128, 913.86, 0.609], [339.45, 1013.046, 0.638]]\nD: [[447.459, 924.256, 0.485], [384.158, 1293.211, 0.601], [467.686, 901.08, 0.57], [450.581, 1198.823, 0.72]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_184_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_184_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_184_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_184_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_184_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_184_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_184_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_184_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[390.593, 1112.966, 0.519], [390.606, 1112.958, 0.557], [390.618, 1112.95, 0.594], [390.631, 1112.942, 0.632]]\nB: [[391.396, 942.794, 0.438], [401.44, 920.349, 0.468], [426.581, 916.13, 0.599], [338.619, 1261.56, 0.586]]\nC: [[374.135, 1327.311, 0.499], [345.631, 969.283, 0.639], [364.128, 913.86, 0.609], [339.45, 1013.046, 0.638]]\nD: [[447.459, 924.256, 0.485], [384.158, 1293.211, 0.601], [467.686, 901.08, 0.57], [450.581, 1198.823, 0.72]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1529.469, 775.588, 0.372], [1793.42, 970.233, 0.339], [2009.523, 974.973, 0.358], [1610.13, 949.492, 0.279]]\nB: [[1591.041, 893.016, 0.43], [1803.48, 918.619, 0.324], [1586.65, 1006.415, 0.274], [2124.951, 926.48, 0.338]]\nC: [[1905.651, 875.006, 0.371], [1905.64, 875.027, 0.347], [1905.629, 875.048, 0.323], [1905.617, 875.069, 0.299]]\nD: [[1763.75, 935.01, 0.421], [2101.6, 837.057, 0.378], [1529.348, 1031.722, 0.334], [1741.729, 804.694, 0.321]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_185_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_185_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_185_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_185_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_185_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_185_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_185_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_185_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1529.469, 775.588, 0.372], [1793.42, 970.233, 0.339], [2009.523, 974.973, 0.358], [1610.13, 949.492, 0.279]]\nB: [[1591.041, 893.016, 0.43], [1803.48, 918.619, 0.324], [1586.65, 1006.415, 0.274], [2124.951, 926.48, 0.338]]\nC: [[1905.651, 875.006, 0.371], [1905.64, 875.027, 0.347], [1905.629, 875.048, 0.323], [1905.617, 875.069, 0.299]]\nD: [[1763.75, 935.01, 0.421], [2101.6, 837.057, 0.378], [1529.348, 1031.722, 0.334], [1741.729, 804.694, 0.321]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1102.52, 1189.44, 1.15], [1247.531, 856.838, 0.847], [1591.837, 1070.296, 0.973], [1281.673, 1018.706, 0.914]]\nB: [[1378.756, 954.378, 1.005], [1282.349, 1065.012, 1.03], [1288.645, 985.253, 1.088], [1368.77, 1206.494, 1.161]]\nC: [[1330.066, 1046.334, 0.969], [1330.066, 1046.334, 0.969], [1330.066, 1046.334, 0.969], [1330.066, 1046.334, 0.969]]\nD: [[1490.554, 938.979, 1.093], [1292.466, 1009.582, 0.816], [1257.859, 968.601, 0.966], [1535.083, 927.732, 1.127]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_186_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_186_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_186_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_186_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_186_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_186_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_186_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_186_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1102.52, 1189.44, 1.15], [1247.531, 856.838, 0.847], [1591.837, 1070.296, 0.973], [1281.673, 1018.706, 0.914]]\nB: [[1378.756, 954.378, 1.005], [1282.349, 1065.012, 1.03], [1288.645, 985.253, 1.088], [1368.77, 1206.494, 1.161]]\nC: [[1330.066, 1046.334, 0.969], [1330.066, 1046.334, 0.969], [1330.066, 1046.334, 0.969], [1330.066, 1046.334, 0.969]]\nD: [[1490.554, 938.979, 1.093], [1292.466, 1009.582, 0.816], [1257.859, 968.601, 0.966], [1535.083, 927.732, 1.127]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[417.399, 1193.55, 0.994], [416.858, 1193.723, 1.044], [416.129, 1193.95, 1.119], [415.49, 1194.12, 1.144]]\nB: [[409.271, 1043.71, 0.802], [478.793, 1282.181, 1.098], [347.872, 1358.99, 1.243], [485.83, 1248.86, 0.927]]\nC: [[335.7, 1322.48, 1.133], [434.978, 1371.089, 0.975], [462.262, 1332.7, 0.913], [375.81, 1227.16, 1.032]]\nD: [[403.098, 1282.9, 0.834], [475.947, 968.443, 0.984], [492.927, 1029.78, 1.321], [332.54, 962.76, 1.165]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_187_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_187_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_187_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_187_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_187_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_187_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_187_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_187_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[417.399, 1193.55, 0.994], [416.858, 1193.723, 1.044], [416.129, 1193.95, 1.119], [415.49, 1194.12, 1.144]]\nB: [[409.271, 1043.71, 0.802], [478.793, 1282.181, 1.098], [347.872, 1358.99, 1.243], [485.83, 1248.86, 0.927]]\nC: [[335.7, 1322.48, 1.133], [434.978, 1371.089, 0.975], [462.262, 1332.7, 0.913], [375.81, 1227.16, 1.032]]\nD: [[403.098, 1282.9, 0.834], [475.947, 968.443, 0.984], [492.927, 1029.78, 1.321], [332.54, 962.76, 1.165]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1542.554, 1185.251, 1.339], [1408.096, 1145.054, 1.499], [1475.525, 890.578, 1.509], [1249.888, 965.619, 1.875]]\nB: [[1536.937, 1095.924, 1.505], [1199.25, 1245.415, 1.478], [1539.555, 1123.021, 1.621], [1138.513, 1047.666, 1.622]]\nC: [[1308.987, 1052.606, 1.402], [1310.095, 1053.915, 1.502], [1311.245, 1055.225, 1.598], [1312.378, 1056.412, 1.693]]\nD: [[1455.025, 1135.652, 1.649], [1113.162, 854.503, 1.292], [1160.009, 1154.438, 1.809], [1412.034, 1067.9, 1.717]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_188_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_188_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_188_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_188_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_188_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_188_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_188_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_188_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1542.554, 1185.251, 1.339], [1408.096, 1145.054, 1.499], [1475.525, 890.578, 1.509], [1249.888, 965.619, 1.875]]\nB: [[1536.937, 1095.924, 1.505], [1199.25, 1245.415, 1.478], [1539.555, 1123.021, 1.621], [1138.513, 1047.666, 1.622]]\nC: [[1308.987, 1052.606, 1.402], [1310.095, 1053.915, 1.502], [1311.245, 1055.225, 1.598], [1312.378, 1056.412, 1.693]]\nD: [[1455.025, 1135.652, 1.649], [1113.162, 854.503, 1.292], [1160.009, 1154.438, 1.809], [1412.034, 1067.9, 1.717]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1136.4, 1251.375, 0.964], [1098.34, 926.608, 0.962], [1167.04, 1063.589, 0.748], [1241.17, 957.965, 0.728]]\nB: [[1133.35, 933.225, 0.869], [1467.73, 1116.69, 0.711], [1336.16, 1216.158, 0.757], [1461.61, 1223.596, 0.736]]\nC: [[1339.79, 1239.08, 0.933], [1509.78, 946.412, 0.694], [1165.99, 1097.514, 0.848], [1235.1, 968.837, 0.75]]\nD: [[1264.56, 1079.653, 0.835], [1264.56, 1079.653, 0.836], [1264.56, 1079.653, 0.837], [1264.56, 1079.653, 0.837]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_189_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_189_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_189_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_189_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_189_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_189_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_189_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_189_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1136.4, 1251.375, 0.964], [1098.34, 926.608, 0.962], [1167.04, 1063.589, 0.748], [1241.17, 957.965, 0.728]]\nB: [[1133.35, 933.225, 0.869], [1467.73, 1116.69, 0.711], [1336.16, 1216.158, 0.757], [1461.61, 1223.596, 0.736]]\nC: [[1339.79, 1239.08, 0.933], [1509.78, 946.412, 0.694], [1165.99, 1097.514, 0.848], [1235.1, 968.837, 0.75]]\nD: [[1264.56, 1079.653, 0.835], [1264.56, 1079.653, 0.836], [1264.56, 1079.653, 0.837], [1264.56, 1079.653, 0.837]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[352.109, 1115.839, 0.635], [352.109, 1115.839, 0.814], [352.109, 1115.839, 0.993], [352.109, 1115.839, 0.801]]\nB: [[403.261, 1293.127, 0.727], [350.132, 1278.347, 0.888], [358.28, 1088.995, 1.055], [317.737, 1172.102, 0.72]]\nC: [[293.311, 1012.839, 0.541], [366.92, 1035.95, 0.854], [356.294, 946.374, 0.819], [390.703, 900.089, 0.781]]\nD: [[318.426, 1207.534, 0.602], [341.429, 1071.396, 0.779], [376.574, 1288.812, 1.059], [313.83, 1243.677, 0.781]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_190_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_190_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_190_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_190_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_190_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_190_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_190_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_190_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[352.109, 1115.839, 0.635], [352.109, 1115.839, 0.814], [352.109, 1115.839, 0.993], [352.109, 1115.839, 0.801]]\nB: [[403.261, 1293.127, 0.727], [350.132, 1278.347, 0.888], [358.28, 1088.995, 1.055], [317.737, 1172.102, 0.72]]\nC: [[293.311, 1012.839, 0.541], [366.92, 1035.95, 0.854], [356.294, 946.374, 0.819], [390.703, 900.089, 0.781]]\nD: [[318.426, 1207.534, 0.602], [341.429, 1071.396, 0.779], [376.574, 1288.812, 1.059], [313.83, 1243.677, 0.781]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[405.833, 996.81, 0.95], [355.845, 1117.198, 1.04], [354.65, 947.797, 0.81], [358.045, 1006.86, 0.9]]\nB: [[464.397, 1283.6, 0.75], [328.65, 936.28, 0.941], [365.855, 941.15, 0.91], [332.144, 978.284, 0.89]]\nC: [[390.721, 1120.16, 0.88], [390.397, 1119.603, 0.905], [390.144, 1119.015, 0.93], [389.874, 1118.388, 1.08]]\nD: [[338.901, 1032.25, 0.92], [452.113, 1110.634, 1.086], [401.774, 1235.48, 1.09], [348.321, 1273.502, 1.06]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_191_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_191_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_191_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_191_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_191_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_191_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_191_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_191_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[405.833, 996.81, 0.95], [355.845, 1117.198, 1.04], [354.65, 947.797, 0.81], [358.045, 1006.86, 0.9]]\nB: [[464.397, 1283.6, 0.75], [328.65, 936.28, 0.941], [365.855, 941.15, 0.91], [332.144, 978.284, 0.89]]\nC: [[390.721, 1120.16, 0.88], [390.397, 1119.603, 0.905], [390.144, 1119.015, 0.93], [389.874, 1118.388, 1.08]]\nD: [[338.901, 1032.25, 0.92], [452.113, 1110.634, 1.086], [401.774, 1235.48, 1.09], [348.321, 1273.502, 1.06]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[310.056, 702.514, -0.534], [308.348, 700.438, -0.379], [306.644, 698.366, -0.226], [299.451, 689.618, 0.29]]\nB: [[311.979, 810.175, -0.565], [260.058, 694.023, -0.342], [272.393, 835.115, -0.249], [357.323, 587.99, 0.25]]\nC: [[344.874, 770.786, -0.524], [341.8, 753.476, -0.452], [326.128, 633.003, -0.265], [319.66, 701.069, 0.24]]\nD: [[365.685, 814.947, -0.568], [301.272, 761.237, -0.383], [347.211, 755.567, -0.186], [320.282, 634.704, 0.25]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_192_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_192_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_192_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_192_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_192_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_192_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_192_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_192_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[310.056, 702.514, -0.534], [308.348, 700.438, -0.379], [306.644, 698.366, -0.226], [299.451, 689.618, 0.29]]\nB: [[311.979, 810.175, -0.565], [260.058, 694.023, -0.342], [272.393, 835.115, -0.249], [357.323, 587.99, 0.25]]\nC: [[344.874, 770.786, -0.524], [341.8, 753.476, -0.452], [326.128, 633.003, -0.265], [319.66, 701.069, 0.24]]\nD: [[365.685, 814.947, -0.568], [301.272, 761.237, -0.383], [347.211, 755.567, -0.186], [320.282, 634.704, 0.25]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[410.458, 1100.432, 0.572], [415.671, 1229.197, 0.401], [335.629, 978.571, 0.674], [395.098, 1300.552, 0.478]]\nB: [[401.269, 1173.484, 0.482], [401.265, 1173.449, 0.482], [401.261, 1173.415, 0.582], [401.213, 1173.399, 0.569]]\nC: [[344.262, 1103.389, 0.405], [334.337, 967.834, 0.523], [322.213, 1142.996, 0.661], [438.778, 1322.064, 0.463]]\nD: [[444.209, 1082.14, 0.539], [424.03, 1030.374, 0.393], [373.133, 1212.622, 0.685], [326.49, 1003.549, 0.642]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_193_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_193_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_193_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_193_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_193_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_193_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_193_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_193_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[410.458, 1100.432, 0.572], [415.671, 1229.197, 0.401], [335.629, 978.571, 0.674], [395.098, 1300.552, 0.478]]\nB: [[401.269, 1173.484, 0.482], [401.265, 1173.449, 0.482], [401.261, 1173.415, 0.582], [401.213, 1173.399, 0.569]]\nC: [[344.262, 1103.389, 0.405], [334.337, 967.834, 0.523], [322.213, 1142.996, 0.661], [438.778, 1322.064, 0.463]]\nD: [[444.209, 1082.14, 0.539], [424.03, 1030.374, 0.393], [373.133, 1212.622, 0.685], [326.49, 1003.549, 0.642]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1266.882, 1077.846, 0.755], [1266.882, 1077.846, 0.755], [1266.882, 1077.846, 0.755], [1266.882, 1077.846, 0.755]]\nB: [[1271.739, 1048.404, 0.906], [1320.835, 1026.02, 0.819], [1396.815, 1235.0, 0.773], [1277.754, 1106.806, 0.618]]\nC: [[1317.337, 1260.195, 0.841], [1225.296, 1191.372, 0.797], [1170.961, 1158.338, 0.874], [1386.483, 1261.547, 0.715]]\nD: [[1218.794, 899.374, 0.833], [1062.39, 1230.912, 0.743], [1031.716, 1194.236, 0.798], [1438.585, 1159.315, 0.751]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_194_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_194_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_194_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_194_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_194_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_194_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_194_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_194_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1266.882, 1077.846, 0.755], [1266.882, 1077.846, 0.755], [1266.882, 1077.846, 0.755], [1266.882, 1077.846, 0.755]]\nB: [[1271.739, 1048.404, 0.906], [1320.835, 1026.02, 0.819], [1396.815, 1235.0, 0.773], [1277.754, 1106.806, 0.618]]\nC: [[1317.337, 1260.195, 0.841], [1225.296, 1191.372, 0.797], [1170.961, 1158.338, 0.874], [1386.483, 1261.547, 0.715]]\nD: [[1218.794, 899.374, 0.833], [1062.39, 1230.912, 0.743], [1031.716, 1194.236, 0.798], [1438.585, 1159.315, 0.751]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1416.446, 917.476, 0.369], [1133.347, 1173.31, 0.385], [1116.37, 899.476, 0.388], [1512.12, 1114.073, 0.426]]\nB: [[1278.607, 1028.824, 0.314], [1281.473, 1031.847, 0.364], [1285.19, 1035.681, 0.414], [1288.26, 1038.767, 0.464]]\nC: [[1110.832, 1041.63, 0.259], [1144.062, 983.891, 0.355], [1120.76, 991.772, 0.443], [1528.04, 941.905, 0.384]]\nD: [[1057.696, 870.984, 0.36], [1077.37, 1211.58, 0.316], [1049.97, 1110.459, 0.384], [1427.8, 977.845, 0.531]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_195_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_195_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_195_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_195_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_195_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_195_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_195_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_195_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1416.446, 917.476, 0.369], [1133.347, 1173.31, 0.385], [1116.37, 899.476, 0.388], [1512.12, 1114.073, 0.426]]\nB: [[1278.607, 1028.824, 0.314], [1281.473, 1031.847, 0.364], [1285.19, 1035.681, 0.414], [1288.26, 1038.767, 0.464]]\nC: [[1110.832, 1041.63, 0.259], [1144.062, 983.891, 0.355], [1120.76, 991.772, 0.443], [1528.04, 941.905, 0.384]]\nD: [[1057.696, 870.984, 0.36], [1077.37, 1211.58, 0.316], [1049.97, 1110.459, 0.384], [1427.8, 977.845, 0.531]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[617.725, 1759.21, -0.3], [551.267, 1929.565, -0.167], [709.584, 1522.655, -0.14], [704.467, 1583.292, -0.112]]\nB: [[650.737, 1625.23, -0.3], [651.288, 1624.989, -0.175], [651.844, 1624.758, -0.15], [652.374, 1624.474, -0.125]]\nC: [[757.657, 1791.25, -0.3], [596.205, 1710.113, -0.196], [594.666, 1578.119, -0.17], [645.182, 1918.374, -0.147]]\nD: [[672.039, 1335.14, -0.3], [676.19, 1821.439, -0.205], [711.246, 1830.07, -0.17], [653.306, 1617.612, -0.115]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_196_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_196_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_196_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_196_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_196_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_196_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_196_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_196_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[617.725, 1759.21, -0.3], [551.267, 1929.565, -0.167], [709.584, 1522.655, -0.14], [704.467, 1583.292, -0.112]]\nB: [[650.737, 1625.23, -0.3], [651.288, 1624.989, -0.175], [651.844, 1624.758, -0.15], [652.374, 1624.474, -0.125]]\nC: [[757.657, 1791.25, -0.3], [596.205, 1710.113, -0.196], [594.666, 1578.119, -0.17], [645.182, 1918.374, -0.147]]\nD: [[672.039, 1335.14, -0.3], [676.19, 1821.439, -0.205], [711.246, 1830.07, -0.17], [653.306, 1617.612, -0.115]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[437.74, 1143.27, 0.674], [451.18, 1139.1, 0.835], [366.614, 1128.63, 0.978], [348.424, 1174.882, 0.985]]\nB: [[462.69, 887.14, 0.851], [342.59, 1042.19, 0.759], [433.546, 1124.124, 0.983], [359.663, 1221.9, 0.894]]\nC: [[398.14, 1103.34, 0.828], [398.08, 1103.37, 0.834], [398.005, 1103.406, 0.891], [398.065, 1103.387, 0.875]]\nD: [[378.04, 1027.98, 0.775], [428.89, 1003.86, 0.777], [374.706, 954.311, 1.028], [402.006, 1268.493, 0.732]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_197_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_197_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_197_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_197_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_197_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_197_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_197_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_197_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[437.74, 1143.27, 0.674], [451.18, 1139.1, 0.835], [366.614, 1128.63, 0.978], [348.424, 1174.882, 0.985]]\nB: [[462.69, 887.14, 0.851], [342.59, 1042.19, 0.759], [433.546, 1124.124, 0.983], [359.663, 1221.9, 0.894]]\nC: [[398.14, 1103.34, 0.828], [398.08, 1103.37, 0.834], [398.005, 1103.406, 0.891], [398.065, 1103.387, 0.875]]\nD: [[378.04, 1027.98, 0.775], [428.89, 1003.86, 0.777], [374.706, 954.311, 1.028], [402.006, 1268.493, 0.732]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[312.42, 996.53, 0.734], [392.368, 1071.586, 1.006], [362.03, 997.691, 1.059], [395.055, 894.894, 0.96]]\nB: [[292.47, 1178.83, 0.536], [385.591, 1223.459, 0.952], [347.65, 1157.122, 1.11], [325.064, 1035.946, 1.008]]\nC: [[424.62, 956.05, 0.586], [414.477, 1277.953, 1.04], [360.719, 916.865, 1.121], [431.128, 1236.879, 1.07]]\nD: [[364.33, 1100.33, 0.667], [364.332, 1100.333, 0.879], [364.336, 1100.342, 1.067], [364.336, 1100.342, 0.917]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_198_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_198_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_198_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_198_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_198_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_198_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_198_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_198_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[312.42, 996.53, 0.734], [392.368, 1071.586, 1.006], [362.03, 997.691, 1.059], [395.055, 894.894, 0.96]]\nB: [[292.47, 1178.83, 0.536], [385.591, 1223.459, 0.952], [347.65, 1157.122, 1.11], [325.064, 1035.946, 1.008]]\nC: [[424.62, 956.05, 0.586], [414.477, 1277.953, 1.04], [360.719, 916.865, 1.121], [431.128, 1236.879, 1.07]]\nD: [[364.33, 1100.33, 0.667], [364.332, 1100.333, 0.879], [364.336, 1100.342, 1.067], [364.336, 1100.342, 0.917]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Tracking"}
{"source": "NuScenes_threed_Object_Tracking", "options": "A: [[1802.663, 947.701, 0.425], [1936.882, 764.775, 0.32], [1669.337, 970.661, 0.35], [2083.327, 843.762, 0.422]]\nB: [[1895.725, 877.102, 0.355], [1895.725, 877.102, 0.34], [1895.725, 877.102, 0.39], [1895.773, 877.087, 0.415]]\nC: [[2260.495, 1033.212, 0.352], [1682.669, 730.505, 0.36], [1808.702, 968.32, 0.35], [2274.387, 847.273, 0.38]]\nD: [[2228.008, 726.803, 0.308], [1759.634, 872.261, 0.3], [2044.819, 846.131, 0.41], [2237.11, 733.52, 0.386]]", "visual_input_component": "LiDAR depth image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_199_0.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_199_1.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_199_2.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_199_3.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_199_4.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_199_5.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_199_6.png", "3D-spatial/threeD_Object_Tracking/threeD_Object_Tracking_199_7.png"], "question": "Given a sequence of RGB and LiDAR depth images capturing object motion over time, please track the movement of the object outlined in the RGB images. In the LiDAR depth images, LiDAR points were projected back to the corresponding RGB images. The output should be in the format of a sequence of 3D positions, i.e., [x, y, z], which represents the gravity center of the 3D bounding boxes in meters of the obejct, with respect to the global coordinate system.", "context": "Your task is to track the movement of objects in 3D space across multiple frames. \nSelect from the following choices.\nA: [[1802.663, 947.701, 0.425], [1936.882, 764.775, 0.32], [1669.337, 970.661, 0.35], [2083.327, 843.762, 0.422]]\nB: [[1895.725, 877.102, 0.355], [1895.725, 877.102, 0.34], [1895.725, 877.102, 0.39], [1895.773, 877.087, 0.415]]\nC: [[2260.495, 1033.212, 0.352], [1682.669, 730.505, 0.36], [1808.702, 968.32, 0.35], [2274.387, 847.273, 0.38]]\nD: [[2228.008, 726.803, 0.308], [1759.634, 872.261, 0.3], [2044.819, 846.131, 0.41], [2237.11, 733.52, 0.386]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Tracking"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: bin\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_0_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_0_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_0_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_0_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_0_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_0_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: bin\nD: sink"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: box\nB: sink\nC: cabinet\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_1_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_1_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_1_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_1_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_1_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_1_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: box\nB: sink\nC: cabinet\nD: shelf"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: bag\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_2_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_2_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_2_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_2_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_2_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_2_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: bag\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: box\nB: sink\nC: chair\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_3_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_3_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_3_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_3_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_3_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_3_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: box\nB: sink\nC: chair\nD: shelf"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: sofa\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_4_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_4_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_4_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_4_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_4_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_4_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: sofa\nC: bed\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: cabinet\nC: desk\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_5_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_5_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_5_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_5_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_5_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_5_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: cabinet\nC: desk\nD: sink"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: desk\nB: cabinet\nC: shelf\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_6_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_6_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_6_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_6_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_6_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_6_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: cabinet\nC: shelf\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sink\nC: bag\nD: bin", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_7_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_7_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_7_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_7_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_7_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_7_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sink\nC: bag\nD: bin"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: bin\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_8_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_8_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_8_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_8_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_8_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_8_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: bin\nD: display"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sofa\nC: sink\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_9_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_9_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_9_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_9_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_9_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_9_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sofa\nC: sink\nD: bag"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: shelf\nC: door\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_10_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_10_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_10_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_10_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_10_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_10_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: shelf\nC: door\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: desk\nB: chair\nC: sofa\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_11_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_11_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_11_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_11_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_11_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_11_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: chair\nC: sofa\nD: toilet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: shelf\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_12_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_12_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_12_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_12_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_12_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_12_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: shelf\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: cabinet\nC: desk\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_13_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_13_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_13_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_13_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_13_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_13_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: cabinet\nC: desk\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: shelf\nD: box", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_14_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_14_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_14_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_14_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_14_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_14_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: shelf\nD: box"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: chair\nC: cabinet\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_15_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_15_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_15_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_15_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_15_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_15_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: chair\nC: cabinet\nD: sofa"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bag\nC: cabinet\nD: door", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_16_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_16_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_16_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_16_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_16_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_16_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bag\nC: cabinet\nD: door"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: table\nD: pillow", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_17_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_17_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_17_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_17_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_17_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_17_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: table\nD: pillow"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: table\nB: sink\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_18_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_18_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_18_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_18_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_18_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_18_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: sink\nC: bed\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bag\nC: sink\nD: box", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_19_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_19_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_19_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_19_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_19_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_19_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bag\nC: sink\nD: box"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sink\nC: shelf\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_20_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_20_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_20_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_20_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_20_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_20_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sink\nC: shelf\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sofa\nB: cabinet\nC: chair\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_21_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_21_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_21_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_21_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_21_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_21_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: cabinet\nC: chair\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: bed\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_22_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_22_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_22_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_22_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_22_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_22_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: bed\nD: display"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: shelf\nC: bin\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_23_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_23_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_23_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_23_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_23_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_23_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: shelf\nC: bin\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: display\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_24_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_24_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_24_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_24_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_24_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_24_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: display\nD: shelf"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: pillow\nC: chair\nD: desk", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_25_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_25_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_25_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_25_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_25_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_25_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: pillow\nC: chair\nD: desk"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: pillow\nB: shelf\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_26_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_26_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_26_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_26_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_26_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_26_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: pillow\nB: shelf\nC: sink\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: shelf\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_27_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_27_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_27_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_27_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_27_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_27_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: shelf\nD: table"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: bag\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_28_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_28_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_28_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_28_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_28_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_28_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: bag\nD: sink"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: cabinet\nC: sofa\nD: desk", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_29_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_29_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_29_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_29_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_29_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_29_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: cabinet\nC: sofa\nD: desk"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: chair\nC: door\nD: pillow", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_30_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_30_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_30_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_30_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_30_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_30_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: chair\nC: door\nD: pillow"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: display\nC: bed\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_31_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_31_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_31_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_31_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_31_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_31_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: display\nC: bed\nD: sofa"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: pillow\nB: sofa\nC: cabinet\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_32_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_32_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_32_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_32_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_32_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_32_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: pillow\nB: sofa\nC: cabinet\nD: toilet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: table\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_33_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_33_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_33_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_33_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_33_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_33_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: table\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bag\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_34_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_34_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_34_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_34_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_34_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_34_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bag\nC: sink\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: bin\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_35_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_35_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_35_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_35_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_35_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_35_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: bin\nD: sink"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: door\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_36_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_36_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_36_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_36_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_36_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_36_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: door\nD: bag"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: cabinet\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_37_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_37_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_37_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_37_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_37_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_37_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: cabinet\nC: sink\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: sink\nC: cabinet\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_38_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_38_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_38_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_38_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_38_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_38_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: sink\nC: cabinet\nD: bag"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sofa\nB: cabinet\nC: bed\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_39_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_39_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_39_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_39_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_39_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_39_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: cabinet\nC: bed\nD: shelf"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: desk\nC: shelf\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_40_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_40_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_40_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_40_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_40_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_40_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: desk\nC: shelf\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: bed\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_41_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_41_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_41_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_41_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_41_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_41_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: bed\nD: table"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: table\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_42_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_42_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_42_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_42_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_42_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_42_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: table\nC: bed\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: cabinet\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_43_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_43_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_43_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_43_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_43_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_43_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: cabinet\nD: shelf"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: bed\nC: cabinet\nD: door", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_44_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_44_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_44_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_44_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_44_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_44_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: bed\nC: cabinet\nD: door"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: sink\nC: cabinet\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_45_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_45_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_45_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_45_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_45_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_45_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: sink\nC: cabinet\nD: table"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: toilet\nC: chair\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_46_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_46_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_46_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_46_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_46_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_46_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: toilet\nC: chair\nD: table"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: sink\nC: cabinet\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_47_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_47_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_47_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_47_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_47_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_47_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: sink\nC: cabinet\nD: bag"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: sink\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_48_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_48_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_48_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_48_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_48_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_48_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: sink\nC: bed\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: desk\nB: shelf\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_49_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_49_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_49_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_49_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_49_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_49_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: shelf\nC: bed\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bag\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_50_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_50_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_50_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_50_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_50_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_50_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bag\nC: bed\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bag\nC: sink\nD: box", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_51_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_51_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_51_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_51_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_51_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_51_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bag\nC: sink\nD: box"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: box\nB: display\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_52_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_52_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_52_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_52_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_52_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_52_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: box\nB: display\nC: bed\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: cabinet\nC: bed\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_53_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_53_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_53_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_53_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_53_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_53_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: cabinet\nC: bed\nD: display"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sink\nC: bag\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_54_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_54_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_54_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_54_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_54_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_54_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sink\nC: bag\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: box\nB: cabinet\nC: bed\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_55_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_55_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_55_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_55_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_55_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_55_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: box\nB: cabinet\nC: bed\nD: sink"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: door\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_56_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_56_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_56_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_56_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_56_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_56_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: door\nC: bed\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: chair\nD: bin", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_57_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_57_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_57_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_57_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_57_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_57_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: chair\nD: bin"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: sink\nC: cabinet\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_58_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_58_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_58_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_58_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_58_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_58_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: sink\nC: cabinet\nD: bag"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: box\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_59_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_59_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_59_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_59_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_59_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_59_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: box\nD: table"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sofa\nB: cabinet\nC: sink\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_60_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_60_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_60_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_60_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_60_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_60_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: cabinet\nC: sink\nD: display"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bag\nC: sink\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_61_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_61_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_61_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_61_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_61_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_61_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bag\nC: sink\nD: display"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: cabinet\nC: sofa\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_62_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_62_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_62_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_62_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_62_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_62_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: cabinet\nC: sofa\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: box\nC: chair\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_63_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_63_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_63_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_63_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_63_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_63_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: box\nC: chair\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: display\nC: chair\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_64_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_64_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_64_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_64_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_64_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_64_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: display\nC: chair\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bag\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_65_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_65_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_65_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_65_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_65_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_65_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bag\nC: bed\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: cabinet\nC: display\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_66_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_66_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_66_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_66_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_66_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_66_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: cabinet\nC: display\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: pillow\nC: cabinet\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_67_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_67_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_67_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_67_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_67_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_67_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: pillow\nC: cabinet\nD: toilet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: bed\nC: door\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_68_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_68_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_68_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_68_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_68_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_68_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: bed\nC: door\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bin\nC: cabinet\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_69_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_69_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_69_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_69_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_69_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_69_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bin\nC: cabinet\nD: display"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: shelf\nC: chair\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_70_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_70_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_70_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_70_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_70_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_70_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: shelf\nC: chair\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: chair\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_71_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_71_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_71_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_71_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_71_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_71_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: chair\nD: sink"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: shelf\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_72_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_72_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_72_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_72_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_72_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_72_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: shelf\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: shelf\nD: pillow", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_73_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_73_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_73_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_73_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_73_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_73_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: shelf\nD: pillow"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: toilet\nB: bag\nC: cabinet\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_74_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_74_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_74_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_74_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_74_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_74_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: toilet\nB: bag\nC: cabinet\nD: sink"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bin\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_75_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_75_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_75_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_75_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_75_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_75_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bin\nC: sink\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: bed\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_76_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_76_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_76_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_76_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_76_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_76_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: bed\nD: display"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: cabinet\nC: bed\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_77_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_77_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_77_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_77_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_77_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_77_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: cabinet\nC: bed\nD: sofa"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: display\nC: cabinet\nD: bin", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_78_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_78_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_78_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_78_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_78_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_78_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: display\nC: cabinet\nD: bin"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: bag\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_79_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_79_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_79_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_79_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_79_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_79_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: bag\nD: sink"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: sink\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_80_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_80_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_80_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_80_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_80_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_80_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: sink\nC: bed\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sofa\nB: display\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_81_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_81_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_81_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_81_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_81_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_81_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: display\nC: sink\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: display\nC: shelf\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_82_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_82_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_82_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_82_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_82_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_82_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: display\nC: shelf\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: sofa\nC: chair\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_83_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_83_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_83_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_83_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_83_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_83_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: sofa\nC: chair\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: table\nB: cabinet\nC: bed\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_84_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_84_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_84_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_84_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_84_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_84_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: cabinet\nC: bed\nD: sink"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: box\nC: cabinet\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_85_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_85_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_85_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_85_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_85_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_85_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: box\nC: cabinet\nD: sink"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: bin\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_86_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_86_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_86_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_86_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_86_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_86_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: bin\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: bag\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_87_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_87_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_87_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_87_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_87_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_87_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: bag\nD: display"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: chair\nC: door\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_88_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_88_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_88_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_88_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_88_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_88_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: chair\nC: door\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: sink\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_89_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_89_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_89_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_89_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_89_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_89_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: sink\nD: sofa"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: chair\nC: cabinet\nD: desk", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_90_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_90_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_90_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_90_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_90_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_90_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: chair\nC: cabinet\nD: desk"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sink\nC: bag\nD: box", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_91_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_91_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_91_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_91_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_91_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_91_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sink\nC: bag\nD: box"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: display\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_92_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_92_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_92_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_92_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_92_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_92_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: display\nC: sink\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: sink\nC: cabinet\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_93_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_93_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_93_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_93_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_93_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_93_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: sink\nC: cabinet\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sink\nC: chair\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_94_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_94_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_94_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_94_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_94_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_94_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sink\nC: chair\nD: display"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: desk\nB: bed\nC: chair\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_95_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_95_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_95_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_95_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_95_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_95_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: bed\nC: chair\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sofa\nC: shelf\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_96_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_96_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_96_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_96_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_96_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_96_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sofa\nC: shelf\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: bin\nC: cabinet\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_97_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_97_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_97_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_97_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_97_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_97_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: bin\nC: cabinet\nD: sofa"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sink\nC: bed\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_98_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_98_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_98_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_98_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_98_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_98_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sink\nC: bed\nD: bag"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: toilet\nC: bag\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_99_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_99_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_99_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_99_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_99_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_99_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: toilet\nC: bag\nD: sink"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: chair\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_100_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_100_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_100_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_100_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_100_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_100_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: chair\nC: bed\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bin\nC: cabinet\nD: box", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_101_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_101_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_101_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_101_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_101_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_101_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bin\nC: cabinet\nD: box"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bin\nC: bag\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_102_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_102_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_102_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_102_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_102_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_102_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bin\nC: bag\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: toilet\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_103_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_103_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_103_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_103_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_103_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_103_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: toilet\nD: display"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: bed\nC: table\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_104_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_104_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_104_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_104_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_104_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_104_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: bed\nC: table\nD: shelf"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: table\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_105_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_105_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_105_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_105_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_105_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_105_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: table\nC: sink\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bin\nC: bed\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_106_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_106_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_106_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_106_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_106_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_106_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bin\nC: bed\nD: sink"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: chair\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_107_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_107_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_107_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_107_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_107_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_107_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: chair\nD: shelf"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: desk\nB: chair\nC: bin\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_108_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_108_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_108_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_108_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_108_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_108_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: chair\nC: bin\nD: display"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: display\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_109_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_109_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_109_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_109_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_109_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_109_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: display\nC: sink\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: cabinet\nC: bin\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_110_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_110_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_110_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_110_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_110_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_110_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: cabinet\nC: bin\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: bed\nC: sofa\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_111_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_111_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_111_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_111_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_111_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_111_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: bed\nC: sofa\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: table\nC: bed\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_112_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_112_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_112_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_112_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_112_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_112_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: table\nC: bed\nD: toilet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: shelf\nC: table\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_113_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_113_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_113_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_113_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_113_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_113_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: shelf\nC: table\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: table\nC: chair\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_114_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_114_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_114_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_114_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_114_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_114_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: table\nC: chair\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: box\nC: toilet\nD: pillow", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_115_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_115_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_115_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_115_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_115_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_115_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: box\nC: toilet\nD: pillow"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: door\nC: bag\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_116_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_116_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_116_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_116_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_116_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_116_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: door\nC: bag\nD: sink"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: desk\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_117_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_117_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_117_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_117_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_117_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_117_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: desk\nC: bed\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: table\nB: sink\nC: cabinet\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_118_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_118_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_118_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_118_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_118_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_118_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: sink\nC: cabinet\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: bed\nC: bag\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_119_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_119_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_119_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_119_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_119_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_119_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: bed\nC: bag\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: cabinet\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_120_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_120_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_120_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_120_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_120_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_120_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: cabinet\nC: sink\nD: bed"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: pillow\nB: bed\nC: cabinet\nD: background", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_121_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_121_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_121_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_121_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_121_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_121_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: pillow\nB: bed\nC: cabinet\nD: background"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: pillow\nB: sofa\nC: bed\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_122_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_122_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_122_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_122_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_122_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_122_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: pillow\nB: sofa\nC: bed\nD: chair"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: bed\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_123_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_123_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_123_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_123_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_123_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_123_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: bed\nC: sink\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: shelf\nD: box", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_124_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_124_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_124_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_124_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_124_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_124_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: shelf\nD: box"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: shelf\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_125_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_125_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_125_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_125_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_125_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_125_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: shelf\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: cabinet\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_126_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_126_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_126_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_126_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_126_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_126_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: cabinet\nD: bag"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: door\nB: cabinet\nC: sink\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_127_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_127_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_127_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_127_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_127_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_127_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: door\nB: cabinet\nC: sink\nD: shelf"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: toilet\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_128_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_128_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_128_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_128_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_128_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_128_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: toilet\nD: sink"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bed\nC: desk\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_129_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_129_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_129_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_129_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_129_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_129_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bed\nC: desk\nD: shelf"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sink\nC: bed\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_130_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_130_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_130_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_130_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_130_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_130_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sink\nC: bed\nD: bag"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sofa\nB: cabinet\nC: toilet\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_131_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_131_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_131_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_131_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_131_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_131_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: cabinet\nC: toilet\nD: shelf"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: bag\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_132_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_132_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_132_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_132_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_132_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_132_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: bag\nD: sink"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: cabinet\nC: display\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_133_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_133_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_133_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_133_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_133_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_133_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: cabinet\nC: display\nD: sink"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: bag\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_134_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_134_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_134_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_134_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_134_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_134_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: bag\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: cabinet\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_135_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_135_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_135_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_135_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_135_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_135_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: cabinet\nD: shelf"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: toilet\nB: cabinet\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_136_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_136_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_136_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_136_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_136_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_136_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: toilet\nB: cabinet\nC: sink\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: pillow\nB: display\nC: chair\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_137_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_137_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_137_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_137_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_137_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_137_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: pillow\nB: display\nC: chair\nD: sink"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: cabinet\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_138_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_138_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_138_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_138_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_138_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_138_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: cabinet\nD: chair"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: box\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_139_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_139_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_139_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_139_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_139_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_139_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: box\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: display\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_140_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_140_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_140_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_140_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_140_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_140_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: display\nC: sink\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: table\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_141_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_141_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_141_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_141_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_141_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_141_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: table\nD: sink"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: shelf\nC: bed\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_142_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_142_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_142_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_142_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_142_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_142_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: shelf\nC: bed\nD: sofa"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: box\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_143_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_143_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_143_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_143_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_143_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_143_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: box\nC: sink\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: cabinet\nC: shelf\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_144_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_144_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_144_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_144_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_144_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_144_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: cabinet\nC: shelf\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: shelf\nC: chair\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_145_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_145_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_145_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_145_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_145_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_145_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: shelf\nC: chair\nD: table"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: box\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_146_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_146_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_146_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_146_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_146_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_146_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: box\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: desk\nB: bed\nC: cabinet\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_147_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_147_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_147_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_147_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_147_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_147_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: bed\nC: cabinet\nD: chair"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: bed\nC: cabinet\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_148_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_148_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_148_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_148_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_148_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_148_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: bed\nC: cabinet\nD: sofa"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: display\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_149_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_149_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_149_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_149_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_149_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_149_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: display\nD: sink"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: sink\nC: bag\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_150_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_150_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_150_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_150_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_150_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_150_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: sink\nC: bag\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: sink\nC: bag\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_151_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_151_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_151_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_151_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_151_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_151_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: sink\nC: bag\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: door\nB: cabinet\nC: bed\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_152_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_152_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_152_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_152_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_152_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_152_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: door\nB: cabinet\nC: bed\nD: shelf"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: bin\nC: shelf\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_153_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_153_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_153_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_153_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_153_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_153_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: bin\nC: shelf\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: bed\nC: pillow\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_154_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_154_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_154_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_154_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_154_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_154_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: bed\nC: pillow\nD: sofa"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: bed\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_155_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_155_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_155_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_155_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_155_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_155_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: bed\nD: display"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bag\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_156_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_156_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_156_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_156_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_156_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_156_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bag\nC: sink\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: display\nC: toilet\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_157_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_157_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_157_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_157_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_157_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_157_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: display\nC: toilet\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: sink\nC: cabinet\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_158_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_158_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_158_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_158_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_158_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_158_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: sink\nC: cabinet\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: bed\nC: table\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_159_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_159_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_159_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_159_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_159_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_159_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: bed\nC: table\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: pillow\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_160_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_160_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_160_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_160_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_160_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_160_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: pillow\nC: bed\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bin\nC: cabinet\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_161_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_161_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_161_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_161_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_161_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_161_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bin\nC: cabinet\nD: bag"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: toilet\nC: cabinet\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_162_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_162_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_162_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_162_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_162_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_162_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: toilet\nC: cabinet\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: display\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_163_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_163_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_163_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_163_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_163_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_163_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: display\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: bin\nC: cabinet\nD: door", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_164_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_164_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_164_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_164_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_164_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_164_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: bin\nC: cabinet\nD: door"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: sink\nC: door\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_165_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_165_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_165_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_165_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_165_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_165_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: sink\nC: door\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: desk\nB: cabinet\nC: shelf\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_166_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_166_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_166_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_166_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_166_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_166_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: cabinet\nC: shelf\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: cabinet\nC: desk\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_167_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_167_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_167_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_167_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_167_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_167_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: cabinet\nC: desk\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sofa\nB: chair\nC: desk\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_168_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_168_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_168_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_168_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_168_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_168_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: chair\nC: desk\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: bag\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_169_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_169_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_169_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_169_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_169_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_169_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: bag\nC: sink\nD: cabinet"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: cabinet\nC: bed\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_170_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_170_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_170_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_170_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_170_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_170_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: cabinet\nC: bed\nD: sink"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: cabinet\nC: sink\nD: door", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_171_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_171_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_171_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_171_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_171_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_171_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: cabinet\nC: sink\nD: door"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: sink\nC: door\nD: shelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_172_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_172_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_172_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_172_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_172_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_172_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: sink\nC: door\nD: shelf"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: shelf\nC: sink\nD: door", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_173_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_173_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_173_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_173_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_173_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_173_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: shelf\nC: sink\nD: door"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: cabinet\nC: sink\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_174_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_174_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_174_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_174_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_174_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_174_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: cabinet\nC: sink\nD: table"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: table\nB: cabinet\nC: sink\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_175_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_175_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_175_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_175_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_175_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_175_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: cabinet\nC: sink\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: sink\nC: cabinet\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_176_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_176_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_176_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_176_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_176_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_176_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: sink\nC: cabinet\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: shelf\nC: cabinet\nD: display", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_177_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_177_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_177_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_177_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_177_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_177_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: shelf\nC: cabinet\nD: display"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: bag\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_178_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_178_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_178_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_178_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_178_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_178_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: bag\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: chair\nC: cabinet\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_179_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_179_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_179_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_179_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_179_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_179_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: chair\nC: cabinet\nD: bag"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: table\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_180_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_180_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_180_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_180_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_180_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_180_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: table\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: door\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_181_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_181_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_181_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_181_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_181_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_181_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: door\nC: sink\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: sofa\nC: bed\nD: bin", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_182_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_182_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_182_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_182_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_182_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_182_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: sofa\nC: bed\nD: bin"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: display\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_183_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_183_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_183_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_183_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_183_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_183_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: display\nC: bed\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: sofa\nC: pillow\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_184_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_184_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_184_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_184_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_184_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_184_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: sofa\nC: pillow\nD: chair"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bin\nB: bed\nC: cabinet\nD: pillow", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_185_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_185_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_185_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_185_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_185_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_185_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bin\nB: bed\nC: cabinet\nD: pillow"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: chair\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_186_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_186_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_186_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_186_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_186_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_186_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: chair\nC: sink\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: cabinet\nC: shelf\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_187_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_187_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_187_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_187_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_187_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_187_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: cabinet\nC: shelf\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: door\nD: bag", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_188_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_188_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_188_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_188_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_188_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_188_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: door\nD: bag"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: cabinet\nC: table\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_189_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_189_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_189_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_189_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_189_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_189_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: cabinet\nC: table\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: shelf\nC: bed\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_190_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_190_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_190_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_190_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_190_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_190_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: shelf\nC: bed\nD: chair"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: display\nB: cabinet\nC: bed\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_191_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_191_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_191_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_191_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_191_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_191_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: display\nB: cabinet\nC: bed\nD: sofa"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: door\nC: cabinet\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_192_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_192_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_192_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_192_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_192_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_192_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: door\nC: cabinet\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: table\nC: sink\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_193_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_193_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_193_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_193_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_193_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_193_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: table\nC: sink\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: shelf\nB: bed\nC: cabinet\nD: door", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_194_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_194_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_194_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_194_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_194_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_194_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: shelf\nB: bed\nC: cabinet\nD: door"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bed\nB: sink\nC: toilet\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_195_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_195_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_195_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_195_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_195_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_195_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: sink\nC: toilet\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: sink\nB: bed\nC: bin\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_196_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_196_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_196_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_196_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_196_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_196_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bed\nC: bin\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: bag\nB: sink\nC: bed\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_197_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_197_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_197_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_197_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_197_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_197_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bag\nB: sink\nC: bed\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: chair\nB: table\nC: sofa\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_198_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_198_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_198_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_198_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_198_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_198_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: table\nC: sofa\nD: bed"}, "output": {"output_text": "C"}, "task": "threed_indoor_recognition"}
{"source": "ScanObjectNN", "options": "A: cabinet\nB: bin\nC: bed\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_indoor_recognition/threed_indoor_recognition_199_0.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_199_1.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_199_2.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_199_3.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_199_4.jpg", "3D-spatial/threed_indoor_recognition/threed_indoor_recognition_199_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bin\nC: bed\nD: sink"}, "output": {"output_text": "D"}, "task": "threed_indoor_recognition"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_0_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_0_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_1_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_1_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_2_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_2_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_3_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_3_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_4_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_4_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_5_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_5_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_6_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_6_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_7_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_7_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_8_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_8_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_9_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_9_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_10_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_10_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_11_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_11_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_12_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_12_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_13_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_13_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_14_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_14_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_15_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_15_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_16_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_16_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_17_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_17_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_18_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_18_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_19_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_19_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_20_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_20_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_21_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_21_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_22_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_22_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_23_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_23_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_24_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_24_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_25_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_25_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_26_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_26_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_27_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_27_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_28_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_28_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_29_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_29_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_30_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_30_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_31_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_31_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_32_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_32_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_33_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_33_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_34_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_34_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_35_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_35_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_36_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_36_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_37_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_37_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_38_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_38_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_39_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_39_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_40_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_40_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_41_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_41_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_42_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_42_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_43_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_43_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_44_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_44_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_45_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_45_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_46_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_46_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_47_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_47_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_48_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_48_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_49_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_49_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_50_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_50_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_51_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_51_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_52_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_52_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_53_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_53_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_54_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_54_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_55_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_55_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_56_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_56_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_57_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_57_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_58_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_58_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_59_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_59_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_60_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_60_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_61_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_61_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_62_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_62_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_63_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_63_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_64_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_64_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_65_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_65_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_66_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_66_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_67_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_67_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_68_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_68_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_69_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_69_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_70_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_70_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_71_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_71_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_72_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_72_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_73_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_73_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_74_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_74_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_75_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_75_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_76_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_76_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_77_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_77_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_78_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_78_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_79_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_79_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_80_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_80_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_81_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_81_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_82_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_82_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_83_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_83_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_84_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_84_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_85_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_85_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_86_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_86_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_87_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_87_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_88_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_88_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_89_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_89_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_90_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_90_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_91_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_91_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_92_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_92_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_93_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_93_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_94_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_94_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_95_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_95_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_96_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_96_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_97_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_97_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_98_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_98_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_99_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_99_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_100_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_100_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_101_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_101_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_102_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_102_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_103_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_103_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_104_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_104_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_105_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_105_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_106_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_106_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_107_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_107_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_108_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_108_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_109_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_109_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_110_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_110_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_111_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_111_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_112_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_112_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_113_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_113_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_114_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_114_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_115_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_115_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_116_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_116_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_117_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_117_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_118_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_118_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_119_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_119_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_120_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_120_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_121_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_121_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_122_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_122_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_123_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_123_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_124_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_124_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_125_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_125_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_126_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_126_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_127_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_127_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_128_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_128_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_129_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_129_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_130_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_130_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "A"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_131_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_131_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "BLINK_MVR", "options": "A: left\nB: right", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_reasoning/Multiview_reasoning_132_0.jpg", "3D-spatial/Multiview_reasoning/Multiview_reasoning_132_1.jpg"], "question": "The images are frames from a video. The first image is from the beginning of the video and the second image is from the end. Is the camera moving left or right when shooting the video?", "context": "Your task is centered on evaluating the multi-view reasoning capabilities of models. The objective is to deduce the relative camera motion based on two images of an object captured from different viewpoints.\nSelect from the following choices.\nA: left\nB: right"}, "output": {"output_text": "B"}, "task": "Multiview_reasoning"}
{"source": "ModelNet40", "options": "A: bottle\nB: lamp\nC: chair\nD: clock", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_0_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_0_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_0_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_0_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_0_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_0_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bottle\nB: lamp\nC: chair\nD: clock"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: wardrobe\nB: television stand\nC: radio\nD: xbox", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_1_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_1_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_1_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_1_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_1_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_1_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: wardrobe\nB: television stand\nC: radio\nD: xbox"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: tv stand\nB: sofa\nC: stool\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_2_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_2_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_2_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_2_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_2_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_2_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: tv stand\nB: sofa\nC: stool\nD: chair"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: radio\nB: loudspeaker\nC: guitar\nD: microphone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_3_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_3_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_3_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_3_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_3_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_3_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: radio\nB: loudspeaker\nC: guitar\nD: microphone"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: cabinet\nB: bathtub\nC: glass box\nD: monitor", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_4_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_4_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_4_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_4_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_4_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_4_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: bathtub\nC: glass box\nD: monitor"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: mantel\nB: bookshelf\nC: curtain\nD: stool", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_5_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_5_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_5_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_5_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_5_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_5_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: mantel\nB: bookshelf\nC: curtain\nD: stool"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: toilet\nB: sink\nC: bathtub\nD: stool", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_6_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_6_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_6_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_6_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_6_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_6_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: toilet\nB: sink\nC: bathtub\nD: stool"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bowl\nB: table\nC: stairs\nD: laptop", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_7_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_7_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_7_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_7_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_7_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_7_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bowl\nB: table\nC: stairs\nD: laptop"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: television stand\nB: radio\nC: vase\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_8_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_8_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_8_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_8_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_8_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_8_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: television stand\nB: radio\nC: vase\nD: lamp"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bookshelf\nB: telephone\nC: chair\nD: desk", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_9_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_9_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_9_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_9_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_9_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_9_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bookshelf\nB: telephone\nC: chair\nD: desk"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: loudspeaker\nB: watercraft\nC: airplane\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_10_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_10_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_10_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_10_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_10_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_10_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: loudspeaker\nB: watercraft\nC: airplane\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: dresser\nC: night stand\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_11_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_11_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_11_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_11_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_11_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_11_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: dresser\nC: night stand\nD: bed"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bookshelf\nB: desk\nC: toilet\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_12_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_12_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_12_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_12_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_12_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_12_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bookshelf\nB: desk\nC: toilet\nD: lamp"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: tv stand\nB: telephone\nC: clock\nD: laptop", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_13_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_13_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_13_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_13_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_13_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_13_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: tv stand\nB: telephone\nC: clock\nD: laptop"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: cabinet\nB: lamp\nC: mantel\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_14_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_14_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_14_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_14_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_14_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_14_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: lamp\nC: mantel\nD: table"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: vase\nC: bookshelf\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_15_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_15_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_15_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_15_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_15_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_15_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: vase\nC: bookshelf\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: guitar\nB: speaker\nC: table\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_16_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_16_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_16_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_16_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_16_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_16_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: guitar\nB: speaker\nC: table\nD: chair"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: stool\nB: piano\nC: microphone\nD: guitar", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_17_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_17_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_17_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_17_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_17_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_17_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: stool\nB: piano\nC: microphone\nD: guitar"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: desk\nB: chair\nC: sofa\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_18_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_18_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_18_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_18_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_18_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_18_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: chair\nC: sofa\nD: table"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: night stand\nC: bed\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_19_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_19_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_19_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_19_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_19_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_19_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: night stand\nC: bed\nD: lamp"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: sofa\nC: table\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_20_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_20_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_20_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_20_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_20_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_20_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: sofa\nC: table\nD: chair"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: range hood\nB: clock\nC: telephone\nD: vase", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_21_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_21_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_21_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_21_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_21_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_21_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: range hood\nB: clock\nC: telephone\nD: vase"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bathtub\nB: airplane\nC: watercraft\nD: car", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_22_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_22_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_22_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_22_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_22_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_22_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bathtub\nB: airplane\nC: watercraft\nD: car"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: airplane\nB: bicycle\nC: motorcycle\nD: car", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_23_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_23_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_23_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_23_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_23_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_23_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: airplane\nB: bicycle\nC: motorcycle\nD: car"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: night stand\nC: lamp\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_24_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_24_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_24_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_24_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_24_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_24_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: night stand\nC: lamp\nD: chair"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: car\nB: telephone\nC: toilet\nD: bottle", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_25_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_25_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_25_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_25_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_25_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_25_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: car\nB: telephone\nC: toilet\nD: bottle"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: flower pot\nB: lamp\nC: stairs\nD: plant", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_26_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_26_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_26_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_26_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_26_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_26_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: flower pot\nB: lamp\nC: stairs\nD: plant"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bookshelf\nB: desk\nC: table\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_27_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_27_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_27_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_27_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_27_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_27_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bookshelf\nB: desk\nC: table\nD: chair"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: chair\nC: night stand\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_28_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_28_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_28_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_28_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_28_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_28_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: chair\nC: night stand\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: faucet\nB: telephone\nC: clock\nD: vase", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_29_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_29_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_29_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_29_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_29_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_29_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: faucet\nB: telephone\nC: clock\nD: vase"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: night stand\nB: chair\nC: lamp\nD: dresser", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_30_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_30_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_30_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_30_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_30_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_30_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: night stand\nB: chair\nC: lamp\nD: dresser"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: desk\nB: cabinet\nC: table\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_31_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_31_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_31_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_31_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_31_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_31_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: cabinet\nC: table\nD: chair"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: guitar\nB: microphone\nC: piano\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_32_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_32_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_32_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_32_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_32_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_32_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: guitar\nB: microphone\nC: piano\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: mantel\nB: chair\nC: sofa\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_33_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_33_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_33_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_33_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_33_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_33_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: mantel\nB: chair\nC: sofa\nD: bed"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: desk\nB: sofa\nC: table\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_34_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_34_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_34_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_34_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_34_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_34_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: sofa\nC: table\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bike\nB: car\nC: airplane\nD: bus", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_35_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_35_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_35_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_35_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_35_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_35_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bike\nB: car\nC: airplane\nD: bus"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: plant\nC: chair\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_36_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_36_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_36_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_36_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_36_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_36_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: plant\nC: chair\nD: telephone"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: mug\nB: bottle\nC: glass box\nD: faucet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_37_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_37_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_37_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_37_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_37_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_37_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: mug\nB: bottle\nC: glass box\nD: faucet"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: tv stand\nC: mantel\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_38_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_38_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_38_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_38_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_38_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_38_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: tv stand\nC: mantel\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: sofa\nC: chair\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_39_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_39_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_39_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_39_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_39_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_39_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: sofa\nC: chair\nD: telephone"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: airplane\nB: boat\nC: sofa\nD: watercraft", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_40_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_40_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_40_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_40_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_40_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_40_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: airplane\nB: boat\nC: sofa\nD: watercraft"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: clock\nC: vase\nD: car", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_41_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_41_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_41_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_41_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_41_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_41_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: clock\nC: vase\nD: car"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: monitor\nB: keyboard\nC: television\nD: speaker", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_42_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_42_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_42_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_42_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_42_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_42_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: monitor\nB: keyboard\nC: television\nD: speaker"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: bathtub\nC: toilet\nD: faucet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_43_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_43_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_43_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_43_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_43_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_43_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bathtub\nC: toilet\nD: faucet"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: telephone\nC: clock\nD: guitar", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_44_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_44_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_44_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_44_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_44_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_44_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: telephone\nC: clock\nD: guitar"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: monitor\nB: loudspeaker\nC: guitar\nD: piano", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_45_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_45_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_45_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_45_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_45_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_45_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: monitor\nB: loudspeaker\nC: guitar\nD: piano"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: tv stand\nB: radio\nC: chair\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_46_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_46_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_46_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_46_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_46_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_46_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: tv stand\nB: radio\nC: chair\nD: cabinet"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: table\nC: sofa\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_47_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_47_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_47_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_47_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_47_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_47_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: table\nC: sofa\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: sofa\nC: bed\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_48_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_48_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_48_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_48_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_48_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_48_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: sofa\nC: bed\nD: chair"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: cabinet\nB: chair\nC: telephone\nD: tv stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_49_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_49_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_49_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_49_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_49_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_49_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: chair\nC: telephone\nD: tv stand"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: toilet\nB: chair\nC: lamp\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_50_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_50_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_50_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_50_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_50_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_50_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: toilet\nB: chair\nC: lamp\nD: sink"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: monitor\nB: airplane\nC: car\nD: person", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_51_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_51_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_51_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_51_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_51_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_51_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: monitor\nB: airplane\nC: car\nD: person"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: mantel\nB: sofa\nC: telephone\nD: clock", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_52_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_52_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_52_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_52_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_52_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_52_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: mantel\nB: sofa\nC: telephone\nD: clock"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: glass box\nB: bottle\nC: mug\nD: watercraft", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_53_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_53_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_53_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_53_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_53_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_53_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: glass box\nB: bottle\nC: mug\nD: watercraft"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: guitar\nC: vase\nD: clock", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_54_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_54_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_54_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_54_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_54_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_54_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: guitar\nC: vase\nD: clock"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: pistol\nC: rifle\nD: tv stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_55_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_55_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_55_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_55_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_55_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_55_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: pistol\nC: rifle\nD: tv stand"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bookshelf\nB: sofa\nC: mantel\nD: television", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_56_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_56_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_56_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_56_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_56_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_56_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bookshelf\nB: sofa\nC: mantel\nD: television"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: vase\nC: bookshelf\nD: curtain", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_57_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_57_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_57_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_57_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_57_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_57_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: vase\nC: bookshelf\nD: curtain"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: dresser\nB: bookshelf\nC: stool\nD: night stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_58_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_58_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_58_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_58_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_58_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_58_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: dresser\nB: bookshelf\nC: stool\nD: night stand"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: clock\nC: tv stand\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_59_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_59_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_59_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_59_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_59_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_59_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: clock\nC: tv stand\nD: telephone"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: desk\nB: laptop\nC: keyboard\nD: monitor", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_60_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_60_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_60_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_60_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_60_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_60_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: laptop\nC: keyboard\nD: monitor"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sofa\nB: clock\nC: telephone\nD: guitar", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_61_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_61_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_61_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_61_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_61_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_61_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: clock\nC: telephone\nD: guitar"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: range hood\nB: telephone\nC: clock\nD: bathtub", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_62_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_62_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_62_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_62_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_62_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_62_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: range hood\nB: telephone\nC: clock\nD: bathtub"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: loudspeaker\nB: radio\nC: telephone\nD: tv stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_63_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_63_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_63_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_63_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_63_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_63_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: loudspeaker\nB: radio\nC: telephone\nD: tv stand"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: guitar\nB: microphone\nC: table\nD: piano", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_64_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_64_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_64_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_64_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_64_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_64_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: guitar\nB: microphone\nC: table\nD: piano"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: chair\nC: sofa\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_65_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_65_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_65_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_65_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_65_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_65_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: chair\nC: sofa\nD: table"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: stair\nB: keyboard\nC: laptop\nD: cellphone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_66_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_66_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_66_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_66_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_66_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_66_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: stair\nB: keyboard\nC: laptop\nD: cellphone"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: telephone\nC: vase\nD: monitor", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_67_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_67_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_67_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_67_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_67_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_67_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: telephone\nC: vase\nD: monitor"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: night stand\nB: dresser\nC: television\nD: bookshelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_68_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_68_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_68_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_68_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_68_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_68_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: night stand\nB: dresser\nC: television\nD: bookshelf"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: laptop\nB: monitor\nC: keyboard\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_69_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_69_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_69_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_69_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_69_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_69_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: laptop\nB: monitor\nC: keyboard\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: bed\nC: lamp\nD: night stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_70_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_70_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_70_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_70_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_70_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_70_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: bed\nC: lamp\nD: night stand"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: clock\nC: vase\nD: tv stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_71_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_71_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_71_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_71_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_71_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_71_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: clock\nC: vase\nD: tv stand"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bottle\nB: lamp\nC: glass box\nD: mug", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_72_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_72_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_72_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_72_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_72_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_72_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bottle\nB: lamp\nC: glass box\nD: mug"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: telephone\nC: tv stand\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_73_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_73_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_73_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_73_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_73_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_73_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: telephone\nC: tv stand\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: piano\nC: dresser\nD: night stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_74_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_74_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_74_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_74_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_74_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_74_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: piano\nC: dresser\nD: night stand"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: sofa\nC: lamp\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_75_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_75_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_75_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_75_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_75_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_75_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: sofa\nC: lamp\nD: chair"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: radio\nB: chair\nC: desk\nD: bench", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_76_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_76_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_76_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_76_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_76_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_76_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: radio\nB: chair\nC: desk\nD: bench"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: piano\nB: chair\nC: stool\nD: guitar", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_77_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_77_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_77_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_77_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_77_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_77_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: piano\nB: chair\nC: stool\nD: guitar"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: faucet\nB: telephone\nC: stool\nD: range hood", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_78_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_78_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_78_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_78_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_78_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_78_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: faucet\nB: telephone\nC: stool\nD: range hood"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: telephone\nC: bookshelf\nD: clock", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_79_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_79_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_79_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_79_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_79_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_79_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: telephone\nC: bookshelf\nD: clock"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: chair\nC: plant\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_80_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_80_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_80_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_80_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_80_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_80_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: chair\nC: plant\nD: cabinet"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: faucet\nB: telephone\nC: table\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_81_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_81_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_81_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_81_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_81_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_81_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: faucet\nB: telephone\nC: table\nD: chair"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: lamp\nC: bookshelf\nD: sink", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_82_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_82_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_82_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_82_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_82_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_82_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: lamp\nC: bookshelf\nD: sink"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: tv stand\nB: desk\nC: monitor\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_83_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_83_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_83_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_83_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_83_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_83_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: tv stand\nB: desk\nC: monitor\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: night stand\nB: vase\nC: clock\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_84_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_84_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_84_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_84_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_84_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_84_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: night stand\nB: vase\nC: clock\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: desk\nC: stool\nD: bookshelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_85_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_85_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_85_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_85_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_85_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_85_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: desk\nC: stool\nD: bookshelf"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: rifle\nB: telephone\nC: car\nD: airplane", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_86_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_86_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_86_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_86_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_86_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_86_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: rifle\nB: telephone\nC: car\nD: airplane"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bottle\nB: sink\nC: toilet\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_87_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_87_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_87_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_87_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_87_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_87_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bottle\nB: sink\nC: toilet\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: television\nC: mirror\nD: decorative bowl", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_88_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_88_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_88_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_88_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_88_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_88_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: television\nC: mirror\nD: decorative bowl"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bed\nB: chair\nC: sofa\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_89_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_89_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_89_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_89_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_89_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_89_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: chair\nC: sofa\nD: table"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: faucet\nC: toilet\nD: bathtub", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_90_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_90_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_90_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_90_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_90_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_90_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: faucet\nC: toilet\nD: bathtub"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: lamp\nC: vase\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_91_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_91_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_91_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_91_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_91_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_91_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: lamp\nC: vase\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: bottle\nC: glass box\nD: faucet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_92_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_92_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_92_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_92_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_92_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_92_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: bottle\nC: glass box\nD: faucet"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: faucet\nB: telephone\nC: airplane\nD: clock", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_93_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_93_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_93_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_93_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_93_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_93_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: faucet\nB: telephone\nC: airplane\nD: clock"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: faucet\nB: clock\nC: telephone\nD: range hood", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_94_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_94_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_94_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_94_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_94_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_94_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: faucet\nB: clock\nC: telephone\nD: range hood"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: rifle\nB: laptop\nC: clock\nD: pistol", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_95_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_95_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_95_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_95_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_95_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_95_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: rifle\nB: laptop\nC: clock\nD: pistol"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: cup\nB: bottle\nC: glass box\nD: mug", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_96_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_96_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_96_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_96_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_96_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_96_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cup\nB: bottle\nC: glass box\nD: mug"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: glass box\nB: television\nC: monitor\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_97_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_97_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_97_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_97_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_97_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_97_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: glass box\nB: television\nC: monitor\nD: chair"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: car\nB: telephone\nC: radio\nD: airplane", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_98_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_98_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_98_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_98_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_98_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_98_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: car\nB: telephone\nC: radio\nD: airplane"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: flower pot\nC: lamp\nD: vase", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_99_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_99_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_99_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_99_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_99_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_99_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: flower pot\nC: lamp\nD: vase"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: night stand\nC: bottle\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_100_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_100_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_100_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_100_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_100_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_100_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: night stand\nC: bottle\nD: sofa"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: table\nC: tv stand\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_101_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_101_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_101_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_101_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_101_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_101_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: table\nC: tv stand\nD: telephone"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: glass box\nB: table\nC: lamp\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_102_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_102_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_102_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_102_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_102_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_102_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: glass box\nB: table\nC: lamp\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: vase\nC: chair\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_103_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_103_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_103_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_103_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_103_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_103_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: vase\nC: chair\nD: lamp"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: chair\nC: table\nD: desk", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_104_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_104_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_104_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_104_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_104_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_104_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: chair\nC: table\nD: desk"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: lamp\nC: flower pot\nD: plant", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_105_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_105_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_105_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_105_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_105_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_105_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: lamp\nC: flower pot\nD: plant"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: sofa\nC: chair\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_106_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_106_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_106_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_106_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_106_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_106_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: sofa\nC: chair\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: guitar\nC: radio\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_107_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_107_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_107_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_107_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_107_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_107_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: guitar\nC: radio\nD: lamp"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: bench\nC: table\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_108_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_108_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_108_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_108_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_108_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_108_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: bench\nC: table\nD: lamp"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: clock\nC: vase\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_109_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_109_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_109_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_109_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_109_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_109_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: clock\nC: vase\nD: lamp"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sofa\nB: table\nC: chair\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_110_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_110_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_110_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_110_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_110_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_110_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: table\nC: chair\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: toilet\nC: table\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_111_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_111_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_111_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_111_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_111_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_111_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: toilet\nC: table\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: toilet\nC: vase\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_112_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_112_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_112_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_112_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_112_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_112_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: toilet\nC: vase\nD: table"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: car\nB: vase\nC: telephone\nD: clock", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_113_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_113_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_113_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_113_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_113_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_113_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: car\nB: vase\nC: telephone\nD: clock"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: desk\nC: bookshelf\nD: wardrobe", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_114_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_114_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_114_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_114_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_114_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_114_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: desk\nC: bookshelf\nD: wardrobe"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: microphone\nB: table\nC: stool\nD: guitar", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_115_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_115_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_115_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_115_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_115_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_115_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: microphone\nB: table\nC: stool\nD: guitar"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: piano\nB: rifle\nC: guitar\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_116_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_116_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_116_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_116_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_116_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_116_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: piano\nB: rifle\nC: guitar\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: stairs\nC: laptop\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_117_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_117_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_117_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_117_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_117_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_117_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: stairs\nC: laptop\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: radio\nB: glass box\nC: monitor\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_118_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_118_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_118_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_118_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_118_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_118_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: radio\nB: glass box\nC: monitor\nD: lamp"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: clock\nC: guitar\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_119_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_119_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_119_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_119_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_119_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_119_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: clock\nC: guitar\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: piano\nC: monitor\nD: laptop", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_120_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_120_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_120_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_120_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_120_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_120_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: piano\nC: monitor\nD: laptop"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: telephone\nC: guitar\nD: vase", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_121_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_121_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_121_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_121_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_121_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_121_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: telephone\nC: guitar\nD: vase"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: telephone\nC: vase\nD: plant", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_122_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_122_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_122_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_122_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_122_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_122_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: telephone\nC: vase\nD: plant"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: lamp\nC: desk\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_123_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_123_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_123_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_123_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_123_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_123_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: lamp\nC: desk\nD: chair"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: telephone\nC: chair\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_124_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_124_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_124_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_124_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_124_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_124_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: telephone\nC: chair\nD: toilet"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: night stand\nB: chair\nC: bed\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_125_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_125_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_125_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_125_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_125_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_125_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: night stand\nB: chair\nC: bed\nD: lamp"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: sofa\nC: chair\nD: cabinet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_126_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_126_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_126_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_126_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_126_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_126_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: sofa\nC: chair\nD: cabinet"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: toilet\nC: telephone\nD: vase", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_127_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_127_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_127_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_127_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_127_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_127_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: toilet\nC: telephone\nD: vase"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bottle\nB: watercraft\nC: airplane\nD: car", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_128_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_128_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_128_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_128_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_128_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_128_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bottle\nB: watercraft\nC: airplane\nD: car"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: desk\nC: table\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_129_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_129_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_129_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_129_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_129_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_129_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: desk\nC: table\nD: sofa"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: vase\nC: stool\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_130_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_130_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_130_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_130_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_130_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_130_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: vase\nC: stool\nD: telephone"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: piano\nB: telephone\nC: clock\nD: vase", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_131_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_131_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_131_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_131_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_131_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_131_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: piano\nB: telephone\nC: clock\nD: vase"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bottle\nB: faucet\nC: glass box\nD: radio", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_132_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_132_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_132_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_132_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_132_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_132_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bottle\nB: faucet\nC: glass box\nD: radio"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: faucet\nC: clock\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_133_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_133_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_133_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_133_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_133_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_133_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: faucet\nC: clock\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: radio\nB: tv stand\nC: lamp\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_134_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_134_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_134_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_134_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_134_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_134_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: radio\nB: tv stand\nC: lamp\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: night stand\nB: dresser\nC: bed\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_135_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_135_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_135_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_135_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_135_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_135_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: night stand\nB: dresser\nC: bed\nD: lamp"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: wardrobe\nB: curtain\nC: bathtub\nD: desk", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_136_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_136_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_136_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_136_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_136_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_136_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: wardrobe\nB: curtain\nC: bathtub\nD: desk"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: stool\nB: chair\nC: desk\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_137_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_137_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_137_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_137_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_137_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_137_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: stool\nB: chair\nC: desk\nD: table"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: lamp\nC: telephone\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_138_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_138_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_138_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_138_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_138_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_138_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: lamp\nC: telephone\nD: toilet"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: guitar\nB: telephone\nC: radio\nD: laptop", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_139_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_139_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_139_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_139_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_139_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_139_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: guitar\nB: telephone\nC: radio\nD: laptop"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: tv stand\nC: clock\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_140_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_140_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_140_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_140_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_140_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_140_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: tv stand\nC: clock\nD: telephone"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: flower pot\nB: clock\nC: vase\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_141_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_141_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_141_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_141_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_141_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_141_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: flower pot\nB: clock\nC: vase\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: vase\nC: bottle\nD: stool", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_142_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_142_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_142_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_142_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_142_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_142_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: vase\nC: bottle\nD: stool"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sofa\nB: chair\nC: bed\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_143_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_143_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_143_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_143_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_143_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_143_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sofa\nB: chair\nC: bed\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: chair\nC: sofa\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_144_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_144_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_144_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_144_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_144_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_144_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: chair\nC: sofa\nD: bed"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: dresser\nB: desk\nC: bathtub\nD: wardrobe", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_145_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_145_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_145_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_145_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_145_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_145_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: dresser\nB: desk\nC: bathtub\nD: wardrobe"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: stool\nB: chair\nC: desk\nD: bench", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_146_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_146_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_146_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_146_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_146_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_146_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: stool\nB: chair\nC: desk\nD: bench"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: rifle\nC: lamp\nD: plant", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_147_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_147_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_147_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_147_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_147_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_147_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: rifle\nC: lamp\nD: plant"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: bed\nC: dresser\nD: wardrobe", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_148_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_148_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_148_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_148_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_148_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_148_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: bed\nC: dresser\nD: wardrobe"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: curtain\nB: stool\nC: mantel\nD: bookshelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_149_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_149_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_149_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_149_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_149_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_149_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: curtain\nB: stool\nC: mantel\nD: bookshelf"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: vase\nB: tv stand\nC: clock\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_150_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_150_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_150_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_150_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_150_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_150_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: vase\nB: tv stand\nC: clock\nD: telephone"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: desk\nB: bookshelf\nC: table\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_151_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_151_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_151_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_151_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_151_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_151_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desk\nB: bookshelf\nC: table\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: glass box\nC: mug\nD: bottle", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_152_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_152_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_152_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_152_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_152_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_152_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: glass box\nC: mug\nD: bottle"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: cellphone\nC: watercraft\nD: laptop", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_153_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_153_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_153_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_153_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_153_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_153_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: cellphone\nC: watercraft\nD: laptop"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: sofa\nC: table\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_154_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_154_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_154_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_154_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_154_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_154_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: sofa\nC: table\nD: lamp"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: microphone\nB: guitar\nC: piano\nD: stool", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_155_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_155_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_155_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_155_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_155_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_155_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: microphone\nB: guitar\nC: piano\nD: stool"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bookshelf\nB: chair\nC: telephone\nD: table", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_156_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_156_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_156_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_156_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_156_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_156_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bookshelf\nB: chair\nC: telephone\nD: table"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bed\nB: chair\nC: sofa\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_157_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_157_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_157_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_157_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_157_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_157_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: chair\nC: sofa\nD: lamp"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: bowl\nC: mug\nD: lamp", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_158_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_158_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_158_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_158_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_158_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_158_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: bowl\nC: mug\nD: lamp"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: faucet\nB: bathtub\nC: shower\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_159_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_159_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_159_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_159_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_159_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_159_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: faucet\nB: bathtub\nC: shower\nD: toilet"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: cabinet\nB: desk\nC: table\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_160_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_160_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_160_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_160_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_160_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_160_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: cabinet\nB: desk\nC: table\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: guitar\nB: stool\nC: telephone\nD: clock", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_161_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_161_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_161_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_161_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_161_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_161_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: guitar\nB: stool\nC: telephone\nD: clock"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bed\nB: stool\nC: night stand\nD: desk", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_162_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_162_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_162_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_162_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_162_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_162_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bed\nB: stool\nC: night stand\nD: desk"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: airplane\nB: car\nC: motorcycle\nD: bicycle", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_163_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_163_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_163_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_163_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_163_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_163_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: airplane\nB: car\nC: motorcycle\nD: bicycle"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: tv stand\nB: sofa\nC: stool\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_164_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_164_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_164_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_164_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_164_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_164_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: tv stand\nB: sofa\nC: stool\nD: chair"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: piano\nB: clock\nC: guitar\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_165_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_165_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_165_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_165_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_165_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_165_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: piano\nB: clock\nC: guitar\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: laptop\nB: telephone\nC: stool\nD: airplane", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_166_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_166_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_166_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_166_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_166_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_166_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: laptop\nB: telephone\nC: stool\nD: airplane"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: clock\nC: vase\nD: pistol", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_167_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_167_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_167_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_167_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_167_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_167_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: clock\nC: vase\nD: pistol"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: plant\nB: television stand\nC: lamp\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_168_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_168_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_168_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_168_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_168_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_168_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: plant\nB: television stand\nC: lamp\nD: chair"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: airplane\nB: lamp\nC: radio\nD: tent", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_169_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_169_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_169_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_169_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_169_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_169_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: airplane\nB: lamp\nC: radio\nD: tent"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: stairs\nC: piano\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_170_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_170_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_170_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_170_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_170_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_170_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: stairs\nC: piano\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: faucet\nC: bottle\nD: tv stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_171_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_171_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_171_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_171_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_171_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_171_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: faucet\nC: bottle\nD: tv stand"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: table\nB: sofa\nC: desk\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_172_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_172_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_172_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_172_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_172_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_172_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: table\nB: sofa\nC: desk\nD: chair"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: vase\nC: chair\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_173_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_173_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_173_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_173_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_173_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_173_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: vase\nC: chair\nD: toilet"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: chair\nC: table\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_174_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_174_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_174_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_174_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_174_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_174_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: chair\nC: table\nD: sofa"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: bathtub\nC: telephone\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_175_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_175_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_175_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_175_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_175_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_175_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bathtub\nC: telephone\nD: toilet"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: tv stand\nC: telephone\nD: vase", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_176_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_176_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_176_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_176_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_176_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_176_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: tv stand\nC: telephone\nD: vase"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: car\nB: bookshelf\nC: airplane\nD: stool", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_177_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_177_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_177_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_177_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_177_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_177_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: car\nB: bookshelf\nC: airplane\nD: stool"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: table\nC: sofa\nD: stool", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_178_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_178_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_178_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_178_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_178_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_178_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: table\nC: sofa\nD: stool"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: faucet\nB: telephone\nC: range hood\nD: stool", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_179_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_179_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_179_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_179_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_179_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_179_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: faucet\nB: telephone\nC: range hood\nD: stool"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: table\nC: chair\nD: bookshelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_180_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_180_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_180_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_180_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_180_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_180_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: table\nC: chair\nD: bookshelf"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: desk\nC: bookshelf\nD: chair", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_181_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_181_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_181_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_181_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_181_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_181_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: desk\nC: bookshelf\nD: chair"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: bathtub\nC: faucet\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_182_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_182_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_182_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_182_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_182_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_182_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bathtub\nC: faucet\nD: toilet"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: stool\nC: chair\nD: tv stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_183_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_183_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_183_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_183_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_183_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_183_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: stool\nC: chair\nD: tv stand"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: mantel\nB: stairs\nC: fireplace\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_184_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_184_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_184_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_184_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_184_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_184_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: mantel\nB: stairs\nC: fireplace\nD: sofa"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: desktop\nB: lamp\nC: radio\nD: glass box", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_185_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_185_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_185_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_185_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_185_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_185_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: desktop\nB: lamp\nC: radio\nD: glass box"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: mantel\nB: plant\nC: radio\nD: tv stand", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_186_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_186_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_186_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_186_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_186_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_186_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: mantel\nB: plant\nC: radio\nD: tv stand"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: clock\nC: vase\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_187_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_187_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_187_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_187_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_187_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_187_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: clock\nC: vase\nD: telephone"}, "output": {"output_text": "A"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: clock\nB: car\nC: vase\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_188_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_188_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_188_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_188_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_188_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_188_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: clock\nB: car\nC: vase\nD: telephone"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: telephone\nB: clock\nC: piano\nD: vase", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_189_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_189_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_189_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_189_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_189_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_189_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: telephone\nB: clock\nC: piano\nD: vase"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: television\nB: glass box\nC: chair\nD: bookshelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_190_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_190_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_190_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_190_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_190_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_190_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: television\nB: glass box\nC: chair\nD: bookshelf"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: shower curtain\nC: monitor\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_191_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_191_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_191_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_191_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_191_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_191_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: shower curtain\nC: monitor\nD: toilet"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: telephone\nC: stool\nD: sofa", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_192_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_192_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_192_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_192_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_192_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_192_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: telephone\nC: stool\nD: sofa"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: chair\nB: keyboard\nC: guitar\nD: telephone", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_193_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_193_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_193_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_193_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_193_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_193_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: chair\nB: keyboard\nC: guitar\nD: telephone"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bookshelf\nB: desk\nC: chair\nD: mantel", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_194_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_194_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_194_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_194_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_194_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_194_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bookshelf\nB: desk\nC: chair\nD: mantel"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: night stand\nB: chair\nC: lamp\nD: bed", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_195_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_195_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_195_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_195_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_195_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_195_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: night stand\nB: chair\nC: lamp\nD: bed"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: sink\nB: bathtub\nC: stool\nD: toilet", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_196_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_196_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_196_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_196_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_196_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_196_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: sink\nB: bathtub\nC: stool\nD: toilet"}, "output": {"output_text": "D"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: lamp\nB: plant\nC: flower pot\nD: bookshelf", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_197_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_197_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_197_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_197_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_197_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_197_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: lamp\nB: plant\nC: flower pot\nD: bookshelf"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: bottle\nB: mug\nC: keyboard\nD: cup", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_198_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_198_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_198_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_198_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_198_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_198_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: bottle\nB: mug\nC: keyboard\nD: cup"}, "output": {"output_text": "B"}, "task": "threed_cad_recognition"}
{"source": "ModelNet40", "options": "A: stool\nB: chair\nC: piano\nD: guitar", "visual_input_component": "Poine cloud image", "input": {"input_image_path": ["3D-spatial/threed_cad_recognition/threed_cad_recognition_199_0.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_199_1.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_199_2.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_199_3.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_199_4.jpg", "3D-spatial/threed_cad_recognition/threed_cad_recognition_199_5.jpg"], "question": "What is the category of the point cloud based on the multi-view of the point cloud?", "context": "Select from the following choices.\nA: stool\nB: chair\nC: piano\nD: guitar"}, "output": {"output_text": "C"}, "task": "threed_cad_recognition"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_0_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_0_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_0_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_0_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_0_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_0_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.891251, 0.378307, -0.25011], [0.443048, 0.608538, -0.658323], [-0.096846, -0.697542, -0.709969]] and translation vector: [4.935522, 3.588868, 1.45033], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.887006, 0.383874, -0.256633], [0.452131, 0.60913, -0.651566], [-0.093796, -0.693975, -0.713864]] and translation vector: [4.940225, 3.582454, 1.45688], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_1_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_1_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_1_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_1_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_1_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_1_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.997112, 0.02462, 0.071841], [-0.04661, 0.548461, -0.834876], [-0.059957, -0.835814, -0.545729]] and translation vector: [4.834615, 3.436689, 1.398379], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.998397, 0.025746, 0.050402], [-0.028149, 0.546702, -0.836854], [-0.0491, -0.836932, -0.545101]] and translation vector: [4.839047, 3.434593, 1.400064], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_2_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_2_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_2_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_2_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_2_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_2_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.485844, -0.617081, 0.619005], [-0.873216, -0.311825, 0.374512], [-0.038083, -0.722479, -0.690343]] and translation vector: [-0.164865, 3.073333, 1.323993], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.482952, -0.621872, 0.616468], [-0.874972, -0.315096, 0.367612], [-0.034361, -0.716931, -0.696297]] and translation vector: [-0.16601, 3.069565, 1.320265], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_3_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_3_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_3_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_3_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_3_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_3_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.934582, -0.143102, 0.325696], [-0.355737, 0.383069, -0.852473], [-0.002774, -0.912568, -0.408916]] and translation vector: [2.694367, 2.483235, 1.465763], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.935747, -0.141154, 0.323191], [-0.352667, 0.379116, -0.85551], [-0.001768, -0.91452, -0.404537]] and translation vector: [2.694351, 2.483417, 1.465522], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_4_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_4_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_4_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_4_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_4_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_4_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.08083, -0.463089, 0.882618], [-0.994842, 0.091929, -0.042874], [-0.061284, -0.881531, -0.468131]] and translation vector: [4.543997, 3.147744, 1.235262], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.097623, -0.477164, 0.873375], [-0.993778, 0.094019, -0.059714], [-0.05362, -0.873771, -0.483373]] and translation vector: [4.550471, 3.148599, 1.246367], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_5_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_5_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_5_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_5_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_5_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_5_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.877021, 0.121711, -0.464779], [0.46491, 0.459041, -0.75706], [0.12121, -0.880038, -0.459173]] and translation vector: [3.922419, 3.230202, 1.747047], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.876473, 0.11975, -0.466322], [0.465798, 0.455895, -0.758415], [0.121773, -0.881941, -0.455359]] and translation vector: [3.923546, 3.227255, 1.740959], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_6_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_6_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_6_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_6_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_6_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_6_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.246516, -0.470365, 0.847341], [-0.959136, 0.006886, 0.282862], [-0.138884, -0.882445, -0.449446]] and translation vector: [3.043058, 2.955299, 1.551102], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.243276, -0.470143, 0.8484], [-0.960213, 0.006937, 0.279182], [-0.13714, -0.882563, -0.44975]] and translation vector: [3.042024, 2.954946, 1.550413], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_7_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_7_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_7_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_7_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_7_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_7_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.236277, -0.452541, 0.859872], [-0.970097, 0.160455, -0.182119], [-0.055554, -0.877189, -0.47692]] and translation vector: [1.575898, 1.961144, 1.314442], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.238966, -0.451212, 0.859828], [-0.9694, 0.162109, -0.184349], [-0.056205, -0.87757, -0.476143]] and translation vector: [1.575219, 1.960128, 1.313122], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_8_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_8_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_8_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_8_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_8_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_8_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.633294, -0.360819, 0.684652], [-0.773758, -0.312806, 0.550863], [0.015401, -0.878613, -0.477285]] and translation vector: [3.241882, 3.386626, 1.367882], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.618852, -0.359339, 0.698497], [-0.785116, -0.311057, 0.535572], [0.02482, -0.87984, -0.47462]] and translation vector: [3.234923, 3.400149, 1.365622], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_9_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_9_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_9_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_9_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_9_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_9_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.974605, -0.106498, 0.196986], [-0.223762, -0.428932, 0.875185], [-0.008712, -0.897037, -0.44187]] and translation vector: [2.006689, 0.552817, 1.711334], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.976991, -0.101609, 0.187523], [-0.213093, -0.42809, 0.878254], [-0.008962, -0.898006, -0.439892]] and translation vector: [2.014877, 0.551422, 1.700123], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_10_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_10_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_10_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_10_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_10_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_10_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.874867, -0.0675, 0.479638], [-0.482919, 0.197999, -0.852987], [-0.037391, -0.977875, -0.205819]] and translation vector: [2.397274, 1.722858, 1.486845], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.874077, -0.063653, 0.4816], [-0.484123, 0.196153, -0.852731], [-0.040189, -0.978505, -0.202269]] and translation vector: [2.402604, 1.721845, 1.489477], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_11_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_11_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_11_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_11_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_11_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_11_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.606497, 0.359513, -0.709163], [0.793947, -0.321582, 0.515978], [-0.042553, -0.875977, -0.480473]] and translation vector: [5.898605, 1.464963, 1.329018], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.603336, 0.358994, -0.712116], [0.79647, -0.316333, 0.515334], [-0.040264, -0.878098, -0.476783]] and translation vector: [5.91512, 1.4588, 1.326343], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_12_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_12_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_12_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_12_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_12_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_12_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.810147, -0.229725, 0.539341], [-0.586224, 0.314131, -0.746769], [0.002128, -0.921167, -0.389162]] and translation vector: [3.108561, 2.950706, 1.466118], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.798041, -0.241673, 0.552019], [-0.602539, 0.306626, -0.736836], [0.00881, -0.920638, -0.390318]] and translation vector: [3.094201, 2.939754, 1.46817], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_13_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_13_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_13_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_13_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_13_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_13_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.994136, 0.036629, -0.101745], [0.107123, -0.462198, 0.880283], [-0.014782, -0.88602, -0.463411]] and translation vector: [3.8191, 1.340951, 1.354002], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.994264, 0.034625, -0.101195], [0.105882, -0.452335, 0.885541], [-0.015112, -0.891176, -0.453407]] and translation vector: [3.821174, 1.339834, 1.359098], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_14_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_14_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_14_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_14_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_14_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_14_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.199941, 0.263531, -0.943703], [0.979453, -0.027844, 0.19974], [0.026362, -0.964249, -0.263683]] and translation vector: [3.611549, 3.757055, 1.562045], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.20075, 0.267793, -0.94233], [0.97934, -0.030969, 0.199834], [0.024331, -0.962979, -0.268477]] and translation vector: [3.608934, 3.756757, 1.557843], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_15_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_15_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_15_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_15_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_15_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_15_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.24604, -0.551346, 0.797171], [-0.968826, -0.115295, 0.219278], [-0.028988, -0.826271, -0.562526]] and translation vector: [1.704247, 2.057158, 1.361636], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.236706, -0.55071, 0.800431], [-0.971342, -0.115817, 0.207564], [-0.021604, -0.826623, -0.562342]] and translation vector: [1.70792, 2.062619, 1.364929], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_16_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_16_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_16_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_16_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_16_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_16_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.695296, -0.421579, 0.582095], [-0.717067, -0.351947, 0.601622], [-0.048765, -0.835707, -0.547007]] and translation vector: [2.470866, 0.652559, 1.473924], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.695871, -0.418819, 0.583399], [-0.716734, -0.353708, 0.600986], [-0.045352, -0.83635, -0.546317]] and translation vector: [2.469546, 0.651931, 1.473078], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_17_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_17_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_17_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_17_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_17_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_17_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.937403, 0.174354, -0.301457], [0.34768, 0.517889, -0.781607], [0.019845, -0.837491, -0.54609]] and translation vector: [1.513881, 1.499843, 1.388066], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.93698, 0.17766, -0.300842], [0.348874, 0.522274, -0.77815], [0.018876, -0.834067, -0.551341]] and translation vector: [1.515168, 1.503997, 1.385631], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_18_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_18_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_18_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_18_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_18_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_18_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.693623, 0.392298, -0.604144], [0.720137, 0.397492, -0.568686], [0.017048, -0.82952, -0.558217]] and translation vector: [2.706242, 2.586761, 1.453005], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.690051, 0.396658, -0.605386], [0.723517, 0.399766, -0.56277], [0.018785, -0.826347, -0.562848]] and translation vector: [2.704536, 2.590014, 1.45316], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_19_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_19_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_19_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_19_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_19_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_19_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.530794, 0.426739, -0.732224], [0.841151, 0.159702, -0.516681], [-0.10355, -0.890162, -0.443721]] and translation vector: [5.418979, 4.373359, 1.385162], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.532043, 0.421439, -0.734384], [0.841755, 0.169492, -0.512564], [-0.091542, -0.890877, -0.444925]] and translation vector: [5.415919, 4.39552, 1.38299], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_20_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_20_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_20_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_20_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_20_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_20_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.484778, 0.389748, -0.782998], [0.874059, -0.248441, 0.417491], [-0.031813, -0.886777, -0.461102]] and translation vector: [2.948564, 2.712566, 1.480667], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.484062, 0.388161, -0.784229], [0.874419, -0.248162, 0.416902], [-0.03279, -0.887551, -0.459542]] and translation vector: [2.949191, 2.711738, 1.477649], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_21_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_21_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_21_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_21_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_21_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_21_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.998134, -0.025826, -0.055325], [0.04389, 0.326427, -0.944203], [0.042444, -0.94487, -0.324684]] and translation vector: [2.355182, 2.984659, 1.395898], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.998605, -0.022906, -0.047579], [0.037628, 0.323493, -0.945482], [0.037048, -0.945953, -0.32218]] and translation vector: [2.345251, 2.98743, 1.391141], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_22_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_22_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_22_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_22_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_22_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_22_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.473704, -0.275929, 0.836342], [-0.879436, -0.198746, 0.432542], [0.046868, -0.940406, -0.336809]] and translation vector: [2.984934, 2.048073, 1.446683], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.466625, -0.271085, 0.841888], [-0.8831, -0.195475, 0.426525], [0.048943, -0.942498, -0.330608]] and translation vector: [2.979092, 2.049407, 1.446378], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_23_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_23_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_23_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_23_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_23_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_23_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.566304, -0.590941, 0.574533], [-0.823945, 0.423135, -0.376925], [-0.020365, -0.686838, -0.726526]] and translation vector: [2.143516, 1.760119, 1.343188], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.561614, -0.596242, 0.57366], [-0.827171, 0.420904, -0.372329], [-0.019457, -0.683619, -0.729579]] and translation vector: [2.147258, 1.761594, 1.344016], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_24_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_24_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_24_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_24_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_24_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_24_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.054781, -0.427281, 0.902458], [-0.998013, -0.051617, 0.036143], [0.031139, -0.902644, -0.429259]] and translation vector: [1.328526, 0.849821, 1.501181], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.086578, -0.407933, 0.908898], [-0.995883, -0.060028, 0.067922], [0.026852, -0.911036, -0.41145]] and translation vector: [1.314662, 0.836147, 1.492068], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_25_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_25_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_25_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_25_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_25_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_25_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.941243, -0.209403, 0.264975], [-0.336113, 0.504116, -0.795548], [0.033012, -0.837865, -0.544878]] and translation vector: [4.828751, 9.008894, 1.463441], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.939528, -0.206646, 0.273103], [-0.341818, 0.516505, -0.785101], [0.021179, -0.830976, -0.555906]] and translation vector: [4.819307, 9.009376, 1.463735], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_26_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_26_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_26_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_26_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_26_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_26_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.68967, 0.288211, -0.664297], [0.724122, -0.27239, 0.633602], [0.001663, -0.918008, -0.396559]] and translation vector: [2.530043, 2.005069, 1.437417], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.68921, 0.288518, -0.66464], [0.724561, -0.273014, 0.632831], [0.001127, -0.917726, -0.397212]] and translation vector: [2.5334, 2.008455, 1.44069], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_27_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_27_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_27_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_27_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_27_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_27_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.994446, -0.078697, 0.06988], [-0.104992, -0.787844, 0.606859], [0.007297, -0.610826, -0.791731]] and translation vector: [1.305105, 0.510448, 1.183315], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.994112, -0.083607, 0.068931], [-0.10831, -0.785774, 0.608956], [0.003251, -0.612836, -0.790203]] and translation vector: [1.308194, 0.508844, 1.184721], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_28_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_28_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_28_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_28_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_28_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_28_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.928375, -0.17783, 0.326339], [-0.371449, 0.415395, -0.830345], [0.012101, -0.892089, -0.451697]] and translation vector: [2.096006, 1.919092, 1.36174], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.929206, -0.177937, 0.323905], [-0.369314, 0.414969, -0.83151], [0.013546, -0.892266, -0.451307]] and translation vector: [2.095672, 1.922099, 1.363168], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_29_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_29_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_29_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_29_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_29_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_29_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.684823, -0.326379, 0.651532], [-0.728707, -0.304485, 0.613413], [-0.001823, -0.894855, -0.446353]] and translation vector: [2.86358, 2.414664, 1.549631], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.684506, -0.325468, 0.652321], [-0.729004, -0.308374, 0.611113], [0.002261, -0.893855, -0.448351]] and translation vector: [2.864701, 2.413023, 1.547001], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_30_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_30_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_30_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_30_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_30_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_30_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.928108, -0.125197, 0.35063], [-0.371823, 0.3599, -0.855699], [-0.019061, -0.924553, -0.380577]] and translation vector: [5.296664, 4.137775, 1.856988], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.930637, -0.119308, 0.34595], [-0.365378, 0.355543, -0.860284], [-0.020361, -0.927014, -0.374474]] and translation vector: [5.29653, 4.126579, 1.856014], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_31_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_31_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_31_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_31_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_31_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_31_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.399387, 0.327689, -0.856218], [0.9115, 0.041819, -0.409169], [-0.098274, -0.94386, -0.315391]] and translation vector: [4.88233, 2.963563, 1.403722], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.394763, 0.316878, -0.86241], [0.913367, 0.033579, -0.40575], [-0.099614, -0.947872, -0.302681]] and translation vector: [4.88409, 2.965299, 1.400614], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_32_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_32_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_32_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_32_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_32_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_32_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.119369, -0.433868, 0.893034], [-0.990549, 0.113242, -0.077387], [-0.067553, -0.893832, -0.443285]] and translation vector: [3.407035, 4.679209, 1.397058], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.120544, -0.432859, 0.893366], [-0.990306, 0.115004, -0.077902], [-0.06902, -0.894096, -0.442526]] and translation vector: [3.401289, 4.681283, 1.397495], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_33_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_33_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_33_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_33_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_33_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_33_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.264492, -0.222038, 0.938479], [-0.962334, 0.002714, 0.271857], [-0.062909, -0.975034, -0.212957]] and translation vector: [0.925816, 4.784833, 1.497389], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.263009, -0.220134, 0.939344], [-0.962729, 0.003779, 0.270443], [-0.063084, -0.975462, -0.210935]] and translation vector: [0.925807, 4.784041, 1.498483], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_34_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_34_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_34_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_34_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_34_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_34_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.711391, -0.463973, 0.527875], [-0.700286, 0.531398, -0.476672], [-0.059349, -0.708763, -0.702945]] and translation vector: [2.53321, 4.394931, 1.530427], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.710702, -0.465347, 0.527594], [-0.701175, 0.5294, -0.477586], [-0.057065, -0.709357, -0.702536]] and translation vector: [2.526067, 4.393322, 1.526345], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_35_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_35_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_35_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_35_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_35_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_35_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.524333, 0.441188, -0.728305], [0.848808, -0.202677, 0.488311], [0.067827, -0.874228, -0.480754]] and translation vector: [3.10696, 1.250425, 1.344077], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.531491, 0.437044, -0.72561], [0.844432, -0.205894, 0.494513], [0.066725, -0.875557, -0.478485]] and translation vector: [3.107462, 1.25329, 1.344278], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_36_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_36_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_36_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_36_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_36_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_36_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.205964, -0.505778, 0.837716], [-0.978495, 0.11627, -0.170378], [-0.011228, -0.854792, -0.518849]] and translation vector: [2.901534, 4.292832, 1.280844], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.204012, -0.504726, 0.838827], [-0.978841, 0.118998, -0.166463], [-0.0158, -0.855039, -0.518324]] and translation vector: [2.909629, 4.290413, 1.285823], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_37_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_37_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_37_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_37_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_37_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_37_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.999847, -0.004634, 0.01689], [-0.017397, -0.374134, 0.927211], [0.002023, -0.927363, -0.374157]] and translation vector: [3.310194, 3.16458, 1.506432], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.999774, -0.010896, 0.018284], [-0.021018, -0.369724, 0.928904], [-0.003361, -0.929078, -0.369869]] and translation vector: [3.316631, 3.168954, 1.519748], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_38_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_38_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_38_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_38_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_38_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_38_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.951558, 0.16536, -0.259218], [0.307283, -0.481983, 0.820531], [0.010744, -0.860436, -0.509446]] and translation vector: [2.919862, 3.428013, 1.521081], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.951326, 0.167996, -0.258374], [0.307875, -0.4803, 0.821295], [0.013877, -0.860866, -0.508643]] and translation vector: [2.920042, 3.428186, 1.518811], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_39_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_39_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_39_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_39_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_39_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_39_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.567127, -0.123224, 0.81436], [-0.823556, -0.071568, 0.562702], [-0.011056, -0.989795, -0.14207]] and translation vector: [0.249561, 0.967409, 1.634127], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.566682, -0.123694, 0.814599], [-0.82386, -0.07149, 0.562268], [-0.011313, -0.989742, -0.142418]] and translation vector: [0.249762, 0.967631, 1.633273], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_40_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_40_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_40_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_40_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_40_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_40_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.464707, 0.496079, -0.733453], [0.882598, 0.326106, -0.338639], [0.071191, -0.804711, -0.589382]] and translation vector: [2.864701, 0.868861, 1.204561], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.473617, 0.501904, -0.723726], [0.878064, 0.332992, -0.343688], [0.068496, -0.798254, -0.598414]] and translation vector: [2.869803, 0.866998, 1.20304], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_41_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_41_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_41_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_41_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_41_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_41_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.943065, -0.17817, 0.280864], [-0.332105, 0.550897, -0.765649], [-0.018311, -0.815333, -0.578703]] and translation vector: [2.74599, 1.673222, 1.294065], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.942639, -0.173012, 0.285478], [-0.332909, 0.550136, -0.765848], [-0.024551, -0.816957, -0.576177]] and translation vector: [2.737266, 1.663808, 1.300966], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_42_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_42_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_42_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_42_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_42_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_42_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.590232, -0.352789, 0.726062], [-0.807221, -0.252962, 0.533296], [-0.004475, -0.900861, -0.434086]] and translation vector: [2.518124, 2.463328, 1.346668], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.586587, -0.358769, 0.726086], [-0.809845, -0.250747, 0.530356], [-0.008212, -0.899117, -0.437632]] and translation vector: [2.520116, 2.462175, 1.344964], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_43_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_43_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_43_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_43_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_43_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_43_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.454685, 0.144673, -0.878824], [0.890085, 0.109034, -0.442562], [0.031795, -0.983454, -0.178347]] and translation vector: [3.311996, 2.119304, 1.59409], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.453171, 0.138778, -0.880555], [0.890847, 0.10604, -0.441756], [0.032068, -0.98463, -0.171684]] and translation vector: [3.314367, 2.120091, 1.591769], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_44_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_44_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_44_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_44_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_44_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_44_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.86482, -0.183466, 0.467362], [-0.501092, -0.256948, 0.826368], [-0.031523, -0.948851, -0.314147]] and translation vector: [3.012278, 2.022242, 1.442339], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.863867, -0.189194, 0.466839], [-0.502557, -0.260784, 0.824274], [-0.034203, -0.946677, -0.320364]] and translation vector: [3.015002, 2.018446, 1.436262], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_45_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_45_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_45_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_45_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_45_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_45_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.880278, -0.246293, 0.405524], [-0.473973, 0.417832, -0.775091], [0.021459, -0.874503, -0.484545]] and translation vector: [3.281806, 2.754624, 1.352781], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.883446, -0.241464, 0.401521], [-0.467927, 0.41107, -0.782347], [0.023856, -0.879043, -0.476146]] and translation vector: [3.2823, 2.745028, 1.352692], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_46_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_46_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_46_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_46_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_46_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_46_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.133825, -0.39571, 0.908573], [-0.990975, -0.046263, 0.125813], [-0.007752, -0.91721, -0.398329]] and translation vector: [4.990516, 4.227292, 1.32289], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.168071, -0.388121, 0.906153], [-0.985699, -0.054747, 0.159375], [-0.012247, -0.919981, -0.391772]] and translation vector: [4.987841, 4.19209, 1.32312], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_47_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_47_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_47_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_47_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_47_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_47_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.48142, 0.335029, -0.809933], [0.872625, 0.096524, -0.478757], [-0.08222, -0.937251, -0.338823]] and translation vector: [4.429162, 2.287411, 1.464776], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.484328, 0.331289, -0.809737], [0.871134, 0.09698, -0.481374], [-0.080946, -0.938532, -0.335568]] and translation vector: [4.432656, 2.285767, 1.465956], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_48_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_48_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_48_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_48_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_48_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_48_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.672393, -0.274439, 0.687438], [-0.739855, -0.221079, 0.635404], [-0.022402, -0.935846, -0.351697]] and translation vector: [3.802358, 2.110255, 1.494557], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.672432, -0.275262, 0.687071], [-0.739825, -0.222066, 0.635095], [-0.022242, -0.93537, -0.35297]] and translation vector: [3.806542, 2.108163, 1.497405], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_49_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_49_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_49_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_49_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_49_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_49_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.176261, -0.039155, 0.983564], [-0.983722, -0.028492, -0.177423], [0.03497, -0.998827, -0.033496]] and translation vector: [3.054739, 2.437738, 1.503838], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.18153, -0.048874, 0.98217], [-0.982778, -0.026092, -0.182941], [0.034567, -0.998464, -0.043296]] and translation vector: [3.061021, 2.450195, 1.498681], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_50_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_50_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_50_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_50_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_50_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_50_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.467192, 0.317292, -0.825262], [0.883302, -0.126478, 0.451421], [0.038855, -0.939856, -0.339354]] and translation vector: [2.723032, 3.168159, 1.438168], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.467636, 0.312306, -0.826911], [0.883318, -0.130557, 0.450227], [0.03265, -0.940968, -0.336919]] and translation vector: [2.722188, 3.168039, 1.441817], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_51_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_51_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_51_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_51_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_51_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_51_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.51864, -0.44867, 0.727811], [-0.853934, -0.229463, 0.467059], [-0.04255, -0.863738, -0.502143]] and translation vector: [1.002297, 1.98866, 1.344191], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.519607, -0.444592, 0.729621], [-0.853432, -0.229314, 0.468049], [-0.040778, -0.865883, -0.498582]] and translation vector: [1.000441, 1.985865, 1.344846], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_52_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_52_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_52_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_52_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_52_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_52_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.688084, 0.423256, -0.589401], [0.725514, -0.415863, 0.54835], [-0.013017, -0.80493, -0.593227]] and translation vector: [3.968163, 0.8771, 1.421607], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.688048, 0.420794, -0.591205], [0.725576, -0.411726, 0.551381], [-0.011397, -0.80834, -0.588605]] and translation vector: [3.964529, 0.870938, 1.417962], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_53_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_53_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_53_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_53_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_53_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_53_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.042655, 0.409797, -0.911179], [0.998036, -0.024411, -0.0577], [-0.045888, -0.91185, -0.40795]] and translation vector: [2.423933, 1.356295, 3.282493], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.032887, 0.418885, -0.907444], [0.998611, -0.023628, -0.047098], [-0.041169, -0.907732, -0.417526]] and translation vector: [2.425306, 1.358764, 3.278826], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_54_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_54_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_54_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_54_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_54_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_54_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.925351, 0.122106, -0.358909], [0.376741, 0.190476, -0.906524], [-0.042329, -0.974068, -0.222259]] and translation vector: [4.735593, 2.732706, 1.21643], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.924788, 0.125024, -0.359357], [0.377675, 0.187086, -0.906841], [-0.046146, -0.974355, -0.220234]] and translation vector: [4.740286, 2.733964, 1.218072], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_55_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_55_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_55_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_55_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_55_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_55_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.908726, 0.150598, -0.389277], [0.406624, 0.108936, -0.907078], [-0.094198, -0.982575, -0.16023]] and translation vector: [8.822721, 3.830595, 1.476402], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.908663, 0.151907, -0.388916], [0.40641, 0.108245, -0.907256], [-0.09572, -0.98245, -0.160095]] and translation vector: [8.818814, 3.832555, 1.475788], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_56_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_56_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_56_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_56_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_56_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_56_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.984594, -0.069457, 0.160469], [-0.174127, -0.305795, 0.936039], [-0.015944, -0.949561, -0.313178]] and translation vector: [3.941113, 2.817773, 1.559826], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.984592, -0.069572, 0.160429], [-0.174152, -0.307406, 0.935507], [-0.015768, -0.949032, -0.314785]] and translation vector: [3.94407, 2.817183, 1.553188], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_57_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_57_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_57_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_57_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_57_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_57_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.677945, 0.409221, -0.610679], [0.735109, 0.38004, -0.561413], [0.00234, -0.829523, -0.558468]] and translation vector: [3.092599, 2.044437, 1.437429], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.678782, 0.408186, -0.610442], [0.734335, 0.380383, -0.562193], [0.002723, -0.829875, -0.557943]] and translation vector: [3.0892, 2.043949, 1.440375], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_58_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_58_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_58_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_58_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_58_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_58_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.330673, -0.328207, 0.884837], [-0.942686, -0.070458, 0.326157], [-0.044703, -0.941975, -0.332694]] and translation vector: [3.753276, 4.481459, 1.345242], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.306694, -0.326667, 0.893995], [-0.950878, -0.063631, 0.302957], [-0.04208, -0.942995, -0.330136]] and translation vector: [3.754864, 4.497246, 1.34429], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_59_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_59_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_59_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_59_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_59_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_59_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.305635, -0.390507, 0.868385], [-0.952144, 0.122302, -0.280116], [0.003183, -0.91244, -0.409198]] and translation vector: [4.266061, 1.773856, 1.285079], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.300987, -0.399102, 0.866097], [-0.953628, 0.125052, -0.273781], [0.00096, -0.908339, -0.418234]] and translation vector: [4.263163, 1.772832, 1.291083], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_60_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_60_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_60_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_60_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_60_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_60_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.853196, -0.330732, 0.403328], [-0.517406, -0.438892, 0.734619], [-0.065945, -0.835458, -0.545584]] and translation vector: [2.734716, 6.775187, 1.412962], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.853022, -0.336855, 0.398601], [-0.516617, -0.436898, 0.736361], [-0.0739, -0.834056, -0.546708]] and translation vector: [2.728871, 6.767794, 1.411126], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_61_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_61_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_61_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_61_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_61_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_61_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.207785, -0.462455, 0.861952], [-0.977184, 0.13779, -0.161637], [-0.044019, -0.875871, -0.480534]] and translation vector: [2.720584, 1.654419, 1.522448], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.211008, -0.462778, 0.860995], [-0.976592, 0.137438, -0.165466], [-0.04176, -0.875755, -0.480946]] and translation vector: [2.717844, 1.649691, 1.521912], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_62_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_62_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_62_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_62_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_62_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_62_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.624751, -0.31057, 0.716403], [-0.780527, -0.273701, 0.562018], [0.021534, -0.910293, -0.413403]] and translation vector: [-0.212106, 0.775797, 1.619325], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.624146, -0.312612, 0.716042], [-0.781019, -0.274551, 0.56092], [0.02124, -0.909338, -0.415515]] and translation vector: [-0.212874, 0.777223, 1.616059], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_63_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_63_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_63_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_63_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_63_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_63_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.964843, 0.186346, -0.185345], [0.252505, 0.461537, -0.850426], [-0.07293, -0.867329, -0.492364]] and translation vector: [3.779865, 2.337391, 1.461827], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.966867, 0.182729, -0.178267], [0.244986, 0.467845, -0.849178], [-0.071768, -0.864715, -0.49711]] and translation vector: [3.779708, 2.335608, 1.46105], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_64_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_64_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_64_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_64_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_64_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_64_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.935902, 0.160482, -0.313582], [0.351212, -0.493772, 0.795512], [-0.027173, -0.854655, -0.518485]] and translation vector: [4.465, -0.226232, 1.550028], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.933656, 0.161027, -0.319933], [0.356818, -0.495752, 0.791777], [-0.03111, -0.853405, -0.520319]] and translation vector: [4.478531, -0.229773, 1.540292], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_65_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_65_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_65_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_65_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_65_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_65_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.955421, 0.119616, -0.269932], [0.295248, 0.388339, -0.872939], [0.000408, -0.91372, -0.406343]] and translation vector: [2.65583, 2.981598, 1.368648], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.951595, 0.120375, -0.282803], [0.307283, 0.392547, -0.866882], [0.006663, -0.91182, -0.410535]] and translation vector: [2.655525, 2.981353, 1.361859], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_66_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_66_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_66_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_66_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_66_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_66_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.409087, -0.112571, 0.905525], [-0.910894, 0.109148, -0.397943], [-0.05404, -0.987631, -0.147191]] and translation vector: [4.421403, 3.579741, 1.526424], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.417977, -0.10834, 0.901974], [-0.906895, 0.107978, -0.407287], [-0.053267, -0.988232, -0.143386]] and translation vector: [4.418822, 3.582731, 1.526625], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_67_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_67_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_67_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_67_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_67_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_67_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.931668, 0.072515, -0.356001], [0.362912, -0.231685, 0.902561], [-0.017031, -0.970084, -0.24217]] and translation vector: [5.886859, 3.543659, 1.354971], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.931979, 0.073028, -0.355079], [0.362119, -0.233112, 0.902513], [-0.016864, -0.969704, -0.2437]] and translation vector: [5.882501, 3.543666, 1.354317], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_68_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_68_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_68_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_68_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_68_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_68_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.081815, 0.638296, -0.765431], [0.996577, -0.061545, 0.055199], [-0.011875, -0.767327, -0.641146]] and translation vector: [3.004073, 1.570726, 1.431248], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.083332, 0.64082, -0.763155], [0.996457, -0.062303, 0.056492], [-0.011346, -0.765159, -0.643742]] and translation vector: [3.00242, 1.571458, 1.432065], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_69_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_69_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_69_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_69_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_69_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_69_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.606468, -0.360414, 0.70873], [-0.789578, -0.16805, 0.590192], [-0.093612, -0.91753, -0.386492]] and translation vector: [2.373669, 6.226582, 1.48631], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.603564, -0.356146, 0.713352], [-0.791899, -0.163667, 0.588311], [-0.092772, -0.919986, -0.380815]] and translation vector: [2.370215, 6.229294, 1.484576], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_70_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_70_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_70_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_70_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_70_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_70_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.481759, -0.460793, 0.745371], [-0.875469, 0.290199, -0.386444], [-0.038235, -0.838722, -0.543216]] and translation vector: [3.08436, 2.075189, 1.468295], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.482142, -0.463533, 0.743422], [-0.87538, 0.289132, -0.387445], [-0.035354, -0.83758, -0.54517]] and translation vector: [3.085865, 2.079347, 1.468915], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_71_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_71_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_71_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_71_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_71_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_71_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.731293, 0.384445, -0.563394], [0.682011, 0.401944, -0.610984], [-0.008437, -0.831049, -0.556135]] and translation vector: [5.176627, 2.209938, 1.427488], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.733453, 0.387758, -0.558292], [0.679719, 0.411882, -0.606907], [-0.005383, -0.82462, -0.565663]] and translation vector: [5.175584, 2.209993, 1.422561], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_72_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_72_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_72_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_72_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_72_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_72_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.414473, -0.491559, 0.765887], [-0.909569, 0.196057, -0.366396], [0.029948, -0.848488, -0.528367]] and translation vector: [0.955419, 3.497842, 1.497559], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.410009, -0.490704, 0.768832], [-0.911757, 0.198024, -0.359841], [0.024328, -0.848526, -0.528594]] and translation vector: [0.937857, 3.503192, 1.495427], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_73_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_73_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_73_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_73_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_73_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_73_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.82141, -0.124481, 0.556588], [-0.562763, -0.33543, 0.755503], [0.092651, -0.933805, -0.345579]] and translation vector: [1.795382, 2.457259, 1.379582], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.820332, -0.124179, 0.558243], [-0.564621, -0.330977, 0.75608], [0.090876, -0.935432, -0.341626]] and translation vector: [1.795684, 2.460531, 1.380001], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_74_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_74_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_74_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_74_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_74_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_74_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.993805, -0.057016, 0.095394], [-0.110597, -0.423109, 0.899304], [-0.010913, -0.904283, -0.426794]] and translation vector: [3.282054, 2.568905, 1.512321], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.993106, -0.061381, 0.099861], [-0.116562, -0.427194, 0.896615], [-0.012375, -0.902074, -0.431404]] and translation vector: [3.283498, 2.568158, 1.509645], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_75_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_75_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_75_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_75_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_75_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_75_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.348231, 0.123124, -0.929288], [0.936413, -1.6e-05, 0.350899], [0.043189, -0.992391, -0.1153]] and translation vector: [2.712005, 2.075202, 1.464169], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.348319, 0.120186, -0.929639], [0.93641, 0.000395, 0.350907], [0.042542, -0.992751, -0.112406]] and translation vector: [2.712393, 2.076758, 1.463984], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_76_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_76_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_76_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_76_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_76_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_76_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.482968, -0.397392, 0.78027], [-0.874514, 0.173759, -0.452807], [0.044362, -0.901048, -0.431445]] and translation vector: [8.974016, 2.795387, 1.945192], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.496352, -0.388832, 0.776173], [-0.867003, 0.176647, -0.465943], [0.044064, -0.904216, -0.424797]] and translation vector: [8.98292, 2.792107, 1.939625], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_77_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_77_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_77_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_77_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_77_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_77_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.079918, -0.690871, 0.718547], [-0.996802, 0.055321, -0.057677], [9.6e-05, -0.720858, -0.693082]] and translation vector: [1.142658, 0.968078, 1.385987], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.080635, -0.691404, 0.717954], [-0.996742, 0.054488, -0.059473], [0.002, -0.72041, -0.693545]] and translation vector: [1.144302, 0.967344, 1.387927], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_78_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_78_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_78_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_78_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_78_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_78_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.725417, 0.297171, -0.620854], [0.687848, -0.279954, 0.669695], [0.025203, -0.912861, -0.407492]] and translation vector: [3.434752, 3.057745, 1.556519], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.722045, 0.303192, -0.621873], [0.691238, -0.278447, 0.666827], [0.029018, -0.911341, -0.410629]] and translation vector: [3.433538, 3.052318, 1.549734], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_79_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_79_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_79_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_79_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_79_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_79_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.156961, 0.257294, -0.953501], [0.986843, 0.002956, -0.161652], [-0.038773, -0.966329, -0.254373]] and translation vector: [1.838324, 1.205476, 1.480452], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.155829, 0.255617, -0.954137], [0.987039, 0.002796, -0.160453], [-0.038347, -0.966774, -0.252739]] and translation vector: [1.83996, 1.205416, 1.474648], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_80_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_80_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_80_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_80_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_80_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_80_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.922168, 0.178823, -0.342969], [0.38661, 0.453076, -0.803278], [0.011746, -0.873352, -0.486947]] and translation vector: [3.207336, 1.959871, 1.267555], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.914921, 0.180426, -0.361063], [0.403188, 0.450583, -0.796502], [0.018979, -0.874312, -0.484993]] and translation vector: [3.204391, 1.957541, 1.273759], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_81_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_81_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_81_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_81_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_81_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_81_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.241978, -0.427128, 0.871211], [-0.963615, 0.210861, -0.164264], [-0.113543, -0.879261, -0.462611]] and translation vector: [2.164319, 10.11033, 1.716674], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.23973, -0.426819, 0.871983], [-0.964754, 0.205144, -0.16482], [-0.108534, -0.880762, -0.460955]] and translation vector: [2.164643, 10.108889, 1.726434], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_82_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_82_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_82_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_82_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_82_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_82_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.424269, -0.366439, 0.828081], [-0.894198, -0.025281, 0.446957], [-0.142848, -0.930098, -0.338395]] and translation vector: [2.638367, 6.760901, 1.41712], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.432512, -0.37625, 0.819371], [-0.890339, -0.034872, 0.45396], [-0.14223, -0.925862, -0.350073]] and translation vector: [2.640049, 6.763855, 1.420073], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_83_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_83_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_83_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_83_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_83_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_83_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.645842, -0.099101, 0.757012], [-0.761541, -0.013148, 0.647984], [-0.054263, -0.994991, -0.083961]] and translation vector: [3.729951, 1.432448, 1.733539], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.649827, -0.099601, 0.753528], [-0.757797, -0.00807, 0.652441], [-0.058903, -0.994995, -0.080722]] and translation vector: [3.727943, 1.43259, 1.731865], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_84_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_84_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_84_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_84_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_84_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_84_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.686341, -0.358824, 0.632599], [-0.727213, -0.35045, 0.590209], [0.009912, -0.865119, -0.50147]] and translation vector: [2.486494, 4.601647, 1.455454], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.681394, -0.352774, 0.64129], [-0.731846, -0.340576, 0.590263], [0.010179, -0.871527, -0.490243]] and translation vector: [2.480601, 4.595852, 1.449959], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_85_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_85_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_85_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_85_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_85_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_85_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.515401, -0.339121, 0.786994], [-0.847541, -0.337435, 0.40965], [0.126638, -0.878143, -0.461333]] and translation vector: [4.776819, 1.138867, 1.280463], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.495978, -0.33911, 0.799381], [-0.859276, -0.324304, 0.395565], [0.125103, -0.88308, -0.452237]] and translation vector: [4.773187, 1.14016, 1.284317], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_86_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_86_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_86_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_86_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_86_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_86_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.799511, 0.533863, -0.275266], [0.600541, 0.71925, -0.349328], [0.011492, -0.4446, -0.895656]] and translation vector: [2.031323, 2.312379, 1.200993], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.794986, 0.540559, -0.275306], [0.606553, 0.715482, -0.346669], [0.009582, -0.442584, -0.896676]] and translation vector: [2.031011, 2.313572, 1.199732], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_87_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_87_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_87_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_87_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_87_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_87_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.830629, 0.239867, -0.502514], [0.556756, 0.37214, -0.742654], [0.008867, -0.896647, -0.442658]] and translation vector: [4.849209, 2.614689, 1.447477], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.826514, 0.239564, -0.509396], [0.562778, 0.371773, -0.738286], [0.012512, -0.89688, -0.442097]] and translation vector: [4.848542, 2.612423, 1.449706], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_88_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_88_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_88_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_88_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_88_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_88_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.977181, 0.077241, -0.197866], [0.211774, -0.426158, 0.879512], [-0.016388, -0.901345, -0.432791]] and translation vector: [0.977323, 0.877303, 1.40232], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.979446, 0.063797, -0.19135], [0.200663, -0.404476, 0.892263], [-0.020472, -0.912321, -0.408965]] and translation vector: [0.961423, 0.875672, 1.418643], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_89_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_89_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_89_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_89_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_89_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_89_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.977514, -0.102294, 0.184398], [-0.210796, -0.497303, 0.841578], [0.005613, -0.861525, -0.507684]] and translation vector: [3.555602, 1.207732, 1.356493], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.976582, -0.105336, 0.187593], [-0.215087, -0.498001, 0.840079], [0.00493, -0.860755, -0.508995]] and translation vector: [3.555365, 1.207812, 1.356155], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_90_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_90_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_90_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_90_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_90_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_90_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.869565, 0.231948, -0.435955], [0.492522, 0.471291, -0.731647], [0.035758, -0.850932, -0.524058]] and translation vector: [2.750575, 3.154689, 1.290553], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.871211, 0.246607, -0.424472], [0.49036, 0.478017, -0.72873], [0.023195, -0.843022, -0.53738]] and translation vector: [2.712538, 3.137298, 1.287246], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_91_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_91_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_91_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_91_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_91_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_91_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.991592, 0.052224, -0.118397], [0.1292, -0.348306, 0.928435], [0.007248, -0.935925, -0.352124]] and translation vector: [2.177373, 2.142725, 1.46728], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.992093, 0.047571, -0.11614], [0.125441, -0.346386, 0.929667], [0.003996, -0.936885, -0.349615]] and translation vector: [2.181058, 2.142908, 1.465582], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_92_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_92_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_92_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_92_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_92_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_92_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.987126, 0.106622, -0.119219], [0.159938, -0.652529, 0.740693], [0.00118, -0.750225, -0.661181]] and translation vector: [4.64166, 4.052867, 1.404314], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.987387, 0.107853, -0.115912], [0.158278, -0.654013, 0.73974], [0.003975, -0.748756, -0.662834]] and translation vector: [4.649776, 4.051806, 1.400746], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_93_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_93_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_93_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_93_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_93_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_93_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.892065, -0.360019, 0.273141], [-0.443019, -0.577417, 0.685801], [-0.089185, -0.732786, -0.674589]] and translation vector: [2.898737, 2.45906, 1.649541], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.888376, -0.366176, 0.276954], [-0.450762, -0.581088, 0.677606], [-0.087189, -0.726809, -0.681283]] and translation vector: [2.873446, 2.440832, 1.651115], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_94_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_94_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_94_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_94_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_94_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_94_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.610102, 0.375008, -0.697958], [0.791763, 0.255448, -0.554849], [-0.029781, -0.891132, -0.452767]] and translation vector: [2.349929, 1.419923, 1.358478], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.607496, 0.374505, -0.700496], [0.793845, 0.255679, -0.551759], [-0.027534, -0.891277, -0.452623]] and translation vector: [2.354864, 1.421781, 1.358478], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_95_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_95_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_95_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_95_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_95_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_95_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.386761, -0.304254, 0.870543], [-0.920043, 0.191539, -0.34181], [-0.062746, -0.933136, -0.354007]] and translation vector: [2.082368, 4.008438, 1.845888], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.387201, -0.298257, 0.872421], [-0.919947, 0.188025, -0.344013], [-0.061432, -0.935783, -0.347183]] and translation vector: [2.08001, 4.010775, 1.842824], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_96_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_96_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_96_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_96_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_96_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_96_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.993306, 0.029023, -0.111812], [0.110831, -0.512349, 0.851596], [-0.032571, -0.858287, -0.512136]] and translation vector: [2.482234, 1.391135, 1.348064], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.992702, 0.031717, -0.116349], [0.116167, -0.510508, 0.85199], [-0.032374, -0.859288, -0.510467]] and translation vector: [2.48213, 1.388715, 1.34704], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_97_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_97_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_97_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_97_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_97_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_97_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.660671, 0.426343, -0.617856], [0.749322, -0.423957, 0.508701], [-0.045063, -0.799057, -0.599565]] and translation vector: [1.739014, 2.260029, 1.323145], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.661948, 0.412501, -0.625834], [0.748146, -0.41469, 0.517987], [-0.045857, -0.811095, -0.583114]] and translation vector: [1.741474, 2.257287, 1.327618], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_98_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_98_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_98_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_98_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_98_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_98_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.283698, -0.38675, 0.877463], [-0.95878, 0.129662, -0.252839], [-0.015988, -0.913024, -0.407593]] and translation vector: [3.69525, 3.551647, 1.352095], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.292652, -0.378333, 0.878191], [-0.956147, 0.127043, -0.2639], [-0.011726, -0.91691, -0.398922]] and translation vector: [3.694781, 3.553972, 1.346799], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_99_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_99_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_99_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_99_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_99_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_99_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.988959, -0.006087, -0.148062], [0.148117, 0.009943, 0.98892], [-0.004548, -0.999932, 0.010735]] and translation vector: [3.911582, 2.672538, 1.565046], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.987297, -0.007995, -0.158684], [0.158774, 0.012251, 0.987239], [-0.005949, -0.999893, 0.013365]] and translation vector: [3.955948, 2.679338, 1.574419], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_100_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_100_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_100_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_100_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_100_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_100_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.95695, -0.100486, 0.272304], [-0.288986, 0.24231, -0.92616], [0.027085, -0.964981, -0.260918]] and translation vector: [1.227478, 4.879099, 1.55452], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.957752, -0.097454, 0.27058], [-0.286469, 0.240112, -0.927514], [0.025421, -0.965841, -0.257885]] and translation vector: [1.221714, 4.885019, 1.554874], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_101_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_101_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_101_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_101_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_101_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_101_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.895509, 0.17248, -0.410263], [0.444823, 0.375965, -0.812886], [0.014038, -0.91044, -0.413402]] and translation vector: [2.818061, 5.409916, 1.54775], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.895274, 0.172164, -0.410907], [0.445264, 0.376844, -0.812237], [0.01501, -0.910136, -0.414037]] and translation vector: [2.819061, 5.407142, 1.548651], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_102_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_102_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_102_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_102_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_102_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_102_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.355681, -0.20797, 0.911175], [-0.934036, 0.113197, -0.338769], [-0.032689, -0.971563, -0.234514]] and translation vector: [0.539195, 4.841905, 1.636959], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.354881, -0.205091, 0.912139], [-0.934375, 0.110848, -0.338608], [-0.031664, -0.972446, -0.230969]] and translation vector: [0.533365, 4.84225, 1.627512], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_103_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_103_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_103_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_103_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_103_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_103_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.506976, -0.449046, 0.735753], [-0.861802, 0.247713, -0.442646], [0.016513, -0.858485, -0.512574]] and translation vector: [1.568574, 4.423309, 1.333385], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.503836, -0.444181, 0.740846], [-0.863753, 0.25025, -0.437385], [0.008882, -0.860278, -0.509748]] and translation vector: [1.576928, 4.418399, 1.331934], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_104_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_104_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_104_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_104_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_104_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_104_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.221984, 0.421429, -0.879273], [0.97466, 0.121427, -0.187867], [0.027595, -0.898695, -0.437705]] and translation vector: [3.155292, 0.483793, 1.35371], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.224547, 0.416482, -0.880978], [0.973822, 0.128715, -0.187361], [0.035363, -0.899986, -0.434482]] and translation vector: [3.157119, 0.483672, 1.354178], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_105_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_105_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_105_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_105_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_105_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_105_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.997074, 0.061747, -0.045056], [0.074474, 0.651998, -0.754554], [-0.017215, -0.755702, -0.654689]] and translation vector: [1.815792, 5.369752, 1.288561], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.994543, 0.080066, -0.066881], [0.102674, 0.63762, -0.763478], [-0.018484, -0.766179, -0.642361]] and translation vector: [1.819087, 5.36055, 1.286161], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_106_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_106_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_106_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_106_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_106_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_106_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.476704, 0.41796, -0.773345], [0.878176, 0.186897, -0.440314], [-0.039498, -0.889033, -0.456137]] and translation vector: [2.405627, 4.675593, 1.276166], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.455958, 0.42895, -0.779811], [0.88909, 0.179883, -0.420905], [-0.040272, -0.885237, -0.463394]] and translation vector: [2.408911, 4.675395, 1.276879], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_107_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_107_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_107_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_107_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_107_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_107_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.573165, 0.475287, -0.667521], [0.819422, -0.337921, 0.462988], [-0.005517, -0.81235, -0.583144]] and translation vector: [4.230747, 1.597944, 1.425469], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.580595, 0.472456, -0.663095], [0.814187, -0.339873, 0.470729], [-0.002969, -0.813186, -0.581996]] and translation vector: [4.228813, 1.597838, 1.42741], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_108_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_108_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_108_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_108_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_108_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_108_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.218501, -0.721835, 0.656667], [-0.97193, -0.10083, 0.212566], [-0.087226, -0.684681, -0.723605]] and translation vector: [2.10902, 2.428258, 1.386435], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.218569, -0.722397, 0.656026], [-0.971546, -0.098231, 0.215522], [-0.091251, -0.684466, -0.723312]] and translation vector: [2.107975, 2.430531, 1.385643], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_109_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_109_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_109_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_109_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_109_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_109_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.819759, -0.274444, 0.502669], [-0.572709, 0.39303, -0.719397], [-0.00013, -0.877615, -0.479366]] and translation vector: [2.765326, 1.370172, 1.355227], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.819555, -0.26888, 0.505998], [-0.572993, 0.389095, -0.721307], [-0.002936, -0.881084, -0.472951]] and translation vector: [2.765196, 1.369276, 1.358405], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_110_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_110_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_110_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_110_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_110_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_110_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.769532, -0.429513, 0.472588], [-0.615738, -0.302759, 0.727464], [-0.169375, -0.850797, -0.49745]] and translation vector: [2.184386, 2.253813, 1.283805], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.76638, -0.428136, 0.478917], [-0.620171, -0.298738, 0.725357], [-0.167481, -0.85291, -0.494464]] and translation vector: [2.185226, 2.257666, 1.286817], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_111_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_111_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_111_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_111_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_111_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_111_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.493838, -0.420518, 0.76111], [-0.864926, -0.147366, 0.479777], [-0.089593, -0.895236, -0.436493]] and translation vector: [0.736944, 2.108944, 1.402726], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.487676, -0.423405, 0.763479], [-0.869284, -0.154634, 0.469504], [-0.080731, -0.892646, -0.443471]] and translation vector: [0.733117, 2.095654, 1.39687], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_112_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_112_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_112_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_112_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_112_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_112_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.341382, 0.594812, -0.727775], [0.932196, 0.11517, -0.343142], [-0.120287, -0.795572, -0.593798]] and translation vector: [7.151203, 3.587152, 1.581923], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.344041, 0.585523, -0.734029], [0.930897, 0.110501, -0.348168], [-0.122749, -0.803089, -0.583079]] and translation vector: [7.150104, 3.60012, 1.584136], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_113_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_113_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_113_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_113_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_113_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_113_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.060487, 0.154719, -0.986105], [0.998165, 0.006603, -0.060191], [-0.002801, -0.987936, -0.154835]] and translation vector: [6.630666, 2.572317, 1.44523], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.062036, 0.175232, -0.982571], [0.998074, 0.011306, -0.060998], [0.00042, -0.984462, -0.175596]] and translation vector: [6.62843, 2.567178, 1.442285], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_114_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_114_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_114_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_114_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_114_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_114_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.207705, 0.494542, -0.843971], [0.97739, -0.069996, 0.199524], [0.039599, -0.866331, -0.497898]] and translation vector: [4.53083, 2.291093, 1.52739], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.209269, 0.494574, -0.843566], [0.977066, -0.071037, 0.200739], [0.039356, -0.866228, -0.498097]] and translation vector: [4.529976, 2.291335, 1.526507], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_115_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_115_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_115_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_115_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_115_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_115_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.778266, 0.076502, -0.623257], [0.626532, 0.028295, -0.778882], [-0.041951, -0.996668, -0.069952]] and translation vector: [4.354075, 2.27787, 1.510689], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.774603, 0.078895, -0.627508], [0.631084, 0.031306, -0.775082], [-0.041505, -0.996391, -0.074039]] and translation vector: [4.353431, 2.276987, 1.507071], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_116_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_116_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_116_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_116_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_116_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_116_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.982764, 0.054289, -0.17671], [0.184841, -0.27426, 0.943724], [0.002769, -0.960122, -0.279568]] and translation vector: [4.072058, 1.220293, 1.47625], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.982485, 0.057917, -0.177113], [0.186218, -0.270474, 0.944546], [0.0068, -0.960984, -0.276522]] and translation vector: [4.071517, 1.218265, 1.477941], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_117_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_117_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_117_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_117_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_117_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_117_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.286652, 0.220257, -0.932372], [0.958024, -0.061246, 0.28007], [0.004584, -0.973517, -0.228568]] and translation vector: [3.76659, 1.676076, 1.452194], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.299829, 0.216367, -0.929133], [0.953977, -0.07366, 0.290693], [-0.005544, -0.973529, -0.228495]] and translation vector: [3.753121, 1.670498, 1.452776], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_118_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_118_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_118_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_118_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_118_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_118_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.848489, -0.131122, 0.512712], [-0.527579, 0.133483, -0.838954], [0.041567, -0.982339, -0.182436]] and translation vector: [2.702568, 1.718074, 1.602473], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.851363, -0.128939, 0.508484], [-0.523333, 0.142037, -0.840207], [0.036112, -0.981428, -0.188403]] and translation vector: [2.706553, 1.721294, 1.602035], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_119_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_119_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_119_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_119_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_119_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_119_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.764638, 0.028658, -0.643823], [0.64431, -0.055554, 0.762744], [-0.013909, -0.998044, -0.060944]] and translation vector: [3.061982, 3.98913, 1.495508], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.765028, 0.027801, -0.643396], [0.643825, -0.056098, 0.763114], [-0.014878, -0.998038, -0.060816]] and translation vector: [3.064652, 3.991985, 1.487138], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_120_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_120_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_120_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_120_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_120_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_120_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.935878, -0.161972, 0.312885], [-0.352322, 0.433116, -0.829627], [-0.001139, -0.886666, -0.46241]] and translation vector: [1.123681, 2.231354, 1.408983], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.935522, -0.159, 0.315466], [-0.353249, 0.430874, -0.830399], [-0.003893, -0.888294, -0.459258]] and translation vector: [1.123559, 2.231523, 1.408322], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_121_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_121_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_121_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_121_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_121_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_121_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.45377, -0.425062, 0.783208], [-0.891046, 0.227634, -0.392708], [-0.01136, -0.876074, -0.482043]] and translation vector: [2.25004, 3.862298, 1.519108], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.453547, -0.422981, 0.784463], [-0.891155, 0.226808, -0.392938], [-0.011717, -0.877294, -0.47981]] and translation vector: [2.249275, 3.861866, 1.519019], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_122_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_122_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_122_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_122_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_122_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_122_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.205292, 0.226186, -0.952205], [0.97316, -0.150555, 0.174048], [-0.103992, -0.962379, -0.251024]] and translation vector: [4.876985, 2.837537, 1.671042], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.210488, 0.22021, -0.952472], [0.971775, -0.153305, 0.17931], [-0.106533, -0.96333, -0.246263]] and translation vector: [4.87733, 2.840179, 1.675237], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_123_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_123_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_123_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_123_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_123_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_123_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.037281, 0.595041, -0.80283], [0.998378, -0.012419, -0.055566], [-0.043034, -0.803599, -0.593613]] and translation vector: [3.95675, 2.244474, 1.442954], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.038109, 0.594465, -0.803218], [0.998341, -0.012073, -0.056302], [-0.043167, -0.80403, -0.593019]] and translation vector: [3.957906, 2.244142, 1.441716], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_124_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_124_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_124_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_124_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_124_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_124_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.070416, -0.411804, 0.908548], [-0.99671, 0.065705, -0.047468], [-0.040148, -0.908901, -0.415075]] and translation vector: [2.214543, 1.806687, 1.391502], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.072195, -0.409813, 0.909308], [-0.996578, 0.066438, -0.049181], [-0.040258, -0.909747, -0.413207]] and translation vector: [2.216063, 1.808517, 1.395188], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_125_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_125_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_125_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_125_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_125_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_125_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.086843, 0.425015, -0.901011], [0.995696, 0.066429, -0.064634], [0.032383, -0.902745, -0.428955]] and translation vector: [4.261571, 5.85756, 1.66629], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.086953, 0.422316, -0.902268], [0.995713, 0.06553, -0.065286], [0.031554, -0.904077, -0.426204]] and translation vector: [4.260677, 5.865657, 1.669414], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_126_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_126_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_126_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_126_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_126_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_126_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.882784, 0.25224, -0.396318], [0.469583, -0.498211, 0.728888], [-0.013595, -0.829554, -0.55826]] and translation vector: [3.463734, 1.394934, 1.262723], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.883097, 0.250738, -0.396574], [0.468931, -0.499833, 0.728197], [-0.015634, -0.829034, -0.558979]] and translation vector: [3.462241, 1.393432, 1.262782], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_127_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_127_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_127_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_127_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_127_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_127_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.079656, -0.319192, 0.944337], [-0.994012, 0.096527, -0.051219], [-0.074805, -0.942762, -0.324969]] and translation vector: [4.3352, 2.935251, 1.464921], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.08136, -0.319768, 0.943996], [-0.993796, 0.098086, -0.052427], [-0.075828, -0.942405, -0.325765]] and translation vector: [4.335558, 2.933583, 1.460394], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_128_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_128_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_128_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_128_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_128_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_128_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.255252, -0.433184, 0.864406], [-0.966562, 0.137073, -0.216725], [-0.024605, -0.890821, -0.453687]] and translation vector: [1.468232, 3.881342, 1.432686], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.253329, -0.437174, 0.862962], [-0.967015, 0.138948, -0.213484], [-0.026577, -0.888579, -0.457953]] and translation vector: [1.469363, 3.879031, 1.438972], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_129_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_129_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_129_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_129_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_129_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_129_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.999403, 0.004498, 0.03425], [-0.034232, -0.004158, 0.999405], [0.004638, -0.999981, -0.004001]] and translation vector: [2.393484, 5.775056, 1.371464], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.998454, -0.001139, 0.055575], [-0.055569, 0.004857, 0.998443], [-0.001408, -0.999988, 0.004786]] and translation vector: [2.356134, 5.774678, 1.367739], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_130_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_130_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_130_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_130_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_130_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_130_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.233902, -0.58763, 0.774584], [-0.967246, -0.059828, 0.246692], [-0.098622, -0.806915, -0.582377]] and translation vector: [0.860343, 3.117731, 1.418568], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.233684, -0.587102, 0.775051], [-0.967496, -0.061159, 0.24538], [-0.096661, -0.8072, -0.58231]] and translation vector: [0.859973, 3.119137, 1.418853], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_131_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_131_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_131_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_131_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_131_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_131_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.767458, -0.265442, 0.583565], [-0.640543, 0.35536, -0.680752], [-0.026676, -0.896248, -0.442751]] and translation vector: [3.343537, 3.697402, 1.375352], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.780866, -0.263741, 0.566294], [-0.624403, 0.357431, -0.694525], [-0.019236, -0.895926, -0.443786]] and translation vector: [3.344022, 3.709659, 1.376654], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_132_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_132_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_132_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_132_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_132_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_132_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.623567, 0.536294, -0.568817], [0.781209, -0.455034, 0.427384], [-0.029628, -0.710867, -0.702702]] and translation vector: [1.790477, 1.816361, 1.229059], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.636074, 0.528408, -0.562313], [0.771074, -0.462894, 0.437235], [-0.029252, -0.711698, -0.701876]] and translation vector: [1.794875, 1.819226, 1.230937], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_133_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_133_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_133_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_133_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_133_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_133_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.140295, 0.625342, -0.767636], [0.990108, -0.090149, 0.107516], [-0.001967, -0.775126, -0.631804]] and translation vector: [3.410891, 3.073526, 1.198756], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.148525, 0.612201, -0.776627], [0.988818, -0.102561, 0.108258], [-0.013376, -0.784022, -0.620589]] and translation vector: [3.421496, 3.097678, 1.206193], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_134_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_134_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_134_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_134_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_134_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_134_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.14018, 0.443083, -0.885453], [0.989985, -0.07783, 0.117782], [-0.016727, -0.893096, -0.449556]] and translation vector: [3.549726, 0.935059, 1.485921], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.140682, 0.443565, -0.885132], [0.989931, -0.077142, 0.11868], [-0.015638, -0.892916, -0.449951]] and translation vector: [3.549777, 0.934132, 1.483108], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_135_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_135_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_135_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_135_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_135_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_135_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.631332, 0.312126, -0.709927], [0.775472, -0.26347, 0.573784], [-0.007951, -0.912776, -0.408382]] and translation vector: [1.600176, 0.624978, 1.327739], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.627277, 0.311053, -0.713982], [0.778666, -0.267257, 0.567673], [-0.014241, -0.912041, -0.409851]] and translation vector: [1.601099, 0.627571, 1.328079], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_136_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_136_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_136_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_136_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_136_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_136_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.963317, 0.154363, -0.219528], [0.260086, 0.335369, -0.905474], [-0.066149, -0.929355, -0.363214]] and translation vector: [5.972451, 2.818726, 1.468896], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.963149, 0.154275, -0.220326], [0.260736, 0.334417, -0.905639], [-0.066037, -0.929712, -0.362318]] and translation vector: [5.973901, 2.819783, 1.467855], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_137_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_137_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_137_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_137_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_137_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_137_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.983299, 0.047874, -0.175588], [0.180439, -0.382417, 0.9062], [-0.023764, -0.922749, -0.384668]] and translation vector: [2.208684, 3.483128, 1.468268], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.982577, 0.045136, -0.18029], [0.183889, -0.376806, 0.907856], [-0.026957, -0.925192, -0.378541]] and translation vector: [2.211137, 3.481059, 1.465482], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_138_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_138_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_138_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_138_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_138_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_138_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.824719, -0.175736, 0.537546], [-0.564369, 0.316962, -0.762249], [-0.036427, -0.932015, -0.360584]] and translation vector: [4.397487, 4.054199, 1.411764], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.821778, -0.181799, 0.540028], [-0.568729, 0.319986, -0.757731], [-0.035047, -0.929816, -0.366351]] and translation vector: [4.391561, 4.044915, 1.406417], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_139_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_139_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_139_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_139_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_139_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_139_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.804945, -0.278842, 0.523748], [-0.593014, 0.407765, -0.694307], [-0.019964, -0.869468, -0.493585]] and translation vector: [4.871809, 2.494869, 1.402737], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.804444, -0.274614, 0.526742], [-0.593612, 0.404842, -0.695506], [-0.022252, -0.872176, -0.488687]] and translation vector: [4.863627, 2.491699, 1.400121], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_140_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_140_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_140_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_140_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_140_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_140_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.000188, -0.47362, 0.88073], [-0.997828, 0.057931, 0.031365], [-0.065877, -0.878822, -0.47258]] and translation vector: [4.366519, 5.511691, 1.307889], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.002248, -0.465195, 0.885205], [-0.998254, 0.053289, 0.02547], [-0.05902, -0.883603, -0.464503]] and translation vector: [4.36891, 5.516212, 1.317108], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_141_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_141_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_141_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_141_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_141_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_141_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.881415, -0.308012, 0.3581], [-0.47008, 0.646119, -0.601294], [-0.046169, -0.698325, -0.71429]] and translation vector: [3.147524, 1.689608, 1.273114], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.879224, -0.311908, 0.360109], [-0.474637, 0.638627, -0.605703], [-0.041052, -0.703469, -0.709539]] and translation vector: [3.141599, 1.689583, 1.27073], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_142_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_142_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_142_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_142_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_142_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_142_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.954506, 0.05554, -0.292973], [0.288831, -0.41644, 0.862064], [-0.074127, -0.907465, -0.413536]] and translation vector: [2.66447, 1.005586, 1.476015], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.956668, 0.052296, -0.286448], [0.280824, -0.425753, 0.860158], [-0.076973, -0.903327, -0.42199]] and translation vector: [2.657996, 1.004761, 1.470821], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_143_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_143_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_143_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_143_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_143_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_143_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.804414, -0.195207, 0.561082], [-0.593456, -0.306943, 0.74404], [0.026978, -0.931494, -0.362756]] and translation vector: [4.397897, 1.805397, 1.263968], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.81043, -0.19082, 0.553888], [-0.585149, -0.309439, 0.749566], [0.028363, -0.931577, -0.362436]] and translation vector: [4.406421, 1.797547, 1.276681], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_144_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_144_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_144_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_144_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_144_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_144_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.924593, 0.219455, -0.311397], [0.371095, 0.334047, -0.86643], [-0.086121, -0.916653, -0.390296]] and translation vector: [7.650298, 2.745242, 1.444521], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.925403, 0.221817, -0.30729], [0.368562, 0.337876, -0.866026], [-0.088274, -0.914679, -0.394425]] and translation vector: [7.650829, 2.747432, 1.442508], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_145_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_145_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_145_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_145_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_145_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_145_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.173351, 0.592298, -0.78685], [0.984858, -0.105806, 0.137329], [-0.001913, -0.798742, -0.601671]] and translation vector: [3.264189, 1.940071, 1.28435], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.172933, 0.589263, -0.789217], [0.98493, -0.105695, 0.136901], [-0.002745, -0.800998, -0.598661]] and translation vector: [3.267153, 1.942133, 1.284021], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_146_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_146_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_146_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_146_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_146_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_146_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.861262, 0.35211, -0.366398], [0.508128, 0.60504, -0.61297], [0.005853, -0.714105, -0.700014]] and translation vector: [3.145762, 3.637784, 1.437024], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.859655, 0.347273, -0.374693], [0.510745, 0.600786, -0.614977], [0.011546, -0.720041, -0.693836]] and translation vector: [3.145171, 3.63531, 1.440385], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_147_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_147_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_147_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_147_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_147_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_147_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.927869, -0.125596, 0.351119], [-0.372891, -0.32108, 0.870551], [0.003399, -0.938687, -0.344754]] and translation vector: [5.442723, 4.031985, 1.348893], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.928984, -0.124208, 0.348657], [-0.370086, -0.32475, 0.870387], [0.005117, -0.937609, -0.347654]] and translation vector: [5.438782, 4.038163, 1.363364], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_148_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_148_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_148_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_148_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_148_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_148_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.32152, -0.4706, 0.821681], [-0.946681, 0.178549, -0.268172], [-0.020508, -0.864092, -0.502915]] and translation vector: [2.120097, 2.367636, 1.494245], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.324752, -0.471365, 0.819971], [-0.945715, 0.173395, -0.274877], [-0.012612, -0.864725, -0.502087]] and translation vector: [2.101204, 2.346659, 1.492081], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_149_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_149_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_149_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_149_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_149_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_149_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.857694, 0.203115, -0.472341], [0.513544, 0.293426, -0.806333], [-0.025181, -0.934155, -0.355978]] and translation vector: [3.161674, 3.662206, 1.335287], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.856666, 0.203827, -0.473897], [0.515344, 0.296604, -0.804019], [-0.023321, -0.932995, -0.359132]] and translation vector: [3.164327, 3.659025, 1.330704], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_150_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_150_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_150_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_150_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_150_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_150_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.975982, 0.033782, -0.215214], [0.215389, -0.297687, 0.930048], [-0.032648, -0.954066, -0.297814]] and translation vector: [2.838751, 1.414222, 1.664536], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.976127, 0.034525, -0.21444], [0.21483, -0.298963, 0.929769], [-0.03201, -0.95364, -0.299243]] and translation vector: [2.83798, 1.414721, 1.663024], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_151_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_151_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_151_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_151_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_151_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_151_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.844798, -0.442354, 0.301064], [-0.534849, 0.714819, -0.450523], [-0.015916, -0.541624, -0.84047]] and translation vector: [3.085932, 7.995926, 1.934485], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.833593, -0.457276, 0.309873], [-0.552243, 0.702368, -0.449118], [-0.012274, -0.545507, -0.838017]] and translation vector: [3.091993, 8.002051, 1.93396], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_152_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_152_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_152_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_152_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_152_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_152_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.956223, -0.170898, 0.237554], [-0.292595, -0.544035, 0.786393], [-0.005155, -0.821474, -0.570223]] and translation vector: [1.275326, 2.834272, 1.3185], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.956815, -0.170774, 0.235249], [-0.290631, -0.544392, 0.786875], [-0.00631, -0.821263, -0.570514]] and translation vector: [1.276568, 2.833979, 1.318089], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_153_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_153_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_153_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_153_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_153_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_153_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.852441, 0.228219, -0.470383], [0.522431, 0.337001, -0.78326], [-0.020235, -0.913426, -0.406502]] and translation vector: [1.798405, 5.320803, 1.619482], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.850776, 0.231102, -0.471988], [0.52508, 0.336676, -0.781627], [-0.021728, -0.91282, -0.407783]] and translation vector: [1.793927, 5.32593, 1.618758], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_154_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_154_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_154_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_154_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_154_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_154_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.999494, 0.005595, 0.031322], [-0.029883, 0.172936, -0.98448], [-0.010925, -0.984917, -0.172681]] and translation vector: [6.687301, 5.436423, 1.742894], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.999393, 0.00615, 0.034285], [-0.032681, 0.175053, -0.984017], [-0.012053, -0.98454, -0.174746]] and translation vector: [6.681215, 5.427393, 1.75699], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_155_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_155_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_155_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_155_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_155_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_155_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.99336, -0.011945, -0.114427], [0.103059, -0.349694, 0.931178], [-0.051137, -0.936788, -0.346141]] and translation vector: [2.948285, 4.432959, 1.460427], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.99314, -0.016022, -0.115825], [0.102925, -0.35027, 0.930977], [-0.055486, -0.936512, -0.346218]] and translation vector: [2.949102, 4.433566, 1.463483], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_156_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_156_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_156_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_156_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_156_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_156_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.40936, -0.486807, 0.77165], [-0.912164, 0.236459, -0.334729], [-0.019515, -0.840896, -0.540844]] and translation vector: [1.412713, 1.214489, 1.390939], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.417972, -0.487805, 0.766384], [-0.908352, 0.237425, -0.344277], [-0.014019, -0.840045, -0.542336]] and translation vector: [1.411881, 1.212071, 1.390231], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_157_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_157_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_157_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_157_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_157_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_157_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.442667, -0.46733, 0.765277], [-0.896368, 0.253361, -0.363776], [-0.023888, -0.847001, -0.531054]] and translation vector: [2.453469, 1.905797, 1.451684], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.441405, -0.472001, 0.763136], [-0.897015, 0.253848, -0.361837], [-0.022933, -0.844261, -0.535442]] and translation vector: [2.45238, 1.90449, 1.449179], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_158_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_158_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_158_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_158_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_158_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_158_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.88123, -0.188698, 0.433389], [-0.470321, -0.258404, 0.843816], [-0.047237, -0.947428, -0.316462]] and translation vector: [1.061636, 1.321782, 1.457525], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.879526, -0.187337, 0.437423], [-0.473303, -0.249401, 0.844857], [-0.049179, -0.950107, -0.308022]] and translation vector: [1.052651, 1.315727, 1.459226], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_159_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_159_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_159_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_159_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_159_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_159_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.15851, 0.420096, -0.893529], [0.981106, -0.034663, -0.190342], [-0.110934, -0.906817, -0.406664]] and translation vector: [4.004256, 0.910349, 2.578562], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.153085, 0.419732, -0.894645], [0.982322, -0.034068, -0.184071], [-0.107739, -0.907009, -0.407097]] and translation vector: [4.005316, 0.908549, 2.574668], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_160_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_160_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_160_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_160_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_160_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_160_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.565317, -0.50256, 0.654103], [-0.824719, 0.328974, -0.460017], [0.016003, -0.799506, -0.600445]] and translation vector: [4.07549, 5.065369, 1.281872], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.538132, -0.502349, 0.676801], [-0.842747, 0.30749, -0.441846], [0.013851, -0.808143, -0.588824]] and translation vector: [4.054681, 5.042427, 1.283033], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_161_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_161_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_161_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_161_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_161_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_161_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.998162, -0.007354, -0.06016], [0.055338, 0.294228, -0.954132], [0.024717, -0.955707, -0.293281]] and translation vector: [1.687981, 4.43329, 1.569003], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.998237, -0.004775, -0.059163], [0.055295, 0.287523, -0.956176], [0.021577, -0.957762, -0.286752]] and translation vector: [1.687716, 4.435163, 1.571974], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_162_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_162_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_162_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_162_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_162_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_162_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.30056, -0.511506, 0.805], [-0.953151, 0.130866, -0.272721], [0.034151, -0.849256, -0.526876]] and translation vector: [-0.281614, 2.924112, 1.306122], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.318531, -0.50267, 0.803655], [-0.947336, 0.139247, -0.288383], [0.033055, -0.85319, -0.520551]] and translation vector: [-0.284617, 2.924129, 1.305331], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_163_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_163_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_163_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_163_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_163_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_163_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.815869, 0.244354, -0.524069], [0.578211, -0.336271, 0.743367], [0.005416, -0.909513, -0.415641]] and translation vector: [2.358014, 1.230078, 1.369842], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.817563, 0.244526, -0.521342], [0.575764, -0.332513, 0.746947], [0.009295, -0.910847, -0.41264]] and translation vector: [2.355037, 1.229076, 1.372478], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_164_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_164_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_164_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_164_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_164_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_164_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.971613, -0.06682, 0.226943], [-0.235147, 0.378036, -0.89543], [-0.02596, -0.923376, -0.383017]] and translation vector: [2.775299, 4.618156, 1.427592], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.969099, -0.066923, 0.237421], [-0.244849, 0.377786, -0.892932], [-0.029937, -0.923471, -0.382498]] and translation vector: [2.770648, 4.620754, 1.418404], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_165_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_165_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_165_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_165_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_165_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_165_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.117057, -0.769276, 0.628102], [-0.987232, -0.021336, 0.157855], [-0.108033, -0.638561, -0.761951]] and translation vector: [1.032686, 1.226834, 2.186959], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.111522, -0.769903, 0.628341], [-0.98843, -0.020525, 0.150284], [-0.102807, -0.637831, -0.763284]] and translation vector: [1.037875, 1.232625, 2.186027], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_166_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_166_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_166_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_166_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_166_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_166_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.053762, 0.423971, -0.904079], [0.99709, -0.071809, 0.025618], [-0.05406, -0.902825, -0.426597]] and translation vector: [3.696534, 7.381392, 1.65485], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.059051, 0.424044, -0.903714], [0.996629, -0.076693, 0.029136], [-0.056954, -0.902388, -0.427143]] and translation vector: [3.693501, 7.384472, 1.654036], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_167_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_167_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_167_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_167_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_167_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_167_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.255196, -0.436856, 0.862573], [-0.966393, 0.143834, -0.213066], [-0.030988, -0.887958, -0.45888]] and translation vector: [1.734999, 0.744851, 1.432124], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.254375, -0.435236, 0.863634], [-0.966628, 0.142475, -0.21291], [-0.03038, -0.888972, -0.456953]] and translation vector: [1.735377, 0.747301, 1.433656], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_168_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_168_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_168_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_168_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_168_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_168_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.299058, 0.37418, -0.877812], [0.95368, -0.085842, 0.288314], [0.032528, -0.923375, -0.38252]] and translation vector: [3.908031, 4.993837, 1.41318], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.301871, 0.365699, -0.880419], [0.952911, -0.087746, 0.290279], [0.028901, -0.926588, -0.374966]] and translation vector: [3.903484, 4.991583, 1.422828], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_169_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_169_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_169_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_169_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_169_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_169_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.349467, 0.022881, -0.936669], [0.936944, -0.011774, 0.349282], [-0.003037, -0.999669, -0.025553]] and translation vector: [3.08553, 2.787215, 1.609269], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.348555, 0.021762, -0.937036], [0.937279, -0.012701, 0.34835], [-0.00432, -0.999682, -0.024824]] and translation vector: [3.086167, 2.787834, 1.610474], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_170_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_170_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_170_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_170_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_170_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_170_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.187285, -0.627824, 0.755488], [-0.982305, 0.118515, -0.145025], [0.001514, -0.76928, -0.63891]] and translation vector: [1.001752, 1.17634, 1.437838], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.187139, -0.630563, 0.75324], [-0.982328, 0.117514, -0.14568], [0.003345, -0.767191, -0.64141]] and translation vector: [1.00191, 1.178201, 1.437088], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_171_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_171_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_171_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_171_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_171_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_171_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.052123, 0.492225, -0.868906], [0.996177, 0.08671, -0.010637], [0.070107, -0.866138, -0.494863]] and translation vector: [3.27549, 2.071379, 1.287401], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.035278, 0.492309, -0.869705], [0.997133, 0.075637, 0.002369], [0.066948, -0.867128, -0.493566]] and translation vector: [3.286684, 2.076202, 1.285681], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_172_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_172_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_172_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_172_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_172_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_172_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.721847, -0.019511, -0.691778], [0.690918, -0.036893, 0.721991], [-0.039608, -0.999129, -0.013151]] and translation vector: [1.871862, 0.815296, 1.594356], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.723033, -0.022358, -0.690452], [0.689637, -0.034974, 0.723311], [-0.04032, -0.999138, -0.009869]] and translation vector: [1.872181, 0.815734, 1.596287], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_173_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_173_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_173_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_173_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_173_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_173_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.112591, -0.547395, 0.829266], [-0.992672, 0.098819, -0.069547], [-0.043877, -0.83102, -0.55451]] and translation vector: [1.18498, 1.814175, 1.496605], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.111637, -0.546351, 0.830083], [-0.992679, 0.100057, -0.067648], [-0.046096, -0.831558, -0.553521]] and translation vector: [1.186424, 1.810214, 1.495373], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_174_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_174_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_174_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_174_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_174_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_174_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.59597, 0.482312, -0.642025], [0.802979, -0.35126, 0.4815], [0.006716, -0.802491, -0.596626]] and translation vector: [3.449961, 1.112515, 1.412234], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.596047, 0.483799, -0.640833], [0.802896, -0.349913, 0.482617], [0.009254, -0.802184, -0.597005]] and translation vector: [3.451157, 1.111087, 1.411899], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_175_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_175_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_175_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_175_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_175_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_175_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.132001, -0.567775, 0.812532], [-0.991224, 0.069667, -0.112349], [0.007182, -0.820231, -0.571988]] and translation vector: [2.407685, 4.450429, 1.359714], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.130918, -0.563466, 0.8157], [-0.991376, 0.069526, -0.111087], [0.005882, -0.823209, -0.567709]] and translation vector: [2.40989, 4.444678, 1.359228], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_176_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_176_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_176_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_176_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_176_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_176_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.443363, -0.325026, 0.835337], [-0.895367, 0.117125, -0.429651], [0.041809, -0.938424, -0.342946]] and translation vector: [2.190343, 3.392878, 1.594635], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.439336, -0.32163, 0.838772], [-0.897253, 0.111545, -0.427195], [0.043838, -0.940272, -0.337589]] and translation vector: [2.183471, 3.393708, 1.586874], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_177_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_177_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_177_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_177_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_177_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_177_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.985254, -0.134646, 0.105573], [-0.142287, -0.302097, 0.942599], [-0.095024, -0.94372, -0.3168]] and translation vector: [1.134605, 1.549487, 1.505245], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.985752, -0.13049, 0.106142], [-0.141062, -0.297585, 0.944216], [-0.091624, -0.945736, -0.311752]] and translation vector: [1.131707, 1.551058, 1.506377], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_178_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_178_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_178_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_178_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_178_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_178_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.753053, 0.123809, -0.646206], [0.619922, -0.462608, 0.633791], [-0.220471, -0.877875, -0.42512]] and translation vector: [4.259223, 3.769218, 1.505729], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.760823, 0.125761, -0.636658], [0.611756, -0.466381, 0.638939], [-0.216572, -0.875599, -0.431768]] and translation vector: [4.257898, 3.775608, 1.505422], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_179_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_179_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_179_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_179_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_179_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_179_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.924746, 0.145405, -0.351715], [0.379908, 0.407811, -0.830277], [0.022707, -0.901414, -0.432362]] and translation vector: [3.891577, 4.106122, 1.335216], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.925289, 0.144931, -0.350479], [0.378485, 0.412032, -0.828842], [0.024284, -0.899569, -0.436102]] and translation vector: [3.892777, 4.104329, 1.336806], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_180_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_180_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_180_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_180_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_180_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_180_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.748873, -0.374013, 0.547087], [-0.662404, -0.447673, 0.600675], [0.020256, -0.812221, -0.582998]] and translation vector: [3.709567, 4.406117, 1.261793], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.747082, -0.370975, 0.551585], [-0.664465, -0.440253, 0.603874], [0.018814, -0.817652, -0.575405]] and translation vector: [3.708719, 4.403161, 1.261416], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_181_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_181_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_181_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_181_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_181_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_181_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.934222, -0.219071, 0.281493], [-0.356558, -0.595286, 0.72007], [0.009823, -0.773073, -0.634241]] and translation vector: [0.331108, 1.989283, 1.551545], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.93341, -0.222981, 0.281114], [-0.358788, -0.589093, 0.724045], [0.004154, -0.776691, -0.629868]] and translation vector: [0.338532, 1.98258, 1.554168], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_182_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_182_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_182_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_182_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_182_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_182_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.52463, -0.231347, 0.819293], [-0.850589, 0.102279, -0.515789], [0.03553, -0.96748, -0.25044]] and translation vector: [5.897326, 2.792535, 1.553822], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.52763, -0.228151, 0.818263], [-0.84888, 0.105585, -0.517933], [0.03177, -0.967884, -0.249382]] and translation vector: [5.897463, 2.790525, 1.551499], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_183_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_183_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_183_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_183_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_183_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_183_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.699126, -0.324611, 0.637064], [-0.713802, 0.265353, -0.648131], [0.041344, -0.907863, -0.417224]] and translation vector: [0.050403, 3.78209, 1.506908], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.698648, -0.327666, 0.636024], [-0.713993, 0.262294, -0.649166], [0.045885, -0.907654, -0.417203]] and translation vector: [0.047406, 3.786517, 1.504266], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_184_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_184_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_184_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_184_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_184_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_184_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.990268, -0.101591, 0.095124], [-0.135934, -0.559426, 0.817658], [-0.029851, -0.822631, -0.567792]] and translation vector: [6.679901, 2.488796, 1.402653], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.989948, -0.105417, 0.094292], [-0.137296, -0.556168, 0.819651], [-0.033963, -0.824357, -0.565051]] and translation vector: [6.681146, 2.493639, 1.408598], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_185_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_185_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_185_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_185_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_185_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_185_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.311411, -0.45253, 0.835607], [-0.948656, 0.199362, -0.245576], [-0.055457, -0.869179, -0.491379]] and translation vector: [2.299133, 2.388773, 1.459468], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.314195, -0.454542, 0.833471], [-0.947818, 0.20019, -0.248124], [-0.05407, -0.867937, -0.493722]] and translation vector: [2.299448, 2.389842, 1.45904], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_186_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_186_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_186_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_186_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_186_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_186_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.789457, 0.162095, -0.592016], [0.613764, 0.197318, -0.764434], [-0.007096, -0.966846, -0.255262]] and translation vector: [5.114759, 3.17533, 1.386193], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.785271, 0.158609, -0.598492], [0.619131, 0.193201, -0.761151], [-0.005096, -0.968255, -0.249915]] and translation vector: [5.11251, 3.170745, 1.383731], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_187_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_187_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_187_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_187_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_187_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_187_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.408988, -0.323891, 0.853126], [-0.912443, -0.158736, 0.37716], [0.013263, -0.932683, -0.360453]] and translation vector: [3.672612, 2.990265, 1.494339], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.403714, -0.307769, 0.861564], [-0.914697, -0.154884, 0.373283], [0.018558, -0.93877, -0.344045]] and translation vector: [3.67724, 2.998002, 1.501107], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_188_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_188_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_188_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_188_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_188_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_188_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.831143, 0.312948, -0.459636], [0.555586, 0.43327, -0.709649], [-0.022937, -0.845187, -0.533978]] and translation vector: [2.360292, 3.05803, 1.315354], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.8108, 0.328121, -0.484706], [0.584922, 0.423558, -0.691711], [-0.021664, -0.844355, -0.535346]] and translation vector: [2.374215, 3.08026, 1.318953], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_189_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_189_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_189_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_189_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_189_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_189_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.802837, 0.056561, -0.593509], [0.596192, 0.071654, -0.799638], [-0.002701, -0.995825, -0.091248]] and translation vector: [2.583219, 4.008804, 1.439254], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.802466, 0.056012, -0.594063], [0.59669, 0.070227, -0.799393], [-0.003056, -0.995957, -0.089777]] and translation vector: [2.583684, 4.008714, 1.434935], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_190_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_190_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_190_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_190_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_190_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_190_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.032646, 0.194727, -0.980314], [0.998594, -0.034636, -0.040135], [-0.04177, -0.980246, -0.193322]] and translation vector: [3.506056, 2.493951, 1.706783], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.038857, 0.192835, -0.980462], [0.998032, -0.040846, -0.047587], [-0.049225, -0.980381, -0.190868]] and translation vector: [3.502031, 2.499079, 1.701362], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_191_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_191_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_191_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_191_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_191_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_191_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.573389, -0.355745, 0.738018], [-0.818965, 0.223754, -0.528424], [0.02285, -0.907403, -0.419641]] and translation vector: [2.061407, 3.857203, 1.382209], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.569689, -0.351701, 0.742806], [-0.821614, 0.221591, -0.525212], [0.020118, -0.909508, -0.4152]] and translation vector: [2.058259, 3.848013, 1.384733], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_192_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_192_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_192_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_192_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_192_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_192_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.863619, -0.252896, 0.436126], [-0.502889, 0.371124, -0.780621], [0.03556, -0.893482, -0.447688]] and translation vector: [2.007098, 3.82416, 1.536992], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.862677, -0.255046, 0.436739], [-0.504412, 0.370978, -0.779707], [0.036841, -0.892932, -0.448682]] and translation vector: [2.007321, 3.81907, 1.542811], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_193_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_193_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_193_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_193_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_193_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_193_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.986418, -0.051155, 0.156087], [-0.152905, 0.633099, -0.758819], [-0.060001, -0.772379, -0.632322]] and translation vector: [2.055195, 1.600374, 1.268236], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.986809, -0.050817, 0.15371], [-0.151071, 0.630346, -0.761474], [-0.058194, -0.77465, -0.629707]] and translation vector: [2.054364, 1.600927, 1.26836], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_194_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_194_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_194_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_194_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_194_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_194_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.436119, -0.427186, 0.79203], [-0.89981, 0.218659, -0.377532], [-0.011909, -0.877326, -0.479747]] and translation vector: [1.992302, 3.72193, 1.553249], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.436462, -0.426736, 0.792084], [-0.899636, 0.219226, -0.377618], [-0.012502, -0.877403, -0.47959]] and translation vector: [1.991236, 3.722176, 1.553282], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "D"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_195_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_195_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_195_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_195_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_195_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_195_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.996429, -0.081152, -0.023325], [-0.01119, 0.400709, -0.916137], [0.083693, -0.912604, -0.400187]] and translation vector: [7.365378, 2.610504, 1.343957], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.997089, -0.075007, -0.013671], [-0.016913, 0.392439, -0.919623], [0.074343, -0.916715, -0.392565]] and translation vector: [7.36531, 2.61944, 1.344548], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_196_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_196_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_196_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_196_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_196_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_196_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.896132, -0.052356, 0.440688], [-0.436974, -0.277444, 0.855616], [0.07747, -0.959314, -0.271505]] and translation vector: [3.211431, 3.110947, 1.584554], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.889709, -0.065096, 0.451863], [-0.451099, -0.277541, 0.848222], [0.070195, -0.958506, -0.276295]] and translation vector: [3.215954, 3.116336, 1.570817], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "A"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_197_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_197_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_197_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_197_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_197_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_197_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.643628, -0.362528, 0.674031], [-0.765241, -0.290748, 0.574345], [-0.012243, -0.88546, -0.464555]] and translation vector: [2.632762, 2.243425, 1.452714], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.642371, -0.361874, 0.675579], [-0.76623, -0.285016, 0.575898], [-0.015852, -0.887589, -0.460364]] and translation vector: [2.634792, 2.237319, 1.452971], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "B"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_198_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_198_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_198_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_198_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_198_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_198_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[-0.612656, -0.411508, 0.674769], [-0.789543, 0.280105, -0.546043], [0.035694, -0.867296, -0.496511]] and translation vector: [1.897828, 2.372103, 1.388776], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[-0.615876, -0.406578, 0.674826], [-0.787242, 0.284147, -0.547275], [0.03076, -0.868305, -0.495075]] and translation vector: [1.892345, 2.36762, 1.390764], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_depth_estimation", "options": "A: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_199_0.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_199_1.jpg", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_199_2.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_199_3.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_199_4.png", "3D-spatial/threeD_Depth_Estimation/threeD_Depth_Estimation_199_5.png"], "question": "Given the first color image view of the scene with the corresponding camera pose, i.e., rotation matrix: [[0.752445, 0.275595, -0.598225], [0.657828, -0.35994, 0.661593], [-0.032994, -0.891342, -0.452129]] and translation vector: [2.633805, 2.70906, 1.31733], and the second color image view of the same scene with the corresponding camera pose, i.e., rotation matrix: [[0.746128, 0.269733, -0.608718], [0.664676, -0.35493, 0.657443], [-0.038718, -0.895136, -0.444108]] and translation vector: [2.667176, 2.689206, 1.310347], please estimate the depth map for the first view of the RGB image. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to estimate the depth map for a color image based on two color images captured from two viewpoints, along with the corresponding camera poses.The input images are the first 2 images\nSelect from the following choices.\nA: The 3th image\nB: The 4th image\nC: The 5th image\nD: The 6th image"}, "output": {"output_text": "C"}, "task": "threeD_Depth_Estimation"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.097, -2.343, -0.119, 0.31, 0.062, 0.564], [-0.682, 2.2, 0.854, 0.245, 0.21, 0.887], [1.912, 1.405, 1.111, 0.468, 0.452, 0.649], [1.666, 2.151, 1.319, 0.566, 0.243, 0.586], [1.68, 1.827, 0.754, -0.048, 0.681, 0.119], [1.224, 1.284, 0.947, -0.031, 0.464, 0.194], [1.776, 1.335, 0.376, 0.886, 0.055, 0.335], [1.559, 1.179, 1.34, -0.063, -0.136, -0.017], [0.95, 0.255, 1.046, -0.137, 0.36, -0.187], [1.987, 0.956, 0.62, 0.226, -0.239, 0.309], [1.652, 0.525, 1.179, 0.104, 0.539, -0.125], [1.409, 0.711, 0.681, 0.259, 0.215, 0.643], [1.086, -0.177, -0.123, 0.706, 0.621, 0.551], [1.203, 1.982, 0.324, 0.158, 0.718, -0.1], [-1.373, -0.521, 0.674, -0.225, 0.726, 0.299], [-0.949, 2.103, 0.551, 0.314, 0.298, 0.513], [-0.76, 1.491, 2.338, 0.158, -0.084, 0.396], [1.233, -0.382, 0.447, 0.091, 0.059, -0.038], [1.9, -1.181, 1.39, 0.188, -0.114, -0.011]]\nB: [[-0.029, -1.923, 0.096, 0.249, 0.113, 0.317], [-0.92, 1.815, 0.646, 0.107, 0.184, 0.516], [1.611, 1.619, 0.954, 0.253, 0.266, 0.249], [1.544, 1.826, 0.949, 0.149, 0.178, 0.182], [1.432, 1.79, 0.87, 0.251, 0.301, 0.054], [1.423, 1.326, 0.897, 0.087, 0.104, 0.188], [1.554, 0.837, 0.836, 0.609, 0.432, 0.17], [1.27, 0.844, 0.842, 0.093, 0.107, 0.179], [1.248, 0.676, 0.764, 0.25, 0.269, 0.064], [1.848, 0.645, 0.825, 0.074, 0.102, 0.07], [1.69, 0.454, 0.837, 0.248, 0.167, 0.182], [1.715, 0.276, 0.86, 0.335, 0.399, 0.17], [1.4, -0.193, 0.037, 0.353, 0.275, 0.161], [1.684, 1.781, 0.29, 0.433, 0.395, 0.339], [-1.62, -0.65, 0.742, 0.272, 0.321, 0.114], [-0.975, 1.832, 0.171, 0.262, 0.195, 0.125], [-1.021, 1.599, 2.098, 0.217, 0.273, 0.221], [1.433, 0.084, 0.074, 0.398, 0.344, 0.176], [1.733, -1.208, 1.076, 0.125, 0.108, 0.327]]\nC: [[0.373, -2.148, -0.291, 0.339, 0.018, 0.694], [-0.499, 2.165, 0.993, 0.276, 0.335, 0.775], [1.74, 1.478, 1.323, -0.069, 0.758, 0.607], [1.532, 2.302, 1.262, 0.285, 0.22, -0.252], [1.323, 1.537, 0.593, 0.351, 0.467, 0.392], [1.364, 1.041, 1.236, 0.12, 0.57, 0.444], [1.442, 1.263, 1.284, 1.004, 0.007, 0.304], [1.115, 0.536, 0.672, -0.113, -0.219, -0.082], [1.743, 0.762, 0.395, 0.159, 0.41, 0.323], [2.121, 0.573, 0.527, -0.324, 0.247, 0.462], [1.447, 0.752, 1.299, 0.299, 0.347, 0.233], [1.92, 0.62, 0.769, -0.13, 0.686, -0.059], [0.942, 0.049, -0.066, 0.316, 0.607, 0.459], [2.077, 2.024, 0.781, 0.373, -0.058, 0.752], [-1.491, -0.599, 0.622, 0.707, -0.171, -0.319], [-1.023, 1.772, -0.236, 0.203, 0.47, 0.117], [-0.596, 1.76, 1.726, 0.197, 0.073, 0.18], [1.574, 0.398, 0.118, 0.732, 0.235, 0.24], [1.654, -1.081, 1.126, -0.043, 0.128, 0.085]]\nD: [[-0.016, -2.182, 0.529, 0.012, -0.234, 0.082], [-0.638, 1.532, 1.107, 0.49, 0.648, 0.861], [1.27, 1.262, 1.438, 0.461, 0.457, 0.658], [1.731, 1.955, 1.411, 0.079, 0.038, 0.636], [1.916, 1.8, 0.455, 0.749, 0.555, 0.441], [1.273, 0.97, 0.909, 0.183, -0.155, 0.402], [1.478, 1.046, 1.305, 0.37, 0.729, 0.224], [1.279, 0.48, 0.354, 0.143, -0.211, 0.086], [1.055, 0.494, 1.055, -0.029, 0.559, -0.151], [2.256, 0.151, 1.167, -0.326, -0.138, 0.075], [1.326, 0.605, 0.815, -0.119, 0.42, 0.177], [2.172, 0.341, 0.688, 0.742, 0.292, 0.566], [1.621, -0.605, 0.175, 0.538, -0.117, 0.628], [1.477, 1.542, -0.082, 0.684, 0.168, -0.065], [-1.675, -0.211, 0.417, 0.169, -0.09, -0.164], [-1.277, 1.624, 0.657, -0.231, 0.334, -0.097], [-0.751, 1.371, 1.707, -0.044, 0.702, 0.452], [1.41, -0.207, 0.284, 0.114, 0.651, -0.312], [1.447, -0.888, 1.553, -0.369, 0.402, 0.174]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_0_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_0_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the object in the scene. The camera pose information includes: the rotation matrix: [[0.86482, -0.183466, 0.467362], [-0.501092, -0.256948, 0.826368], [-0.031523, -0.948851, -0.314147]]; the translation vector: [3.012278, 2.022242, 1.442339], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.097, -2.343, -0.119, 0.31, 0.062, 0.564], [-0.682, 2.2, 0.854, 0.245, 0.21, 0.887], [1.912, 1.405, 1.111, 0.468, 0.452, 0.649], [1.666, 2.151, 1.319, 0.566, 0.243, 0.586], [1.68, 1.827, 0.754, -0.048, 0.681, 0.119], [1.224, 1.284, 0.947, -0.031, 0.464, 0.194], [1.776, 1.335, 0.376, 0.886, 0.055, 0.335], [1.559, 1.179, 1.34, -0.063, -0.136, -0.017], [0.95, 0.255, 1.046, -0.137, 0.36, -0.187], [1.987, 0.956, 0.62, 0.226, -0.239, 0.309], [1.652, 0.525, 1.179, 0.104, 0.539, -0.125], [1.409, 0.711, 0.681, 0.259, 0.215, 0.643], [1.086, -0.177, -0.123, 0.706, 0.621, 0.551], [1.203, 1.982, 0.324, 0.158, 0.718, -0.1], [-1.373, -0.521, 0.674, -0.225, 0.726, 0.299], [-0.949, 2.103, 0.551, 0.314, 0.298, 0.513], [-0.76, 1.491, 2.338, 0.158, -0.084, 0.396], [1.233, -0.382, 0.447, 0.091, 0.059, -0.038], [1.9, -1.181, 1.39, 0.188, -0.114, -0.011]]\nB: [[-0.029, -1.923, 0.096, 0.249, 0.113, 0.317], [-0.92, 1.815, 0.646, 0.107, 0.184, 0.516], [1.611, 1.619, 0.954, 0.253, 0.266, 0.249], [1.544, 1.826, 0.949, 0.149, 0.178, 0.182], [1.432, 1.79, 0.87, 0.251, 0.301, 0.054], [1.423, 1.326, 0.897, 0.087, 0.104, 0.188], [1.554, 0.837, 0.836, 0.609, 0.432, 0.17], [1.27, 0.844, 0.842, 0.093, 0.107, 0.179], [1.248, 0.676, 0.764, 0.25, 0.269, 0.064], [1.848, 0.645, 0.825, 0.074, 0.102, 0.07], [1.69, 0.454, 0.837, 0.248, 0.167, 0.182], [1.715, 0.276, 0.86, 0.335, 0.399, 0.17], [1.4, -0.193, 0.037, 0.353, 0.275, 0.161], [1.684, 1.781, 0.29, 0.433, 0.395, 0.339], [-1.62, -0.65, 0.742, 0.272, 0.321, 0.114], [-0.975, 1.832, 0.171, 0.262, 0.195, 0.125], [-1.021, 1.599, 2.098, 0.217, 0.273, 0.221], [1.433, 0.084, 0.074, 0.398, 0.344, 0.176], [1.733, -1.208, 1.076, 0.125, 0.108, 0.327]]\nC: [[0.373, -2.148, -0.291, 0.339, 0.018, 0.694], [-0.499, 2.165, 0.993, 0.276, 0.335, 0.775], [1.74, 1.478, 1.323, -0.069, 0.758, 0.607], [1.532, 2.302, 1.262, 0.285, 0.22, -0.252], [1.323, 1.537, 0.593, 0.351, 0.467, 0.392], [1.364, 1.041, 1.236, 0.12, 0.57, 0.444], [1.442, 1.263, 1.284, 1.004, 0.007, 0.304], [1.115, 0.536, 0.672, -0.113, -0.219, -0.082], [1.743, 0.762, 0.395, 0.159, 0.41, 0.323], [2.121, 0.573, 0.527, -0.324, 0.247, 0.462], [1.447, 0.752, 1.299, 0.299, 0.347, 0.233], [1.92, 0.62, 0.769, -0.13, 0.686, -0.059], [0.942, 0.049, -0.066, 0.316, 0.607, 0.459], [2.077, 2.024, 0.781, 0.373, -0.058, 0.752], [-1.491, -0.599, 0.622, 0.707, -0.171, -0.319], [-1.023, 1.772, -0.236, 0.203, 0.47, 0.117], [-0.596, 1.76, 1.726, 0.197, 0.073, 0.18], [1.574, 0.398, 0.118, 0.732, 0.235, 0.24], [1.654, -1.081, 1.126, -0.043, 0.128, 0.085]]\nD: [[-0.016, -2.182, 0.529, 0.012, -0.234, 0.082], [-0.638, 1.532, 1.107, 0.49, 0.648, 0.861], [1.27, 1.262, 1.438, 0.461, 0.457, 0.658], [1.731, 1.955, 1.411, 0.079, 0.038, 0.636], [1.916, 1.8, 0.455, 0.749, 0.555, 0.441], [1.273, 0.97, 0.909, 0.183, -0.155, 0.402], [1.478, 1.046, 1.305, 0.37, 0.729, 0.224], [1.279, 0.48, 0.354, 0.143, -0.211, 0.086], [1.055, 0.494, 1.055, -0.029, 0.559, -0.151], [2.256, 0.151, 1.167, -0.326, -0.138, 0.075], [1.326, 0.605, 0.815, -0.119, 0.42, 0.177], [2.172, 0.341, 0.688, 0.742, 0.292, 0.566], [1.621, -0.605, 0.175, 0.538, -0.117, 0.628], [1.477, 1.542, -0.082, 0.684, 0.168, -0.065], [-1.675, -0.211, 0.417, 0.169, -0.09, -0.164], [-1.277, 1.624, 0.657, -0.231, 0.334, -0.097], [-0.751, 1.371, 1.707, -0.044, 0.702, 0.452], [1.41, -0.207, 0.284, 0.114, 0.651, -0.312], [1.447, -0.888, 1.553, -0.369, 0.402, 0.174]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.373, -1.08, 0.874, 0.298, 4.385, 1.982], [2.17, -0.036, 1.086, 0.309, 6.903, 1.887], [0.087, 4.155, 1.455, 2.931, 0.24, 1.054], [-2.394, 2.523, 0.998, 0.208, 1.864, 1.096]]\nB: [[-2.011, -0.956, 1.222, 0.755, 4.764, 2.451], [2.068, 0.255, 0.587, 0.436, 7.258, 1.991], [0.329, 4.55, 1.265, 3.421, 0.529, 1.367], [-2.081, 2.555, 0.715, 0.343, 1.488, 1.021]]\nC: [[-2.751, -0.591, 0.65, -0.115, 4.495, 2.351], [1.978, 0.331, 1.034, 0.171, 7.03, 2.051], [0.03, 3.84, 1.693, 3.348, 0.554, 1.247], [-2.636, 2.957, 1.408, -0.018, 1.435, 1.132]]\nD: [[-2.401, -1.35, 0.706, 0.08, 4.387, 2.134], [2.651, -0.401, 0.766, 0.612, 6.557, 1.511], [0.041, 3.914, 1.655, 3.173, 0.701, 0.821], [-2.147, 2.752, 0.898, 0.388, 2.028, 1.081]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_1_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_1_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.060487, 0.154719, -0.986105], [0.998165, 0.006603, -0.060191], [-0.002801, -0.987936, -0.154835]]; the translation vector: [6.630666, 2.572317, 1.44523], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.373, -1.08, 0.874, 0.298, 4.385, 1.982], [2.17, -0.036, 1.086, 0.309, 6.903, 1.887], [0.087, 4.155, 1.455, 2.931, 0.24, 1.054], [-2.394, 2.523, 0.998, 0.208, 1.864, 1.096]]\nB: [[-2.011, -0.956, 1.222, 0.755, 4.764, 2.451], [2.068, 0.255, 0.587, 0.436, 7.258, 1.991], [0.329, 4.55, 1.265, 3.421, 0.529, 1.367], [-2.081, 2.555, 0.715, 0.343, 1.488, 1.021]]\nC: [[-2.751, -0.591, 0.65, -0.115, 4.495, 2.351], [1.978, 0.331, 1.034, 0.171, 7.03, 2.051], [0.03, 3.84, 1.693, 3.348, 0.554, 1.247], [-2.636, 2.957, 1.408, -0.018, 1.435, 1.132]]\nD: [[-2.401, -1.35, 0.706, 0.08, 4.387, 2.134], [2.651, -0.401, 0.766, 0.612, 6.557, 1.511], [0.041, 3.914, 1.655, 3.173, 0.701, 0.821], [-2.147, 2.752, 0.898, 0.388, 2.028, 1.081]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.552, 0.743, 0.817, 1.009, -0.013, 1.113], [0.943, 1.174, 0.929, -0.18, 0.779, 1.068]]\nB: [[0.748, 0.782, 0.621, 0.556, 0.127, 0.839], [1.697, 1.131, 0.375, -0.273, 1.039, 1.495]]\nC: [[0.612, 1.202, 0.708, 0.529, 0.114, 0.821], [1.612, 1.184, 0.198, 0.419, 0.977, 0.647]]\nD: [[0.368, 1.181, 0.615, 0.989, 0.028, 1.271], [1.284, 0.726, 0.504, 0.067, 0.905, 1.037]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_2_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_2_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the doorframe in the scene. The camera pose information includes: the rotation matrix: [[-0.610102, 0.375008, -0.697958], [0.791763, 0.255448, -0.554849], [-0.029781, -0.891132, -0.452767]]; the translation vector: [2.349929, 1.419923, 1.358478], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.552, 0.743, 0.817, 1.009, -0.013, 1.113], [0.943, 1.174, 0.929, -0.18, 0.779, 1.068]]\nB: [[0.748, 0.782, 0.621, 0.556, 0.127, 0.839], [1.697, 1.131, 0.375, -0.273, 1.039, 1.495]]\nC: [[0.612, 1.202, 0.708, 0.529, 0.114, 0.821], [1.612, 1.184, 0.198, 0.419, 0.977, 0.647]]\nD: [[0.368, 1.181, 0.615, 0.989, 0.028, 1.271], [1.284, 0.726, 0.504, 0.067, 0.905, 1.037]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.47, 0.453, 0.894, 0.2, 0.52, 0.291], [1.542, -0.676, 0.862, 0.217, 0.405, 0.289], [-1.666, -1.034, 0.158, 0.332, 0.363, 0.294]]\nB: [[1.471, 0.336, 1.375, -0.216, 0.786, 0.736], [1.469, -0.24, 1.152, 0.44, 0.255, 0.196], [-1.84, -1.112, 0.203, 0.586, 0.569, 0.159]]\nC: [[1.315, 0.861, 1.294, -0.145, 0.334, 0.615], [1.166, -0.686, 1.016, 0.2, 0.258, 0.346], [-2.029, -1.236, -0.071, 0.818, 0.37, 0.684]]\nD: [[1.59, 0.904, 1.331, 0.394, 0.302, 0.781], [1.626, -0.262, 1.266, -0.178, 0.337, 0.326], [-1.34, -1.047, 0.45, -0.149, 0.438, 0.179]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_3_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_3_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the speaker in the scene. The camera pose information includes: the rotation matrix: [[-0.283698, -0.38675, 0.877463], [-0.95878, 0.129662, -0.252839], [-0.015988, -0.913024, -0.407593]]; the translation vector: [3.69525, 3.551647, 1.352095], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.47, 0.453, 0.894, 0.2, 0.52, 0.291], [1.542, -0.676, 0.862, 0.217, 0.405, 0.289], [-1.666, -1.034, 0.158, 0.332, 0.363, 0.294]]\nB: [[1.471, 0.336, 1.375, -0.216, 0.786, 0.736], [1.469, -0.24, 1.152, 0.44, 0.255, 0.196], [-1.84, -1.112, 0.203, 0.586, 0.569, 0.159]]\nC: [[1.315, 0.861, 1.294, -0.145, 0.334, 0.615], [1.166, -0.686, 1.016, 0.2, 0.258, 0.346], [-2.029, -1.236, -0.071, 0.818, 0.37, 0.684]]\nD: [[1.59, 0.904, 1.331, 0.394, 0.302, 0.781], [1.626, -0.262, 1.266, -0.178, 0.337, 0.326], [-1.34, -1.047, 0.45, -0.149, 0.438, 0.179]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.415, 0.474, 0.662, 0.608, 0.318, 0.607], [0.137, 1.366, 0.214, 0.809, 0.687, 0.563], [-0.119, -1.513, 0.412, 0.991, 0.469, 0.436], [0.958, -1.756, 0.374, 0.393, 0.461, 0.273]]\nB: [[0.097, 0.337, 0.367, 0.736, 0.669, 0.76], [-0.118, 0.915, 0.406, 0.54, 0.71, 0.78], [0.039, -1.273, 0.366, 0.52, 0.703, 0.787], [0.484, -2.107, 0.393, 0.516, 0.773, 0.731]]\nC: [[-0.145, 0.33, 0.215, 0.329, 0.397, 1.235], [-0.041, 1.377, -0.008, 0.173, 0.698, 1.043], [0.354, -0.822, 0.479, 0.306, 0.474, 0.987], [0.885, -2.559, 0.576, 0.548, 1.045, 0.546]]\nD: [[-0.062, 0.608, 0.646, 1.11, 1.056, 0.374], [0.266, 1.23, 0.893, 0.95, 0.801, 1.268], [-0.368, -1.641, 0.003, 0.257, 0.709, 0.427], [0.563, -2.189, 0.486, 0.531, 1.025, 0.714]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_4_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_4_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.857694, 0.203115, -0.472341], [0.513544, 0.293426, -0.806333], [-0.025181, -0.934155, -0.355978]]; the translation vector: [3.161674, 3.662206, 1.335287], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.415, 0.474, 0.662, 0.608, 0.318, 0.607], [0.137, 1.366, 0.214, 0.809, 0.687, 0.563], [-0.119, -1.513, 0.412, 0.991, 0.469, 0.436], [0.958, -1.756, 0.374, 0.393, 0.461, 0.273]]\nB: [[0.097, 0.337, 0.367, 0.736, 0.669, 0.76], [-0.118, 0.915, 0.406, 0.54, 0.71, 0.78], [0.039, -1.273, 0.366, 0.52, 0.703, 0.787], [0.484, -2.107, 0.393, 0.516, 0.773, 0.731]]\nC: [[-0.145, 0.33, 0.215, 0.329, 0.397, 1.235], [-0.041, 1.377, -0.008, 0.173, 0.698, 1.043], [0.354, -0.822, 0.479, 0.306, 0.474, 0.987], [0.885, -2.559, 0.576, 0.548, 1.045, 0.546]]\nD: [[-0.062, 0.608, 0.646, 1.11, 1.056, 0.374], [0.266, 1.23, 0.893, 0.95, 0.801, 1.268], [-0.368, -1.641, 0.003, 0.257, 0.709, 0.427], [0.563, -2.189, 0.486, 0.531, 1.025, 0.714]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.3, -0.383, 0.923, -0.356, 3.02, 2.635], [-2.115, 1.383, 0.841, 0.469, 0.927, 1.856], [-0.097, 1.484, 1.187, 3.044, -0.119, 1.807], [-1.377, 2.117, 0.899, -0.033, 1.21, 1.808], [1.159, -1.198, 1.874, 0.555, 0.176, 1.325]]\nB: [[1.941, -0.755, 1.598, -0.121, 2.674, 2.518], [-2.423, 1.211, 1.402, 0.192, 0.868, 2.164], [0.169, 1.203, 1.223, 2.673, -0.21, 2.157], [-1.688, 1.565, 0.939, 0.535, 1.926, 1.811], [1.734, -1.649, 1.385, 0.714, 0.109, 1.38]]\nC: [[1.696, -0.287, 1.129, 0.119, 2.8, 2.268], [-2.577, 1.535, 1.204, 0.613, 1.32, 2.198], [0.155, 1.133, 1.131, 3.032, 0.104, 2.204], [-1.285, 1.824, 1.154, 0.225, 1.437, 2.219], [1.488, -1.695, 1.419, 0.405, 0.098, 1.172]]\nD: [[1.453, -0.695, 1.348, 0.547, 3.272, 2.434], [-2.777, 1.417, 1.534, 1.007, 1.19, 1.834], [-0.034, 0.735, 1.208, 2.75, 0.551, 2.499], [-1.311, 1.97, 1.046, -0.151, 1.928, 1.721], [1.211, -2.136, 1.037, 0.829, -0.033, 1.518]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_5_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_5_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.199941, 0.263531, -0.943703], [0.979453, -0.027844, 0.19974], [0.026362, -0.964249, -0.263683]]; the translation vector: [3.611549, 3.757055, 1.562045], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.3, -0.383, 0.923, -0.356, 3.02, 2.635], [-2.115, 1.383, 0.841, 0.469, 0.927, 1.856], [-0.097, 1.484, 1.187, 3.044, -0.119, 1.807], [-1.377, 2.117, 0.899, -0.033, 1.21, 1.808], [1.159, -1.198, 1.874, 0.555, 0.176, 1.325]]\nB: [[1.941, -0.755, 1.598, -0.121, 2.674, 2.518], [-2.423, 1.211, 1.402, 0.192, 0.868, 2.164], [0.169, 1.203, 1.223, 2.673, -0.21, 2.157], [-1.688, 1.565, 0.939, 0.535, 1.926, 1.811], [1.734, -1.649, 1.385, 0.714, 0.109, 1.38]]\nC: [[1.696, -0.287, 1.129, 0.119, 2.8, 2.268], [-2.577, 1.535, 1.204, 0.613, 1.32, 2.198], [0.155, 1.133, 1.131, 3.032, 0.104, 2.204], [-1.285, 1.824, 1.154, 0.225, 1.437, 2.219], [1.488, -1.695, 1.419, 0.405, 0.098, 1.172]]\nD: [[1.453, -0.695, 1.348, 0.547, 3.272, 2.434], [-2.777, 1.417, 1.534, 1.007, 1.19, 1.834], [-0.034, 0.735, 1.208, 2.75, 0.551, 2.499], [-1.311, 1.97, 1.046, -0.151, 1.928, 1.721], [1.211, -2.136, 1.037, 0.829, -0.033, 1.518]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.228, -1.379, 0.968, 0.05, 0.868, 0.876]]\nB: [[1.972, -1.363, 0.684, 0.094, 1.002, 1.369]]\nC: [[2.421, -1.068, 1.034, 0.177, 1.167, 1.478]]\nD: [[2.274, -1.598, 1.168, 0.52, 1.421, 1.228]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_6_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_6_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[-0.156961, 0.257294, -0.953501], [0.986843, 0.002956, -0.161652], [-0.038773, -0.966329, -0.254373]]; the translation vector: [1.838324, 1.205476, 1.480452], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.228, -1.379, 0.968, 0.05, 0.868, 0.876]]\nB: [[1.972, -1.363, 0.684, 0.094, 1.002, 1.369]]\nC: [[2.421, -1.068, 1.034, 0.177, 1.167, 1.478]]\nD: [[2.274, -1.598, 1.168, 0.52, 1.421, 1.228]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.821, -1.627, 0.918, 0.317, 0.52, -0.34]]\nB: [[-1.196, -1.892, 0.539, 0.329, 0.374, 0.146]]\nC: [[-1.472, -2.353, 0.745, 0.703, 0.302, -0.352]]\nD: [[-1.33, -1.906, 0.911, -0.061, 0.797, -0.104]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_7_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_7_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the jacket in the scene. The camera pose information includes: the rotation matrix: [[0.999847, -0.004634, 0.01689], [-0.017397, -0.374134, 0.927211], [0.002023, -0.927363, -0.374157]]; the translation vector: [3.310194, 3.16458, 1.506432], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.821, -1.627, 0.918, 0.317, 0.52, -0.34]]\nB: [[-1.196, -1.892, 0.539, 0.329, 0.374, 0.146]]\nC: [[-1.472, -2.353, 0.745, 0.703, 0.302, -0.352]]\nD: [[-1.33, -1.906, 0.911, -0.061, 0.797, -0.104]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.441, 1.064, 0.78, 0.6, 1.194, 1.759]]\nB: [[0.806, 1.175, 0.723, 1.145, 0.857, 1.307]]\nC: [[1.47, 1.204, 0.738, 1.161, 1.149, 1.298]]\nD: [[1.013, 1.023, 0.774, 0.913, 1.329, 1.578]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_8_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_8_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the desk in the scene. The camera pose information includes: the rotation matrix: [[0.977181, 0.077241, -0.197866], [0.211774, -0.426158, 0.879512], [-0.016388, -0.901345, -0.432791]]; the translation vector: [0.977323, 0.877303, 1.40232], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.441, 1.064, 0.78, 0.6, 1.194, 1.759]]\nB: [[0.806, 1.175, 0.723, 1.145, 0.857, 1.307]]\nC: [[1.47, 1.204, 0.738, 1.161, 1.149, 1.298]]\nD: [[1.013, 1.023, 0.774, 0.913, 1.329, 1.578]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.32, 0.548, 0.015, 3.907, 5.219, 0.432]]\nB: [[-0.869, 0.661, -0.307, 3.672, 4.614, -0.069]]\nC: [[-0.885, 0.436, 0.066, 3.44, 4.871, 0.305]]\nD: [[-1.228, 0.813, -0.025, 3.355, 4.94, 0.54]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_9_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_9_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[-0.30056, -0.511506, 0.805], [-0.953151, 0.130866, -0.272721], [0.034151, -0.849256, -0.526876]]; the translation vector: [-0.281614, 2.924112, 1.306122], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.32, 0.548, 0.015, 3.907, 5.219, 0.432]]\nB: [[-0.869, 0.661, -0.307, 3.672, 4.614, -0.069]]\nC: [[-0.885, 0.436, 0.066, 3.44, 4.871, 0.305]]\nD: [[-1.228, 0.813, -0.025, 3.355, 4.94, 0.54]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.236, 0.438, 2.571, 1.376, 1.585, 0.296]]\nB: [[0.105, 0.071, 2.209, 1.375, 1.269, -0.259]]\nC: [[-0.082, 0.088, 2.553, 1.257, 1.665, 0.102]]\nD: [[-0.566, -0.056, 2.979, 1.134, 1.904, -0.21]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_10_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_10_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the ceiling in the scene. The camera pose information includes: the rotation matrix: [[-0.255196, -0.436856, 0.862573], [-0.966393, 0.143834, -0.213066], [-0.030988, -0.887958, -0.45888]]; the translation vector: [1.734999, 0.744851, 1.432124], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.236, 0.438, 2.571, 1.376, 1.585, 0.296]]\nB: [[0.105, 0.071, 2.209, 1.375, 1.269, -0.259]]\nC: [[-0.082, 0.088, 2.553, 1.257, 1.665, 0.102]]\nD: [[-0.566, -0.056, 2.979, 1.134, 1.904, -0.21]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.043, 0.444, 0.066, 3.645, 4.94, 0.241]]\nB: [[0.071, 0.025, -0.197, 3.897, 4.771, 0.696]]\nC: [[-0.523, 0.143, -0.403, 3.288, 4.973, 0.134]]\nD: [[0.378, 0.809, 0.444, 3.764, 4.725, 0.036]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_11_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_11_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[-0.436119, -0.427186, 0.79203], [-0.89981, 0.218659, -0.377532], [-0.011909, -0.877326, -0.479747]]; the translation vector: [1.992302, 3.72193, 1.553249], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.043, 0.444, 0.066, 3.645, 4.94, 0.241]]\nB: [[0.071, 0.025, -0.197, 3.897, 4.771, 0.696]]\nC: [[-0.523, 0.143, -0.403, 3.288, 4.973, 0.134]]\nD: [[0.378, 0.809, 0.444, 3.764, 4.725, 0.036]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.786, -1.469, 1.112, 0.86, 0.176, 1.647]]\nB: [[-0.914, -1.825, 1.495, 0.394, 0.281, 2.141]]\nC: [[-1.155, -1.563, 0.935, 0.81, 0.246, 1.235]]\nD: [[-0.398, -1.079, 1.275, 0.659, -0.007, 1.932]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_12_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_12_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the curtain in the scene. The camera pose information includes: the rotation matrix: [[-0.112591, -0.547395, 0.829266], [-0.992672, 0.098819, -0.069547], [-0.043877, -0.83102, -0.55451]]; the translation vector: [1.18498, 1.814175, 1.496605], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.786, -1.469, 1.112, 0.86, 0.176, 1.647]]\nB: [[-0.914, -1.825, 1.495, 0.394, 0.281, 2.141]]\nC: [[-1.155, -1.563, 0.935, 0.81, 0.246, 1.235]]\nD: [[-0.398, -1.079, 1.275, 0.659, -0.007, 1.932]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.187, -2.136, 1.49, 0.407, 0.4, 0.612], [0.6, -1.205, 1.939, 0.176, 0.133, -0.205]]\nB: [[0.434, -1.704, 1.717, 0.327, 0.549, 0.278], [0.752, -1.616, 1.803, 0.403, 0.362, 0.211]]\nC: [[0.158, -1.92, 1.36, -0.055, 0.096, 0.484], [0.44, -1.879, 1.563, 0.594, 0.374, 0.673]]\nD: [[-0.017, -1.973, 1.957, -0.127, 0.324, 0.483], [0.88, -1.365, 2.154, 0.664, 0.083, -0.049]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_13_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_13_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the box in the scene. The camera pose information includes: the rotation matrix: [[0.645842, -0.099101, 0.757012], [-0.761541, -0.013148, 0.647984], [-0.054263, -0.994991, -0.083961]]; the translation vector: [3.729951, 1.432448, 1.733539], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.187, -2.136, 1.49, 0.407, 0.4, 0.612], [0.6, -1.205, 1.939, 0.176, 0.133, -0.205]]\nB: [[0.434, -1.704, 1.717, 0.327, 0.549, 0.278], [0.752, -1.616, 1.803, 0.403, 0.362, 0.211]]\nC: [[0.158, -1.92, 1.36, -0.055, 0.096, 0.484], [0.44, -1.879, 1.563, 0.594, 0.374, 0.673]]\nD: [[-0.017, -1.973, 1.957, -0.127, 0.324, 0.483], [0.88, -1.365, 2.154, 0.664, 0.083, -0.049]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.798, -1.611, 1.132, 0.285, 0.531, 1.165]]\nB: [[1.687, -1.332, 1.2, 0.199, 0.988, 0.799]]\nC: [[1.357, -0.901, 1.518, -0.194, 0.606, 1.017]]\nD: [[1.876, -1.168, 0.737, 0.277, 1.058, 1.074]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_14_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_14_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the window in the scene. The camera pose information includes: the rotation matrix: [[0.14018, 0.443083, -0.885453], [0.989985, -0.07783, 0.117782], [-0.016727, -0.893096, -0.449556]]; the translation vector: [3.549726, 0.935059, 1.485921], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.798, -1.611, 1.132, 0.285, 0.531, 1.165]]\nB: [[1.687, -1.332, 1.2, 0.199, 0.988, 0.799]]\nC: [[1.357, -0.901, 1.518, -0.194, 0.606, 1.017]]\nD: [[1.876, -1.168, 0.737, 0.277, 1.058, 1.074]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.819, -0.006, 0.434, 0.452, 1.821, 0.691], [-2.563, 0.098, 0.464, 0.939, 2.679, 0.721]]\nB: [[-1.198, -0.018, -0.225, 0.953, 2.14, 0.57], [-3.038, 0.583, 0.16, 0.212, 2.66, 1.39]]\nC: [[-1.115, -0.366, 0.124, 1.037, 1.922, 0.126], [-3.074, 0.098, -0.014, 0.214, 2.71, 0.451]]\nD: [[-0.889, -0.312, 0.236, 0.943, 2.266, 0.443], [-3.042, 0.305, 0.458, 0.511, 3.034, 0.927]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_15_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_15_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the table in the scene. The camera pose information includes: the rotation matrix: [[0.988959, -0.006087, -0.148062], [0.148117, 0.009943, 0.98892], [-0.004548, -0.999932, 0.010735]]; the translation vector: [3.911582, 2.672538, 1.565046], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.819, -0.006, 0.434, 0.452, 1.821, 0.691], [-2.563, 0.098, 0.464, 0.939, 2.679, 0.721]]\nB: [[-1.198, -0.018, -0.225, 0.953, 2.14, 0.57], [-3.038, 0.583, 0.16, 0.212, 2.66, 1.39]]\nC: [[-1.115, -0.366, 0.124, 1.037, 1.922, 0.126], [-3.074, 0.098, -0.014, 0.214, 2.71, 0.451]]\nD: [[-0.889, -0.312, 0.236, 0.943, 2.266, 0.443], [-3.042, 0.305, 0.458, 0.511, 3.034, 0.927]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.003, 1.71, 2.094, 0.541, 0.263, 0.118], [1.203, 1.68, 0.477, 1.133, 0.414, 1.172]]\nB: [[1.233, 1.735, 1.724, 0.591, 0.58, 0.487], [0.701, 1.63, 0.882, 0.862, 0.077, 0.96]]\nC: [[0.897, 1.469, 2.006, 0.732, 0.251, 0.23], [0.896, 1.321, 0.509, 1.078, 0.563, 0.956]]\nD: [[1.067, 1.911, 1.889, 0.58, 0.392, -0.26], [1.382, 1.503, 0.381, 1.083, 0.31, 0.478]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_16_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_16_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shelf in the scene. The camera pose information includes: the rotation matrix: [[-0.767458, -0.265442, 0.583565], [-0.640543, 0.35536, -0.680752], [-0.026676, -0.896248, -0.442751]]; the translation vector: [3.343537, 3.697402, 1.375352], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.003, 1.71, 2.094, 0.541, 0.263, 0.118], [1.203, 1.68, 0.477, 1.133, 0.414, 1.172]]\nB: [[1.233, 1.735, 1.724, 0.591, 0.58, 0.487], [0.701, 1.63, 0.882, 0.862, 0.077, 0.96]]\nC: [[0.897, 1.469, 2.006, 0.732, 0.251, 0.23], [0.896, 1.321, 0.509, 1.078, 0.563, 0.956]]\nD: [[1.067, 1.911, 1.889, 0.58, 0.392, -0.26], [1.382, 1.503, 0.381, 1.083, 0.31, 0.478]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.138, 0.124, 0.676, -0.24, 4.388, 1.6], [-0.991, 0.479, 1.092, 0.412, 4.521, 1.772], [0.357, 1.729, 0.48, 2.481, 0.664, 1.17]]\nB: [[1.22, 0.284, 0.605, 0.161, 4.409, 2.133], [-1.442, 0.481, 1.313, -0.024, 4.063, 1.777], [-0.203, 2.498, 0.224, 1.968, 0.299, 1.17]]\nC: [[0.938, 0.113, 0.929, -0.179, 3.724, 1.454], [-0.685, 0.25, 0.853, -0.017, 3.941, 2.536], [-0.076, 2.502, 0.394, 2.11, -0.271, 0.712]]\nD: [[1.264, 0.297, 0.846, 0.212, 3.931, 1.71], [-1.088, 0.197, 1.004, 0.293, 4.063, 2.062], [0.059, 2.138, 0.489, 2.4, 0.176, 0.937]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_17_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_17_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.311411, -0.45253, 0.835607], [-0.948656, 0.199362, -0.245576], [-0.055457, -0.869179, -0.491379]]; the translation vector: [2.299133, 2.388773, 1.459468], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.138, 0.124, 0.676, -0.24, 4.388, 1.6], [-0.991, 0.479, 1.092, 0.412, 4.521, 1.772], [0.357, 1.729, 0.48, 2.481, 0.664, 1.17]]\nB: [[1.22, 0.284, 0.605, 0.161, 4.409, 2.133], [-1.442, 0.481, 1.313, -0.024, 4.063, 1.777], [-0.203, 2.498, 0.224, 1.968, 0.299, 1.17]]\nC: [[0.938, 0.113, 0.929, -0.179, 3.724, 1.454], [-0.685, 0.25, 0.853, -0.017, 3.941, 2.536], [-0.076, 2.502, 0.394, 2.11, -0.271, 0.712]]\nD: [[1.264, 0.297, 0.846, 0.212, 3.931, 1.71], [-1.088, 0.197, 1.004, 0.293, 4.063, 2.062], [0.059, 2.138, 0.489, 2.4, 0.176, 0.937]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.286, 0.023, 0.43, 0.149, 0.955, 0.837], [-1.223, 1.506, 0.654, 0.172, 1.002, 1.099]]\nB: [[-1.716, 0.263, -0.049, 0.591, 1.354, 0.711], [-1.546, 1.706, 0.156, 0.078, 0.869, 0.807]]\nC: [[-0.94, -0.207, 0.36, 0.417, 1.226, 0.947], [-1.061, 1.683, 0.653, 0.491, 0.617, 1.297]]\nD: [[-1.505, -0.176, 0.438, -0.283, 0.675, 1.3], [-1.566, 1.679, 1.013, -0.274, 0.726, 0.998]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_18_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_18_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the doorframe in the scene. The camera pose information includes: the rotation matrix: [[-0.305635, -0.390507, 0.868385], [-0.952144, 0.122302, -0.280116], [0.003183, -0.91244, -0.409198]]; the translation vector: [4.266061, 1.773856, 1.285079], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.286, 0.023, 0.43, 0.149, 0.955, 0.837], [-1.223, 1.506, 0.654, 0.172, 1.002, 1.099]]\nB: [[-1.716, 0.263, -0.049, 0.591, 1.354, 0.711], [-1.546, 1.706, 0.156, 0.078, 0.869, 0.807]]\nC: [[-0.94, -0.207, 0.36, 0.417, 1.226, 0.947], [-1.061, 1.683, 0.653, 0.491, 0.617, 1.297]]\nD: [[-1.505, -0.176, 0.438, -0.283, 0.675, 1.3], [-1.566, 1.679, 1.013, -0.274, 0.726, 0.998]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.301, -0.248, -0.183, 3.399, 5.209, 0.282]]\nB: [[0.089, -0.015, -0.009, 3.337, 5.518, 0.258]]\nC: [[0.396, -0.159, 0.362, 3.257, 5.951, -0.2]]\nD: [[0.093, 0.115, -0.474, 3.284, 5.168, 0.122]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_19_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_19_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[-0.934582, -0.143102, 0.325696], [-0.355737, 0.383069, -0.852473], [-0.002774, -0.912568, -0.408916]]; the translation vector: [2.694367, 2.483235, 1.465763], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.301, -0.248, -0.183, 3.399, 5.209, 0.282]]\nB: [[0.089, -0.015, -0.009, 3.337, 5.518, 0.258]]\nC: [[0.396, -0.159, 0.362, 3.257, 5.951, -0.2]]\nD: [[0.093, 0.115, -0.474, 3.284, 5.168, 0.122]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.264, 0.68, 0.463, 0.725, 0.55, 0.967], [1.225, 0.142, 2.156, 0.68, 2.62, 0.669]]\nB: [[0.179, 0.182, 0.661, 0.23, 1.022, 0.617], [1.51, -0.031, 1.909, 0.955, 2.219, 0.647]]\nC: [[0.069, 0.653, 0.766, 0.622, 0.263, 0.911], [0.908, 0.073, 1.898, 0.615, 2.442, 0.579]]\nD: [[-0.094, 1.133, 0.833, 0.562, 0.551, 0.493], [1.073, 0.074, 2.645, 0.407, 2.814, 0.994]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_20_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_20_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the cabinet in the scene. The camera pose information includes: the rotation matrix: [[-0.928375, -0.17783, 0.326339], [-0.371449, 0.415395, -0.830345], [0.012101, -0.892089, -0.451697]]; the translation vector: [2.096006, 1.919092, 1.36174], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.264, 0.68, 0.463, 0.725, 0.55, 0.967], [1.225, 0.142, 2.156, 0.68, 2.62, 0.669]]\nB: [[0.179, 0.182, 0.661, 0.23, 1.022, 0.617], [1.51, -0.031, 1.909, 0.955, 2.219, 0.647]]\nC: [[0.069, 0.653, 0.766, 0.622, 0.263, 0.911], [0.908, 0.073, 1.898, 0.615, 2.442, 0.579]]\nD: [[-0.094, 1.133, 0.833, 0.562, 0.551, 0.493], [1.073, 0.074, 2.645, 0.407, 2.814, 0.994]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.718, -0.44, 1.96, 0.228, 0.897, 0.293], [-1.706, -1.293, 1.868, 0.22, 0.846, 0.362], [-1.707, -1.314, 0.762, 0.375, 0.826, 0.302], [-1.691, 1.543, 1.626, 0.337, 0.697, 0.437], [-1.573, 1.406, 1.291, 0.181, 0.564, 0.313]]\nB: [[-1.988, -0.706, 1.585, 0.615, 0.861, 0.547], [-1.403, -1.309, 1.785, -0.129, 1.049, -0.092], [-1.749, -1.25, 0.92, 0.869, 1.088, 0.428], [-1.92, 1.941, 1.694, 0.669, 1.107, 0.403], [-1.483, 1.056, 1.615, 0.417, 0.387, 0.739]]\nC: [[-1.546, -0.182, 1.499, 0.527, 1.029, 0.605], [-1.401, -1.244, 2.308, -0.222, 0.467, 0.206], [-2.193, -1.361, 0.929, 0.133, 0.525, 0.091], [-1.321, 1.865, 1.781, -0.053, 0.666, 0.358], [-1.503, 1.488, 1.689, 0.165, 0.203, 0.17]]\nD: [[-1.94, -0.345, 2.215, 0.028, 0.642, 0.092], [-2.035, -1.654, 1.937, 0.612, 1.134, -0.102], [-1.249, -1.385, 0.367, 0.613, 1.003, 0.682], [-2.01, 1.507, 1.513, 0.614, 0.573, 0.003], [-1.183, 1.016, 0.985, -0.012, 0.86, 0.417]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_21_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_21_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the books in the scene. The camera pose information includes: the rotation matrix: [[0.725417, 0.297171, -0.620854], [0.687848, -0.279954, 0.669695], [0.025203, -0.912861, -0.407492]]; the translation vector: [3.434752, 3.057745, 1.556519], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.718, -0.44, 1.96, 0.228, 0.897, 0.293], [-1.706, -1.293, 1.868, 0.22, 0.846, 0.362], [-1.707, -1.314, 0.762, 0.375, 0.826, 0.302], [-1.691, 1.543, 1.626, 0.337, 0.697, 0.437], [-1.573, 1.406, 1.291, 0.181, 0.564, 0.313]]\nB: [[-1.988, -0.706, 1.585, 0.615, 0.861, 0.547], [-1.403, -1.309, 1.785, -0.129, 1.049, -0.092], [-1.749, -1.25, 0.92, 0.869, 1.088, 0.428], [-1.92, 1.941, 1.694, 0.669, 1.107, 0.403], [-1.483, 1.056, 1.615, 0.417, 0.387, 0.739]]\nC: [[-1.546, -0.182, 1.499, 0.527, 1.029, 0.605], [-1.401, -1.244, 2.308, -0.222, 0.467, 0.206], [-2.193, -1.361, 0.929, 0.133, 0.525, 0.091], [-1.321, 1.865, 1.781, -0.053, 0.666, 0.358], [-1.503, 1.488, 1.689, 0.165, 0.203, 0.17]]\nD: [[-1.94, -0.345, 2.215, 0.028, 0.642, 0.092], [-2.035, -1.654, 1.937, 0.612, 1.134, -0.102], [-1.249, -1.385, 0.367, 0.613, 1.003, 0.682], [-2.01, 1.507, 1.513, 0.614, 0.573, 0.003], [-1.183, 1.016, 0.985, -0.012, 0.86, 0.417]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.266, 0.688, 1.427, 0.645, 4.144, 1.891], [1.793, 0.377, 0.356, 0.744, 3.81, 1.61], [-0.941, -1.334, 0.579, 2.335, 0.039, 1.673], [0.362, -1.428, 0.335, 0.484, 0.146, 1.101], [0.403, -1.616, 0.92, 1.722, 0.842, 1.057], [1.194, 3.045, 0.588, 0.013, 0.929, 1.121], [1.761, 2.38, 0.509, 0.767, -0.159, 0.975]]\nB: [[-2.307, 0.099, 1.206, 0.234, 3.804, 1.593], [2.401, 0.098, 0.732, 0.485, 3.642, 1.662], [-1.38, -1.427, 1.26, 2.485, 0.117, 1.419], [0.047, -1.412, 0.542, 0.454, 0.56, 1.109], [0.451, -2.029, 0.885, 1.313, 0.841, 0.961], [1.821, 2.284, 0.965, -0.181, 0.842, 1.118], [1.675, 2.553, 0.956, 0.491, 0.104, 1.591]]\nC: [[-1.548, 0.69, 1.589, 0.293, 3.816, 1.624], [2.411, 0.459, 0.334, 0.543, 4.513, 1.336], [-0.832, -2.051, 0.999, 1.925, 0.593, 1.075], [-0.217, -1.618, 0.815, -0.295, 0.494, 0.985], [1.068, -1.834, 1.273, 1.646, 0.657, 0.959], [1.898, 2.458, 0.543, 0.44, 1.518, 1.452], [2.071, 1.877, 0.293, 1.12, 0.358, 1.716]]\nD: [[-1.964, 0.397, 1.135, 0.305, 4.04, 1.813], [2.143, 0.114, 0.673, 0.413, 4.08, 1.439], [-0.926, -1.676, 0.892, 2.284, 0.231, 1.529], [0.195, -1.875, 0.811, 0.153, 0.424, 1.364], [0.78, -1.998, 0.788, 1.226, 0.36, 1.309], [1.439, 2.685, 0.692, 0.249, 1.216, 1.435], [1.802, 2.098, 0.616, 0.629, 0.105, 1.315]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_22_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_22_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.205292, 0.226186, -0.952205], [0.97316, -0.150555, 0.174048], [-0.103992, -0.962379, -0.251024]]; the translation vector: [4.876985, 2.837537, 1.671042], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.266, 0.688, 1.427, 0.645, 4.144, 1.891], [1.793, 0.377, 0.356, 0.744, 3.81, 1.61], [-0.941, -1.334, 0.579, 2.335, 0.039, 1.673], [0.362, -1.428, 0.335, 0.484, 0.146, 1.101], [0.403, -1.616, 0.92, 1.722, 0.842, 1.057], [1.194, 3.045, 0.588, 0.013, 0.929, 1.121], [1.761, 2.38, 0.509, 0.767, -0.159, 0.975]]\nB: [[-2.307, 0.099, 1.206, 0.234, 3.804, 1.593], [2.401, 0.098, 0.732, 0.485, 3.642, 1.662], [-1.38, -1.427, 1.26, 2.485, 0.117, 1.419], [0.047, -1.412, 0.542, 0.454, 0.56, 1.109], [0.451, -2.029, 0.885, 1.313, 0.841, 0.961], [1.821, 2.284, 0.965, -0.181, 0.842, 1.118], [1.675, 2.553, 0.956, 0.491, 0.104, 1.591]]\nC: [[-1.548, 0.69, 1.589, 0.293, 3.816, 1.624], [2.411, 0.459, 0.334, 0.543, 4.513, 1.336], [-0.832, -2.051, 0.999, 1.925, 0.593, 1.075], [-0.217, -1.618, 0.815, -0.295, 0.494, 0.985], [1.068, -1.834, 1.273, 1.646, 0.657, 0.959], [1.898, 2.458, 0.543, 0.44, 1.518, 1.452], [2.071, 1.877, 0.293, 1.12, 0.358, 1.716]]\nD: [[-1.964, 0.397, 1.135, 0.305, 4.04, 1.813], [2.143, 0.114, 0.673, 0.413, 4.08, 1.439], [-0.926, -1.676, 0.892, 2.284, 0.231, 1.529], [0.195, -1.875, 0.811, 0.153, 0.424, 1.364], [0.78, -1.998, 0.788, 1.226, 0.36, 1.309], [1.439, 2.685, 0.692, 0.249, 1.216, 1.435], [1.802, 2.098, 0.616, 0.629, 0.105, 1.315]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.121, -0.501, -0.221, 0.439, 0.387, 0.089], [1.109, -1.077, -0.06, 0.221, 0.081, 0.556]]\nB: [[-0.019, -0.497, 0.389, 1.016, 0.944, 0.39], [1.279, -1.542, -0.239, 0.195, 0.216, 0.872]]\nC: [[0.21, -0.049, -0.032, 0.947, 0.552, 0.204], [0.617, -0.987, 0.392, 0.284, 0.553, 0.828]]\nD: [[0.392, -0.219, 0.176, 0.595, 0.63, 0.481], [0.882, -1.099, 0.197, 0.524, 0.524, 0.466]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_23_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_23_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the ottoman in the scene. The camera pose information includes: the rotation matrix: [[0.133825, -0.39571, 0.908573], [-0.990975, -0.046263, 0.125813], [-0.007752, -0.91721, -0.398329]]; the translation vector: [4.990516, 4.227292, 1.32289], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.121, -0.501, -0.221, 0.439, 0.387, 0.089], [1.109, -1.077, -0.06, 0.221, 0.081, 0.556]]\nB: [[-0.019, -0.497, 0.389, 1.016, 0.944, 0.39], [1.279, -1.542, -0.239, 0.195, 0.216, 0.872]]\nC: [[0.21, -0.049, -0.032, 0.947, 0.552, 0.204], [0.617, -0.987, 0.392, 0.284, 0.553, 0.828]]\nD: [[0.392, -0.219, 0.176, 0.595, 0.63, 0.481], [0.882, -1.099, 0.197, 0.524, 0.524, 0.466]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.923, 3.072, 1.641, 0.406, 0.224, 0.224], [0.852, 2.684, 1.628, 0.411, 0.369, 0.342], [0.952, 2.353, 1.591, 0.332, 0.315, 0.303], [0.919, 1.934, 1.549, 0.278, 0.356, 0.3], [0.991, 1.596, 1.521, 0.302, 0.285, 0.248], [1.083, 1.197, 1.51, 0.2, 0.428, 0.292], [1.067, 0.874, 1.479, 0.258, 0.387, 0.349], [1.029, 0.682, 1.414, 0.27, 0.238, 0.229], [1.041, 0.446, 1.386, 0.31, 0.355, 0.267], [1.007, 0.119, 1.367, 0.313, 0.297, 0.251], [1.072, -0.152, 1.331, 0.368, 0.301, 0.196], [0.978, -0.542, 1.366, 0.293, 0.411, 0.344], [1.038, -0.846, 1.349, 0.398, 0.352, 0.371], [0.995, -1.285, 1.277, 0.273, 0.319, 0.287], [1.051, -1.623, 1.317, 0.372, 0.433, 0.346], [1.016, -1.909, 1.267, 0.375, 0.379, 0.355], [1.01, -2.206, 1.239, 0.32, 0.305, 0.33], [1.021, -2.389, 1.248, 0.292, 0.375, 0.256], [0.945, -2.669, 1.168, 0.312, 0.307, 0.249], [0.986, -2.904, 1.157, 0.265, 0.331, 0.203]]\nB: [[1.16, 2.801, 1.566, 0.581, 0.486, -0.268], [0.756, 2.458, 1.347, -0.04, 0.522, 0.189], [0.535, 2.032, 1.866, 0.051, 0.318, 0.012], [0.896, 2.321, 1.823, -0.143, 0.711, 0.696], [1.3, 1.485, 1.216, 0.089, 0.474, 0.726], [1.333, 1.63, 1.281, 0.587, 0.639, -0.131], [1.034, 0.752, 1.496, 0.694, 0.45, 0.002], [1.132, 0.488, 1.903, -0.121, -0.068, 0.586], [1.244, 0.056, 1.06, 0.343, 0.366, 0.492], [0.523, 0.369, 1.091, 0.036, 0.297, 0.341], [0.945, -0.379, 1.231, -0.009, 0.698, 0.282], [0.742, -0.538, 1.804, 0.143, 0.887, 0.377], [1.245, -0.568, 1.71, 0.143, 0.603, 0.41], [1.356, -0.879, 1.397, 0.576, 0.048, 0.554], [1.47, -2.036, 1.112, 0.54, 0.795, 0.096], [1.472, -1.52, 0.829, 0.648, 0.598, 0.49], [0.775, -2.633, 1.506, -0.16, -0.139, -0.099], [0.838, -2.702, 1.211, 0.137, 0.331, -0.011], [1.261, -2.818, 1.474, 0.679, -0.005, 0.352], [0.793, -2.949, 1.566, -0.008, 0.477, 0.693]]\nC: [[1.056, 2.871, 1.196, 0.82, -0.168, 0.476], [1.086, 3.168, 1.177, -0.05, 0.768, 0.624], [1.078, 2.314, 1.991, 0.481, -0.014, 0.382], [0.899, 1.855, 1.409, -0.073, 0.065, 0.078], [0.796, 1.846, 1.026, -0.008, 0.461, 0.294], [0.96, 0.751, 1.316, 0.52, 0.805, 0.752], [1.18, 1.031, 1.766, 0.673, 0.119, 0.034], [1.398, 0.505, 1.118, -0.168, 0.16, -0.249], [0.838, 0.65, 1.392, 0.173, 0.458, 0.332], [1.111, -0.328, 1.396, 0.558, 0.481, 0.366], [0.597, -0.355, 1.146, 0.623, 0.368, 0.632], [0.691, -0.514, 1.338, -0.157, 0.304, -0.124], [0.696, -1.125, 1.476, 0.501, 0.757, 0.356], [0.907, -0.859, 1.385, 0.656, 0.571, -0.029], [1.035, -1.127, 1.219, 0.093, 0.841, 0.704], [0.635, -1.763, 1.501, -0.076, -0.097, 0.162], [0.614, -1.848, 1.062, 0.328, 0.483, 0.674], [0.692, -2.453, 1.556, 0.665, 0.718, 0.625], [1.074, -2.937, 1.026, 0.776, 0.224, 0.639], [0.852, -3.222, 1.01, 0.571, -0.139, 0.12]]\nD: [[1.022, 3.318, 1.189, 0.205, -0.146, 0.042], [0.815, 2.622, 1.239, 0.213, 0.653, 0.265], [1.051, 2.623, 1.858, 0.743, -0.174, 0.425], [1.36, 1.47, 1.216, -0.071, -0.098, -0.074], [1.312, 2.017, 2.002, -0.015, 0.439, 0.124], [0.798, 1.663, 1.184, 0.218, 0.773, 0.512], [1.438, 0.663, 1.321, 0.334, 0.497, 0.799], [1.496, 1.067, 1.009, 0.492, 0.69, -0.197], [0.673, 0.916, 1.137, 0.692, -0.115, 0.537], [0.588, 0.319, 1.507, 0.723, 0.486, 0.106], [0.938, -0.596, 1.384, 0.378, 0.487, -0.284], [0.718, -0.867, 0.941, 0.405, 0.388, -0.074], [1.365, -0.417, 1.613, 0.897, 0.508, -0.003], [1.124, -1.228, 1.16, 0.374, 0.651, 0.692], [0.872, -1.666, 1.25, 0.857, 0.612, -0.1], [0.693, -1.777, 1.038, 0.754, 0.733, 0.072], [1.133, -1.714, 1.626, 0.475, -0.192, 0.478], [1.392, -2.804, 1.671, -0.124, 0.18, 0.524], [1.024, -2.671, 1.235, 0.602, 0.29, 0.162], [0.636, -2.621, 1.52, -0.11, 0.64, 0.18]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_24_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_24_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the book in the scene. The camera pose information includes: the rotation matrix: [[0.999403, 0.004498, 0.03425], [-0.034232, -0.004158, 0.999405], [0.004638, -0.999981, -0.004001]]; the translation vector: [2.393484, 5.775056, 1.371464], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.923, 3.072, 1.641, 0.406, 0.224, 0.224], [0.852, 2.684, 1.628, 0.411, 0.369, 0.342], [0.952, 2.353, 1.591, 0.332, 0.315, 0.303], [0.919, 1.934, 1.549, 0.278, 0.356, 0.3], [0.991, 1.596, 1.521, 0.302, 0.285, 0.248], [1.083, 1.197, 1.51, 0.2, 0.428, 0.292], [1.067, 0.874, 1.479, 0.258, 0.387, 0.349], [1.029, 0.682, 1.414, 0.27, 0.238, 0.229], [1.041, 0.446, 1.386, 0.31, 0.355, 0.267], [1.007, 0.119, 1.367, 0.313, 0.297, 0.251], [1.072, -0.152, 1.331, 0.368, 0.301, 0.196], [0.978, -0.542, 1.366, 0.293, 0.411, 0.344], [1.038, -0.846, 1.349, 0.398, 0.352, 0.371], [0.995, -1.285, 1.277, 0.273, 0.319, 0.287], [1.051, -1.623, 1.317, 0.372, 0.433, 0.346], [1.016, -1.909, 1.267, 0.375, 0.379, 0.355], [1.01, -2.206, 1.239, 0.32, 0.305, 0.33], [1.021, -2.389, 1.248, 0.292, 0.375, 0.256], [0.945, -2.669, 1.168, 0.312, 0.307, 0.249], [0.986, -2.904, 1.157, 0.265, 0.331, 0.203]]\nB: [[1.16, 2.801, 1.566, 0.581, 0.486, -0.268], [0.756, 2.458, 1.347, -0.04, 0.522, 0.189], [0.535, 2.032, 1.866, 0.051, 0.318, 0.012], [0.896, 2.321, 1.823, -0.143, 0.711, 0.696], [1.3, 1.485, 1.216, 0.089, 0.474, 0.726], [1.333, 1.63, 1.281, 0.587, 0.639, -0.131], [1.034, 0.752, 1.496, 0.694, 0.45, 0.002], [1.132, 0.488, 1.903, -0.121, -0.068, 0.586], [1.244, 0.056, 1.06, 0.343, 0.366, 0.492], [0.523, 0.369, 1.091, 0.036, 0.297, 0.341], [0.945, -0.379, 1.231, -0.009, 0.698, 0.282], [0.742, -0.538, 1.804, 0.143, 0.887, 0.377], [1.245, -0.568, 1.71, 0.143, 0.603, 0.41], [1.356, -0.879, 1.397, 0.576, 0.048, 0.554], [1.47, -2.036, 1.112, 0.54, 0.795, 0.096], [1.472, -1.52, 0.829, 0.648, 0.598, 0.49], [0.775, -2.633, 1.506, -0.16, -0.139, -0.099], [0.838, -2.702, 1.211, 0.137, 0.331, -0.011], [1.261, -2.818, 1.474, 0.679, -0.005, 0.352], [0.793, -2.949, 1.566, -0.008, 0.477, 0.693]]\nC: [[1.056, 2.871, 1.196, 0.82, -0.168, 0.476], [1.086, 3.168, 1.177, -0.05, 0.768, 0.624], [1.078, 2.314, 1.991, 0.481, -0.014, 0.382], [0.899, 1.855, 1.409, -0.073, 0.065, 0.078], [0.796, 1.846, 1.026, -0.008, 0.461, 0.294], [0.96, 0.751, 1.316, 0.52, 0.805, 0.752], [1.18, 1.031, 1.766, 0.673, 0.119, 0.034], [1.398, 0.505, 1.118, -0.168, 0.16, -0.249], [0.838, 0.65, 1.392, 0.173, 0.458, 0.332], [1.111, -0.328, 1.396, 0.558, 0.481, 0.366], [0.597, -0.355, 1.146, 0.623, 0.368, 0.632], [0.691, -0.514, 1.338, -0.157, 0.304, -0.124], [0.696, -1.125, 1.476, 0.501, 0.757, 0.356], [0.907, -0.859, 1.385, 0.656, 0.571, -0.029], [1.035, -1.127, 1.219, 0.093, 0.841, 0.704], [0.635, -1.763, 1.501, -0.076, -0.097, 0.162], [0.614, -1.848, 1.062, 0.328, 0.483, 0.674], [0.692, -2.453, 1.556, 0.665, 0.718, 0.625], [1.074, -2.937, 1.026, 0.776, 0.224, 0.639], [0.852, -3.222, 1.01, 0.571, -0.139, 0.12]]\nD: [[1.022, 3.318, 1.189, 0.205, -0.146, 0.042], [0.815, 2.622, 1.239, 0.213, 0.653, 0.265], [1.051, 2.623, 1.858, 0.743, -0.174, 0.425], [1.36, 1.47, 1.216, -0.071, -0.098, -0.074], [1.312, 2.017, 2.002, -0.015, 0.439, 0.124], [0.798, 1.663, 1.184, 0.218, 0.773, 0.512], [1.438, 0.663, 1.321, 0.334, 0.497, 0.799], [1.496, 1.067, 1.009, 0.492, 0.69, -0.197], [0.673, 0.916, 1.137, 0.692, -0.115, 0.537], [0.588, 0.319, 1.507, 0.723, 0.486, 0.106], [0.938, -0.596, 1.384, 0.378, 0.487, -0.284], [0.718, -0.867, 0.941, 0.405, 0.388, -0.074], [1.365, -0.417, 1.613, 0.897, 0.508, -0.003], [1.124, -1.228, 1.16, 0.374, 0.651, 0.692], [0.872, -1.666, 1.25, 0.857, 0.612, -0.1], [0.693, -1.777, 1.038, 0.754, 0.733, 0.072], [1.133, -1.714, 1.626, 0.475, -0.192, 0.478], [1.392, -2.804, 1.671, -0.124, 0.18, 0.524], [1.024, -2.671, 1.235, 0.602, 0.29, 0.162], [0.636, -2.621, 1.52, -0.11, 0.64, 0.18]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.127, 1.263, 0.842, 1.099, 0.165, 0.151], [0.899, 0.349, 0.833, 0.078, 0.633, 0.087]]\nB: [[-0.285, 1.099, 0.515, 1.523, 0.256, -0.319], [0.56, 0.838, 0.875, -0.327, 0.985, 0.228]]\nC: [[0.446, 1.442, 1.259, 1.427, 0.331, 0.006], [0.446, 0.556, 0.643, 0.276, 0.563, -0.341]]\nD: [[-0.356, 1.631, 0.612, 0.864, 0.511, -0.226], [0.523, 0.674, 0.57, 0.567, 0.594, 0.019]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_25_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_25_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the rail in the scene. The camera pose information includes: the rotation matrix: [[0.631332, 0.312126, -0.709927], [0.775472, -0.26347, 0.573784], [-0.007951, -0.912776, -0.408382]]; the translation vector: [1.600176, 0.624978, 1.327739], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.127, 1.263, 0.842, 1.099, 0.165, 0.151], [0.899, 0.349, 0.833, 0.078, 0.633, 0.087]]\nB: [[-0.285, 1.099, 0.515, 1.523, 0.256, -0.319], [0.56, 0.838, 0.875, -0.327, 0.985, 0.228]]\nC: [[0.446, 1.442, 1.259, 1.427, 0.331, 0.006], [0.446, 0.556, 0.643, 0.276, 0.563, -0.341]]\nD: [[-0.356, 1.631, 0.612, 0.864, 0.511, -0.226], [0.523, 0.674, 0.57, 0.567, 0.594, 0.019]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.787, 2.876, 1.457, 2.063, -0.079, 1.518], [-0.953, 1.998, 0.997, 0.069, 3.579, 2.369], [0.693, -0.876, 1.265, 0.33, 4.219, 2.072], [0.345, -2.632, 0.88, 1.928, -0.067, 1.565], [-0.455, -2.068, 1.068, 0.08, 1.617, 2.382], [-0.899, -1.196, 0.623, 0.651, 0.095, 2.066]]\nB: [[0.116, 3.687, 0.955, 2.711, 0.51, 1.785], [-0.801, 1.888, 0.628, 0.573, 3.732, 2.205], [1.093, -0.187, 0.521, -0.148, 5.045, 2.538], [0.42, -2.481, 1.79, 1.307, 0.36, 0.947], [-0.514, -1.855, 0.567, 0.468, 1.76, 1.511], [-1.181, -1.155, 0.753, 0.267, -0.268, 1.847]]\nC: [[0.302, 3.207, 1.219, 2.255, 0.306, 1.414], [-0.871, 1.59, 0.966, 0.239, 3.492, 2.066], [0.732, -0.454, 0.961, 0.242, 4.576, 2.069], [-0.078, -2.664, 1.355, 1.624, 0.192, 1.303], [-0.886, -1.849, 0.913, 0.175, 1.703, 1.972], [-1.091, -1.016, 0.816, 0.518, 0.228, 1.826]]\nD: [[0.349, 2.764, 1.178, 2.112, -0.17, 1.603], [-1.032, 1.356, 0.914, 0.415, 3.877, 2.097], [0.285, -0.798, 1.205, 0.303, 4.409, 2.223], [-0.233, -2.574, 1.577, 1.241, 0.359, 1.513], [-1.059, -2.05, 1.259, 0.503, 1.807, 1.753], [-1.108, -1.393, 0.584, 0.052, -0.001, 1.469]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_26_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_26_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.386761, -0.304254, 0.870543], [-0.920043, 0.191539, -0.34181], [-0.062746, -0.933136, -0.354007]]; the translation vector: [2.082368, 4.008438, 1.845888], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.787, 2.876, 1.457, 2.063, -0.079, 1.518], [-0.953, 1.998, 0.997, 0.069, 3.579, 2.369], [0.693, -0.876, 1.265, 0.33, 4.219, 2.072], [0.345, -2.632, 0.88, 1.928, -0.067, 1.565], [-0.455, -2.068, 1.068, 0.08, 1.617, 2.382], [-0.899, -1.196, 0.623, 0.651, 0.095, 2.066]]\nB: [[0.116, 3.687, 0.955, 2.711, 0.51, 1.785], [-0.801, 1.888, 0.628, 0.573, 3.732, 2.205], [1.093, -0.187, 0.521, -0.148, 5.045, 2.538], [0.42, -2.481, 1.79, 1.307, 0.36, 0.947], [-0.514, -1.855, 0.567, 0.468, 1.76, 1.511], [-1.181, -1.155, 0.753, 0.267, -0.268, 1.847]]\nC: [[0.302, 3.207, 1.219, 2.255, 0.306, 1.414], [-0.871, 1.59, 0.966, 0.239, 3.492, 2.066], [0.732, -0.454, 0.961, 0.242, 4.576, 2.069], [-0.078, -2.664, 1.355, 1.624, 0.192, 1.303], [-0.886, -1.849, 0.913, 0.175, 1.703, 1.972], [-1.091, -1.016, 0.816, 0.518, 0.228, 1.826]]\nD: [[0.349, 2.764, 1.178, 2.112, -0.17, 1.603], [-1.032, 1.356, 0.914, 0.415, 3.877, 2.097], [0.285, -0.798, 1.205, 0.303, 4.409, 2.223], [-0.233, -2.574, 1.577, 1.241, 0.359, 1.513], [-1.059, -2.05, 1.259, 0.503, 1.807, 1.753], [-1.108, -1.393, 0.584, 0.052, -0.001, 1.469]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.506, 0.209, 0.255, 0.924, 1.928, 0.478], [0.463, -1.087, 0.626, 1.179, 0.62, 0.996], [2.049, 0.799, -0.061, 0.618, 1.041, 1.191]]\nB: [[-2.054, 0.6, 0.688, 1.044, 1.508, 0.567], [0.964, -1.042, 0.495, 1.122, 0.573, 0.421], [2.453, 0.513, 0.739, 0.463, 1.578, 0.424]]\nC: [[-2.686, -0.003, 0.374, 0.562, 1.486, 0.489], [1.084, -0.733, 0.31, 1.073, 1.131, 0.967], [1.7, 0.788, 0.091, 0.433, 1.461, 1.21]]\nD: [[-2.225, 0.184, 0.565, 0.652, 1.867, 0.966], [0.89, -0.986, 0.428, 1.537, 0.782, 0.844], [2.106, 0.485, 0.423, 0.73, 1.476, 0.84]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_27_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_27_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the couch in the scene. The camera pose information includes: the rotation matrix: [[-0.565317, -0.50256, 0.654103], [-0.824719, 0.328974, -0.460017], [0.016003, -0.799506, -0.600445]]; the translation vector: [4.07549, 5.065369, 1.281872], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.506, 0.209, 0.255, 0.924, 1.928, 0.478], [0.463, -1.087, 0.626, 1.179, 0.62, 0.996], [2.049, 0.799, -0.061, 0.618, 1.041, 1.191]]\nB: [[-2.054, 0.6, 0.688, 1.044, 1.508, 0.567], [0.964, -1.042, 0.495, 1.122, 0.573, 0.421], [2.453, 0.513, 0.739, 0.463, 1.578, 0.424]]\nC: [[-2.686, -0.003, 0.374, 0.562, 1.486, 0.489], [1.084, -0.733, 0.31, 1.073, 1.131, 0.967], [1.7, 0.788, 0.091, 0.433, 1.461, 1.21]]\nD: [[-2.225, 0.184, 0.565, 0.652, 1.867, 0.966], [0.89, -0.986, 0.428, 1.537, 0.782, 0.844], [2.106, 0.485, 0.423, 0.73, 1.476, 0.84]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.787, -0.535, 0.927, 0.017, -0.194, -0.206]]\nB: [[-1.049, -0.444, 0.739, 0.127, 0.097, 0.179]]\nC: [[-1.148, -0.307, 0.649, -0.194, 0.004, 0.501]]\nD: [[-1.423, -0.784, 0.923, 0.285, 0.539, 0.33]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_28_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_28_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the water bottle in the scene. The camera pose information includes: the rotation matrix: [[0.684823, -0.326379, 0.651532], [-0.728707, -0.304485, 0.613413], [-0.001823, -0.894855, -0.446353]]; the translation vector: [2.86358, 2.414664, 1.549631], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.787, -0.535, 0.927, 0.017, -0.194, -0.206]]\nB: [[-1.049, -0.444, 0.739, 0.127, 0.097, 0.179]]\nC: [[-1.148, -0.307, 0.649, -0.194, 0.004, 0.501]]\nD: [[-1.423, -0.784, 0.923, 0.285, 0.539, 0.33]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.125, -0.371, 0.52, 0.921, 0.949, 1.032], [-0.05, 0.47, 0.51, 0.929, 1.055, 1.018]]\nB: [[-0.03, 0.021, 0.629, 1.294, 0.744, 0.853], [0.141, 0.523, 0.057, 0.461, 0.601, 1.102]]\nC: [[-0.027, -0.543, 0.255, 1.392, 0.459, 1.351], [-0.542, 0.241, 0.854, 1.099, 1.281, 1.01]]\nD: [[-0.353, -0.617, 0.621, 0.568, 1.229, 1.321], [-0.327, 0.58, 0.56, 0.835, 0.644, 0.683]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_29_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_29_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the table in the scene. The camera pose information includes: the rotation matrix: [[0.935902, 0.160482, -0.313582], [0.351212, -0.493772, 0.795512], [-0.027173, -0.854655, -0.518485]]; the translation vector: [4.465, -0.226232, 1.550028], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.125, -0.371, 0.52, 0.921, 0.949, 1.032], [-0.05, 0.47, 0.51, 0.929, 1.055, 1.018]]\nB: [[-0.03, 0.021, 0.629, 1.294, 0.744, 0.853], [0.141, 0.523, 0.057, 0.461, 0.601, 1.102]]\nC: [[-0.027, -0.543, 0.255, 1.392, 0.459, 1.351], [-0.542, 0.241, 0.854, 1.099, 1.281, 1.01]]\nD: [[-0.353, -0.617, 0.621, 0.568, 1.229, 1.321], [-0.327, 0.58, 0.56, 0.835, 0.644, 0.683]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.113, 1.152, 0.299, 1.212, 0.824, 1.479], [-0.739, -1.761, 0.838, 1.759, 0.908, 0.436]]\nB: [[-1.686, 0.962, 0.402, 1.418, 0.984, 0.915], [-0.524, -1.303, 0.377, 1.429, 0.342, 0.995]]\nC: [[-1.37, 1.148, 0.616, 1.114, 0.537, 1.159], [-0.283, -1.543, 0.412, 1.531, 0.506, 0.887]]\nD: [[-1.358, 1.603, 0.665, 1.495, 0.045, 1.488], [0.077, -1.171, 0.113, 1.245, 0.683, 1.338]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_30_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_30_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the dresser in the scene. The camera pose information includes: the rotation matrix: [[0.993306, 0.029023, -0.111812], [0.110831, -0.512349, 0.851596], [-0.032571, -0.858287, -0.512136]]; the translation vector: [2.482234, 1.391135, 1.348064], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.113, 1.152, 0.299, 1.212, 0.824, 1.479], [-0.739, -1.761, 0.838, 1.759, 0.908, 0.436]]\nB: [[-1.686, 0.962, 0.402, 1.418, 0.984, 0.915], [-0.524, -1.303, 0.377, 1.429, 0.342, 0.995]]\nC: [[-1.37, 1.148, 0.616, 1.114, 0.537, 1.159], [-0.283, -1.543, 0.412, 1.531, 0.506, 0.887]]\nD: [[-1.358, 1.603, 0.665, 1.495, 0.045, 1.488], [0.077, -1.171, 0.113, 1.245, 0.683, 1.338]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.429, 0.564, 1.259, 0.514, 4.432, 2.586], [-1.998, 0.309, 1.385, 0.292, 3.896, 2.792], [0.693, 2.704, 1.079, 1.949, 0.124, 2.2]]\nB: [[1.111, 0.098, 1.082, 0.466, 4.575, 2.917], [-1.93, 0.083, 1.425, -0.025, 4.078, 2.389], [0.372, 3.074, 1.309, 1.613, 0.349, 2.653]]\nC: [[1.746, 0.141, 1.259, 0.14, 4.199, 2.418], [-1.8, 0.062, 1.744, -0.163, 3.558, 2.447], [0.931, 3.17, 1.18, 1.489, -0.095, 2.336]]\nD: [[1.116, 0.433, 1.412, 0.515, 4.324, 2.69], [-1.509, 0.174, 1.744, -0.053, 3.532, 2.532], [0.744, 2.248, 0.965, 1.964, 0.231, 1.764]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_31_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_31_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.32152, -0.4706, 0.821681], [-0.946681, 0.178549, -0.268172], [-0.020508, -0.864092, -0.502915]]; the translation vector: [2.120097, 2.367636, 1.494245], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.429, 0.564, 1.259, 0.514, 4.432, 2.586], [-1.998, 0.309, 1.385, 0.292, 3.896, 2.792], [0.693, 2.704, 1.079, 1.949, 0.124, 2.2]]\nB: [[1.111, 0.098, 1.082, 0.466, 4.575, 2.917], [-1.93, 0.083, 1.425, -0.025, 4.078, 2.389], [0.372, 3.074, 1.309, 1.613, 0.349, 2.653]]\nC: [[1.746, 0.141, 1.259, 0.14, 4.199, 2.418], [-1.8, 0.062, 1.744, -0.163, 3.558, 2.447], [0.931, 3.17, 1.18, 1.489, -0.095, 2.336]]\nD: [[1.116, 0.433, 1.412, 0.515, 4.324, 2.69], [-1.509, 0.174, 1.744, -0.053, 3.532, 2.532], [0.744, 2.248, 0.965, 1.964, 0.231, 1.764]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.189, -0.394, 0.453, 1.615, 0.833, 0.943]]\nB: [[-0.04, -0.278, 0.23, 1.326, 1.046, 0.463]]\nC: [[-0.492, -0.1, 0.679, 1.535, 0.67, -0.014]]\nD: [[0.006, 0.067, 0.535, 1.038, 1.473, 0.446]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_32_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_32_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the coffee table in the scene. The camera pose information includes: the rotation matrix: [[-0.799511, 0.533863, -0.275266], [0.600541, 0.71925, -0.349328], [0.011492, -0.4446, -0.895656]]; the translation vector: [2.031323, 2.312379, 1.200993], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.189, -0.394, 0.453, 1.615, 0.833, 0.943]]\nB: [[-0.04, -0.278, 0.23, 1.326, 1.046, 0.463]]\nC: [[-0.492, -0.1, 0.679, 1.535, 0.67, -0.014]]\nD: [[0.006, 0.067, 0.535, 1.038, 1.473, 0.446]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.534, -3.167, 1.655, 0.929, 0.443, 2.126]]\nB: [[1.524, -3.177, 0.91, 0.507, 0.601, 2.272]]\nC: [[1.265, -3.361, 1.281, 0.587, 0.91, 2.343]]\nD: [[1.106, -3.397, 1.033, 0.365, 0.531, 2.023]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_33_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_33_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shower walls in the scene. The camera pose information includes: the rotation matrix: [[0.590232, -0.352789, 0.726062], [-0.807221, -0.252962, 0.533296], [-0.004475, -0.900861, -0.434086]]; the translation vector: [2.518124, 2.463328, 1.346668], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.534, -3.167, 1.655, 0.929, 0.443, 2.126]]\nB: [[1.524, -3.177, 0.91, 0.507, 0.601, 2.272]]\nC: [[1.265, -3.361, 1.281, 0.587, 0.91, 2.343]]\nD: [[1.106, -3.397, 1.033, 0.365, 0.531, 2.023]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.432, -0.058, 1.349, -0.208, 0.669, 1.766], [0.527, -0.253, 0.616, -0.259, 1.051, 2.58]]\nB: [[-1.145, -0.538, 0.911, 0.071, 0.71, 1.954], [0.803, -0.422, 1.032, 0.108, 0.84, 2.211]]\nC: [[-1.363, -0.409, 0.647, 0.052, 0.929, 2.359], [0.332, 0.057, 1.462, -0.091, 0.807, 2.526]]\nD: [[-1.139, -0.369, 0.72, -0.007, 0.535, 2.292], [0.44, -0.22, 1.3, 0.54, 1.144, 2.107]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_34_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_34_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[-0.464707, 0.496079, -0.733453], [0.882598, 0.326106, -0.338639], [0.071191, -0.804711, -0.589382]]; the translation vector: [2.864701, 0.868861, 1.204561], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.432, -0.058, 1.349, -0.208, 0.669, 1.766], [0.527, -0.253, 0.616, -0.259, 1.051, 2.58]]\nB: [[-1.145, -0.538, 0.911, 0.071, 0.71, 1.954], [0.803, -0.422, 1.032, 0.108, 0.84, 2.211]]\nC: [[-1.363, -0.409, 0.647, 0.052, 0.929, 2.359], [0.332, 0.057, 1.462, -0.091, 0.807, 2.526]]\nD: [[-1.139, -0.369, 0.72, -0.007, 0.535, 2.292], [0.44, -0.22, 1.3, 0.54, 1.144, 2.107]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.366, -0.589, 0.493, 0.271, 1.157, 0.396]]\nB: [[-2.148, -0.107, 0.643, 0.495, 1.354, 0.165]]\nC: [[-2.396, -0.378, 0.719, 0.293, 1.134, 0.807]]\nD: [[-2.162, -0.271, 0.293, -0.116, 0.802, 0.089]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_35_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_35_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the books in the scene. The camera pose information includes: the rotation matrix: [[0.467192, 0.317292, -0.825262], [0.883302, -0.126478, 0.451421], [0.038855, -0.939856, -0.339354]]; the translation vector: [2.723032, 3.168159, 1.438168], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.366, -0.589, 0.493, 0.271, 1.157, 0.396]]\nB: [[-2.148, -0.107, 0.643, 0.495, 1.354, 0.165]]\nC: [[-2.396, -0.378, 0.719, 0.293, 1.134, 0.807]]\nD: [[-2.162, -0.271, 0.293, -0.116, 0.802, 0.089]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.721, -0.518, 0.621, 0.489, 0.194, 0.671], [0.219, 1.2, 0.605, 0.561, 1.11, 0.032]]\nB: [[1.127, -0.237, 0.575, 0.571, 0.442, 0.463], [0.315, 0.86, 0.589, 0.436, 0.639, 0.436]]\nC: [[1.534, -0.019, 0.554, 1.064, 0.929, 0.813], [0.238, 0.541, 0.519, 0.085, 0.619, 0.329]]\nD: [[0.67, 0.235, 1.051, 0.639, -0.039, 0.084], [0.187, 0.38, 0.829, 0.452, 0.327, 0.898]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_36_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_36_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[0.473704, -0.275929, 0.836342], [-0.879436, -0.198746, 0.432542], [0.046868, -0.940406, -0.336809]]; the translation vector: [2.984934, 2.048073, 1.446683], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.721, -0.518, 0.621, 0.489, 0.194, 0.671], [0.219, 1.2, 0.605, 0.561, 1.11, 0.032]]\nB: [[1.127, -0.237, 0.575, 0.571, 0.442, 0.463], [0.315, 0.86, 0.589, 0.436, 0.639, 0.436]]\nC: [[1.534, -0.019, 0.554, 1.064, 0.929, 0.813], [0.238, 0.541, 0.519, 0.085, 0.619, 0.329]]\nD: [[0.67, 0.235, 1.051, 0.639, -0.039, 0.084], [0.187, 0.38, 0.829, 0.452, 0.327, 0.898]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.092, -0.678, 1.584, -0.059, 1.814, 1.264], [-1.925, -0.058, 1.478, -0.34, 2.948, 1.581]]\nB: [[1.208, -0.318, 1.322, 0.047, 1.935, 1.314], [-2.088, -0.879, 1.441, 0.235, 2.296, 1.085]]\nC: [[1.41, -0.38, 1.574, 0.141, 1.666, 1.41], [-1.712, -0.407, 1.364, 0.152, 2.69, 1.496]]\nD: [[1.415, -0.841, 1.953, -0.188, 1.625, 1.182], [-1.333, -0.183, 1.414, 0.172, 2.326, 1.539]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_37_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_37_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the blackboard in the scene. The camera pose information includes: the rotation matrix: [[0.24604, -0.551346, 0.797171], [-0.968826, -0.115295, 0.219278], [-0.028988, -0.826271, -0.562526]]; the translation vector: [1.704247, 2.057158, 1.361636], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.092, -0.678, 1.584, -0.059, 1.814, 1.264], [-1.925, -0.058, 1.478, -0.34, 2.948, 1.581]]\nB: [[1.208, -0.318, 1.322, 0.047, 1.935, 1.314], [-2.088, -0.879, 1.441, 0.235, 2.296, 1.085]]\nC: [[1.41, -0.38, 1.574, 0.141, 1.666, 1.41], [-1.712, -0.407, 1.364, 0.152, 2.69, 1.496]]\nD: [[1.415, -0.841, 1.953, -0.188, 1.625, 1.182], [-1.333, -0.183, 1.414, 0.172, 2.326, 1.539]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.294, -3.518, 1.054, 3.936, 0.361, 0.915], [0.879, 3.786, 1.41, 2.097, 0.63, 1.328]]\nB: [[-0.76, -3.309, 1.31, 3.985, 0.372, 1.047], [0.904, 3.311, 1.519, 1.8, 0.243, 1.41]]\nC: [[-0.969, -3.07, 1.797, 3.572, 0.172, 1.293], [1.021, 3.539, 1.127, 2.014, -0.169, 1.491]]\nD: [[-0.614, -3.4, 1.295, 4.114, 0.42, 0.553], [0.711, 2.902, 1.12, 1.656, 0.643, 1.016]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_38_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_38_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the whiteboard in the scene. The camera pose information includes: the rotation matrix: [[-0.852441, 0.228219, -0.470383], [0.522431, 0.337001, -0.78326], [-0.020235, -0.913426, -0.406502]]; the translation vector: [1.798405, 5.320803, 1.619482], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.294, -3.518, 1.054, 3.936, 0.361, 0.915], [0.879, 3.786, 1.41, 2.097, 0.63, 1.328]]\nB: [[-0.76, -3.309, 1.31, 3.985, 0.372, 1.047], [0.904, 3.311, 1.519, 1.8, 0.243, 1.41]]\nC: [[-0.969, -3.07, 1.797, 3.572, 0.172, 1.293], [1.021, 3.539, 1.127, 2.014, -0.169, 1.491]]\nD: [[-0.614, -3.4, 1.295, 4.114, 0.42, 0.553], [0.711, 2.902, 1.12, 1.656, 0.643, 1.016]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.694, -2.027, 0.441, 1.326, 1.107, 0.898], [-0.288, -2.078, 0.474, 1.039, 1.539, 0.924]]\nB: [[1.654, -2.115, 0.692, 1.098, 1.029, 0.654], [-0.011, -1.968, 0.288, 1.388, 1.994, 1.185]]\nC: [[2.035, -2.378, 0.613, 1.604, 1.492, 1.161], [-0.68, -1.93, 0.48, 0.656, 1.897, 0.701]]\nD: [[1.361, -2.093, 0.306, 1.061, 0.846, 0.974], [-0.065, -1.942, 0.682, 1.519, 1.648, 1.035]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_39_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_39_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the foosball table in the scene. The camera pose information includes: the rotation matrix: [[-0.699126, -0.324611, 0.637064], [-0.713802, 0.265353, -0.648131], [0.041344, -0.907863, -0.417224]]; the translation vector: [0.050403, 3.78209, 1.506908], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.694, -2.027, 0.441, 1.326, 1.107, 0.898], [-0.288, -2.078, 0.474, 1.039, 1.539, 0.924]]\nB: [[1.654, -2.115, 0.692, 1.098, 1.029, 0.654], [-0.011, -1.968, 0.288, 1.388, 1.994, 1.185]]\nC: [[2.035, -2.378, 0.613, 1.604, 1.492, 1.161], [-0.68, -1.93, 0.48, 0.656, 1.897, 0.701]]\nD: [[1.361, -2.093, 0.306, 1.061, 0.846, 0.974], [-0.065, -1.942, 0.682, 1.519, 1.648, 1.035]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.934, -0.844, -0.178, -0.06, 0.379, 0.377]]\nB: [[-2.118, -0.866, 0.424, -0.166, 0.218, 0.556]]\nC: [[-2.075, -0.928, -0.19, 0.558, 0.471, 0.431]]\nD: [[-1.78, -0.879, 0.057, 0.14, 0.194, 0.118]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_40_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_40_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.079918, -0.690871, 0.718547], [-0.996802, 0.055321, -0.057677], [9.6e-05, -0.720858, -0.693082]]; the translation vector: [1.142658, 0.968078, 1.385987], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.934, -0.844, -0.178, -0.06, 0.379, 0.377]]\nB: [[-2.118, -0.866, 0.424, -0.166, 0.218, 0.556]]\nC: [[-2.075, -0.928, -0.19, 0.558, 0.471, 0.431]]\nD: [[-1.78, -0.879, 0.057, 0.14, 0.194, 0.118]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.004, -0.056, 0.156, 0.548, 2.574, 0.973], [0.863, 1.538, 0.8, 1.622, 0.932, 0.511]]\nB: [[-0.752, -0.451, 0.479, 0.974, 2.169, 0.971], [0.505, 1.322, 0.592, 1.774, 0.902, 0.995]]\nC: [[-0.502, -0.659, 0.53, 0.847, 2.257, 0.624], [0.069, 1.171, 0.213, 2.015, 1.277, 1.24]]\nD: [[-0.413, -0.371, 0.765, 1.102, 2.094, 1.312], [0.364, 1.532, 0.25, 2.233, 1.243, 0.916]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_41_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_41_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the couch in the scene. The camera pose information includes: the rotation matrix: [[-0.861262, 0.35211, -0.366398], [0.508128, 0.60504, -0.61297], [0.005853, -0.714105, -0.700014]]; the translation vector: [3.145762, 3.637784, 1.437024], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.004, -0.056, 0.156, 0.548, 2.574, 0.973], [0.863, 1.538, 0.8, 1.622, 0.932, 0.511]]\nB: [[-0.752, -0.451, 0.479, 0.974, 2.169, 0.971], [0.505, 1.322, 0.592, 1.774, 0.902, 0.995]]\nC: [[-0.502, -0.659, 0.53, 0.847, 2.257, 0.624], [0.069, 1.171, 0.213, 2.015, 1.277, 1.24]]\nD: [[-0.413, -0.371, 0.765, 1.102, 2.094, 1.312], [0.364, 1.532, 0.25, 2.233, 1.243, 0.916]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.464, -1.008, 0.704, 0.548, 0.603, 1.02], [-0.19, -0.382, 0.144, 0.733, 0.716, 0.646], [-0.404, 0.288, 0.436, 0.98, 0.357, 1.05], [1.664, -1.226, -0.006, 0.561, 0.79, 0.588], [0.842, 1.15, 0.487, 0.536, 0.705, 0.632], [0.433, 0.35, -0.074, 0.642, 0.688, 0.335], [1.494, 3.097, 0.552, 0.862, 0.855, 0.649], [-1.799, -1.668, 0.843, 1.135, 1.012, 0.533], [2.071, -0.103, 0.082, 0.7, 0.467, 1.183], [2.558, 1.11, 0.748, 0.486, 0.458, 0.736], [-0.954, 2.892, -0.06, 0.296, 0.278, 1.194], [-1.303, 2.083, 0.061, 0.236, 0.278, 0.444], [-1.569, 0.894, 0.218, 0.482, 1.049, 0.471]]\nB: [[0.772, -0.719, 0.389, 0.713, 0.789, 0.818], [-0.024, -0.745, 0.397, 0.693, 0.69, 0.791], [-0.445, -0.009, 0.396, 0.704, 0.6, 0.798], [1.881, -0.924, 0.405, 0.629, 0.643, 0.773], [0.681, 0.918, 0.401, 0.691, 0.741, 0.776], [0.646, 0.122, 0.392, 0.618, 0.697, 0.804], [1.675, 2.694, 0.343, 0.794, 0.824, 0.712], [-1.741, -1.918, 0.384, 0.689, 0.734, 0.793], [1.972, 0.182, 0.329, 0.798, 0.905, 0.759], [2.104, 1.432, 0.601, 0.176, 0.467, 0.305], [-1.26, 2.803, 0.397, 0.519, 0.618, 0.85], [-1.699, 1.837, 0.379, 0.732, 0.671, 0.798], [-1.685, 1.314, 0.409, 0.719, 0.764, 0.815]]\nC: [[0.533, -0.974, 0.234, 0.918, 0.378, 0.964], [-0.355, -1.19, 0.156, 0.302, 0.635, 0.774], [-0.597, 0.157, 0.288, 1.05, 0.184, 0.298], [2.072, -0.909, 0.536, 0.468, 0.691, 0.463], [0.786, 1.284, 0.692, 1.11, 1.012, 1.207], [0.407, 0.333, 0.418, 0.195, 0.858, 0.97], [1.968, 3.191, -0.153, 0.695, 1.269, 0.454], [-1.257, -1.997, 0.349, 0.303, 0.286, 0.552], [2.317, 0.459, 0.175, 0.403, 1.116, 1.213], [2.141, 1.823, 0.68, -0.29, 0.059, -0.035], [-1.354, 3.299, 0.362, 0.406, 0.802, 0.98], [-2.092, 2.265, 0.732, 1.224, 0.725, 0.93], [-1.784, 1.414, 0.713, 0.316, 1.116, 0.675]]\nD: [[0.989, -0.333, 0.223, 0.813, 0.656, 0.519], [0.19, -0.985, 0.389, 0.303, 0.729, 1.121], [-0.625, 0.156, 0.665, 1.074, 0.926, 0.429], [2.366, -0.669, 0.862, 0.551, 0.718, 0.409], [1.078, 0.548, 0.472, 1.129, 0.587, 0.295], [0.268, -0.298, 0.199, 0.384, 0.582, 0.724], [1.775, 3.124, 0.353, 0.87, 1.306, 0.424], [-2.119, -2.015, 0.712, 0.444, 0.613, 1.097], [2.125, 0.536, -0.025, 0.783, 0.67, 0.385], [2.16, 1.441, 0.464, 0.575, 0.443, 0.108], [-1.127, 3.006, 0.402, 0.226, 0.819, 0.552], [-1.981, 2.007, -0.054, 1.127, 0.372, 0.846], [-1.217, 1.009, -0.072, 0.967, 0.351, 1.126]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_42_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_42_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[0.951558, 0.16536, -0.259218], [0.307283, -0.481983, 0.820531], [0.010744, -0.860436, -0.509446]]; the translation vector: [2.919862, 3.428013, 1.521081], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.464, -1.008, 0.704, 0.548, 0.603, 1.02], [-0.19, -0.382, 0.144, 0.733, 0.716, 0.646], [-0.404, 0.288, 0.436, 0.98, 0.357, 1.05], [1.664, -1.226, -0.006, 0.561, 0.79, 0.588], [0.842, 1.15, 0.487, 0.536, 0.705, 0.632], [0.433, 0.35, -0.074, 0.642, 0.688, 0.335], [1.494, 3.097, 0.552, 0.862, 0.855, 0.649], [-1.799, -1.668, 0.843, 1.135, 1.012, 0.533], [2.071, -0.103, 0.082, 0.7, 0.467, 1.183], [2.558, 1.11, 0.748, 0.486, 0.458, 0.736], [-0.954, 2.892, -0.06, 0.296, 0.278, 1.194], [-1.303, 2.083, 0.061, 0.236, 0.278, 0.444], [-1.569, 0.894, 0.218, 0.482, 1.049, 0.471]]\nB: [[0.772, -0.719, 0.389, 0.713, 0.789, 0.818], [-0.024, -0.745, 0.397, 0.693, 0.69, 0.791], [-0.445, -0.009, 0.396, 0.704, 0.6, 0.798], [1.881, -0.924, 0.405, 0.629, 0.643, 0.773], [0.681, 0.918, 0.401, 0.691, 0.741, 0.776], [0.646, 0.122, 0.392, 0.618, 0.697, 0.804], [1.675, 2.694, 0.343, 0.794, 0.824, 0.712], [-1.741, -1.918, 0.384, 0.689, 0.734, 0.793], [1.972, 0.182, 0.329, 0.798, 0.905, 0.759], [2.104, 1.432, 0.601, 0.176, 0.467, 0.305], [-1.26, 2.803, 0.397, 0.519, 0.618, 0.85], [-1.699, 1.837, 0.379, 0.732, 0.671, 0.798], [-1.685, 1.314, 0.409, 0.719, 0.764, 0.815]]\nC: [[0.533, -0.974, 0.234, 0.918, 0.378, 0.964], [-0.355, -1.19, 0.156, 0.302, 0.635, 0.774], [-0.597, 0.157, 0.288, 1.05, 0.184, 0.298], [2.072, -0.909, 0.536, 0.468, 0.691, 0.463], [0.786, 1.284, 0.692, 1.11, 1.012, 1.207], [0.407, 0.333, 0.418, 0.195, 0.858, 0.97], [1.968, 3.191, -0.153, 0.695, 1.269, 0.454], [-1.257, -1.997, 0.349, 0.303, 0.286, 0.552], [2.317, 0.459, 0.175, 0.403, 1.116, 1.213], [2.141, 1.823, 0.68, -0.29, 0.059, -0.035], [-1.354, 3.299, 0.362, 0.406, 0.802, 0.98], [-2.092, 2.265, 0.732, 1.224, 0.725, 0.93], [-1.784, 1.414, 0.713, 0.316, 1.116, 0.675]]\nD: [[0.989, -0.333, 0.223, 0.813, 0.656, 0.519], [0.19, -0.985, 0.389, 0.303, 0.729, 1.121], [-0.625, 0.156, 0.665, 1.074, 0.926, 0.429], [2.366, -0.669, 0.862, 0.551, 0.718, 0.409], [1.078, 0.548, 0.472, 1.129, 0.587, 0.295], [0.268, -0.298, 0.199, 0.384, 0.582, 0.724], [1.775, 3.124, 0.353, 0.87, 1.306, 0.424], [-2.119, -2.015, 0.712, 0.444, 0.613, 1.097], [2.125, 0.536, -0.025, 0.783, 0.67, 0.385], [2.16, 1.441, 0.464, 0.575, 0.443, 0.108], [-1.127, 3.006, 0.402, 0.226, 0.819, 0.552], [-1.981, 2.007, -0.054, 1.127, 0.372, 0.846], [-1.217, 1.009, -0.072, 0.967, 0.351, 1.126]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.467, 4.66, 0.479, 1.188, 0.953, 0.41], [-0.153, 2.813, 0.487, 0.892, 1.061, 0.54], [0.179, 1.102, 0.898, 0.937, 1.029, 0.418], [1.771, 1.676, 0.076, 1.486, 0.58, 0.338], [1.933, -1.484, -0.005, 1.579, 0.28, 0.413], [-0.13, 5.403, 0.908, 0.195, 1.153, 0.536]]\nB: [[2.334, 4.242, 0.72, 1.63, 0.144, 0.491], [-0.388, 2.622, 0.373, 0.028, 1.059, 0.86], [0.405, 0.837, 0.295, 0.037, 1.048, 0.862], [2.233, 1.663, 0.713, 1.024, 0.482, 0.49], [1.733, -1.171, 0.524, 1.296, 0.141, 0.721], [-0.04, 4.994, 0.079, 0.373, 0.629, 0.062]]\nC: [[2.181, 4.661, 0.338, 1.164, 0.599, 0.637], [-0.181, 2.466, 0.019, 0.362, 0.849, 0.165], [-0.035, 1.291, 0.403, 0.392, 0.795, 0.638], [2.338, 1.45, 0.464, 0.895, 0.891, 0.816], [2.039, -1.264, 0.768, 1.237, 0.686, -0.053], [-0.249, 5.084, 0.593, 0.24, 0.421, 0.492]]\nD: [[1.918, 4.662, 0.478, 1.328, 0.546, 0.414], [0.093, 2.502, 0.4, 0.472, 0.776, 0.619], [0.138, 1.203, 0.414, 0.446, 0.869, 0.461], [1.918, 1.858, 0.513, 1.358, 0.507, 0.451], [2.021, -1.528, 0.41, 1.371, 0.472, 0.431], [0.209, 5.284, 0.463, 0.428, 0.778, 0.322]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_43_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_43_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the bench in the scene. The camera pose information includes: the rotation matrix: [[-0.482968, -0.397392, 0.78027], [-0.874514, 0.173759, -0.452807], [0.044362, -0.901048, -0.431445]]; the translation vector: [8.974016, 2.795387, 1.945192], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.467, 4.66, 0.479, 1.188, 0.953, 0.41], [-0.153, 2.813, 0.487, 0.892, 1.061, 0.54], [0.179, 1.102, 0.898, 0.937, 1.029, 0.418], [1.771, 1.676, 0.076, 1.486, 0.58, 0.338], [1.933, -1.484, -0.005, 1.579, 0.28, 0.413], [-0.13, 5.403, 0.908, 0.195, 1.153, 0.536]]\nB: [[2.334, 4.242, 0.72, 1.63, 0.144, 0.491], [-0.388, 2.622, 0.373, 0.028, 1.059, 0.86], [0.405, 0.837, 0.295, 0.037, 1.048, 0.862], [2.233, 1.663, 0.713, 1.024, 0.482, 0.49], [1.733, -1.171, 0.524, 1.296, 0.141, 0.721], [-0.04, 4.994, 0.079, 0.373, 0.629, 0.062]]\nC: [[2.181, 4.661, 0.338, 1.164, 0.599, 0.637], [-0.181, 2.466, 0.019, 0.362, 0.849, 0.165], [-0.035, 1.291, 0.403, 0.392, 0.795, 0.638], [2.338, 1.45, 0.464, 0.895, 0.891, 0.816], [2.039, -1.264, 0.768, 1.237, 0.686, -0.053], [-0.249, 5.084, 0.593, 0.24, 0.421, 0.492]]\nD: [[1.918, 4.662, 0.478, 1.328, 0.546, 0.414], [0.093, 2.502, 0.4, 0.472, 0.776, 0.619], [0.138, 1.203, 0.414, 0.446, 0.869, 0.461], [1.918, 1.858, 0.513, 1.358, 0.507, 0.451], [2.021, -1.528, 0.41, 1.371, 0.472, 0.431], [0.209, 5.284, 0.463, 0.428, 0.778, 0.322]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.653, 1.297, 0.818, 0.22, 0.426, 1.095]]\nB: [[-1.511, 1.726, 0.43, 0.986, 0.39, 0.401]]\nC: [[-0.81, 1.586, -0.129, 0.278, 0.94, 0.466]]\nD: [[-1.238, 1.344, 0.361, 0.491, 0.703, 0.77]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_44_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_44_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the toilet in the scene. The camera pose information includes: the rotation matrix: [[0.573165, 0.475287, -0.667521], [0.819422, -0.337921, 0.462988], [-0.005517, -0.81235, -0.583144]]; the translation vector: [4.230747, 1.597944, 1.425469], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.653, 1.297, 0.818, 0.22, 0.426, 1.095]]\nB: [[-1.511, 1.726, 0.43, 0.986, 0.39, 0.401]]\nC: [[-0.81, 1.586, -0.129, 0.278, 0.94, 0.466]]\nD: [[-1.238, 1.344, 0.361, 0.491, 0.703, 0.77]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.21, -0.485, 1.162, 0.745, 0.977, 0.286], [0.409, 1.308, 0.507, 0.07, 0.653, 0.254], [-1.112, -1.013, 0.307, 0.502, 0.872, 0.567], [-0.178, 2.738, 0.98, 0.567, 0.888, 0.53], [0.062, 1.603, 0.573, 0.692, 0.195, 0.048], [0.203, -0.892, 0.691, 0.653, 0.429, 0.535], [0.028, 1.784, 0.14, 1.147, 0.406, 0.851], [-1.458, 1.116, 0.904, -0.122, 0.156, 0.773], [-1.192, -0.149, 0.71, 0.375, 0.509, 0.581], [1.914, -2.0, 0.71, 0.384, 0.176, -0.331], [1.687, -2.156, 1.387, -0.058, 0.551, 0.368], [0.793, -1.724, 1.309, 1.148, 0.62, 0.588], [1.399, -0.955, 1.401, 0.399, 0.543, 0.388], [-0.785, -3.035, 1.174, 0.319, 0.082, 0.789], [-0.814, -2.329, 0.623, 0.245, 0.091, 0.496], [-1.62, -3.469, 0.316, 0.527, 0.537, -0.091]]\nB: [[-0.08, -0.154, 1.025, 0.591, 0.905, 0.775], [0.195, 0.928, 0.983, 0.833, 0.216, 0.071], [-0.777, -0.244, 0.921, 0.352, 0.434, 0.837], [-0.104, 1.99, 0.831, 0.825, 0.625, 0.159], [1.019, 2.186, 0.505, 0.763, 0.5, 0.673], [0.085, -0.695, 1.038, 0.323, 0.449, 0.684], [-0.014, 1.677, 0.448, 0.846, 0.305, -0.088], [-0.906, 1.351, 0.456, 0.541, 1.066, 0.626], [-1.282, 0.246, 0.87, 0.842, 0.096, -0.15], [1.42, -1.945, 0.918, 0.762, 0.341, 0.254], [1.009, -1.899, 1.409, -0.041, 0.531, 0.04], [0.874, -1.746, 1.047, 0.664, 0.437, 0.465], [0.723, -1.178, 0.705, 0.411, 0.715, 0.301], [-0.325, -2.808, 0.799, 0.443, 0.515, -0.023], [-1.586, -1.764, 0.236, 0.308, 0.382, 0.158], [-1.704, -3.657, 0.202, 0.579, -0.129, 0.217]]\nC: [[0.074, -0.449, 0.793, 0.473, 0.548, 0.492], [0.224, 1.038, 0.781, 0.486, 0.546, 0.522], [-0.941, -0.689, 0.578, 0.69, 0.615, 0.42], [-0.002, 2.387, 0.73, 0.7, 0.637, 0.471], [0.54, 1.814, 0.876, 0.434, 0.466, 0.544], [-0.295, -1.043, 0.729, 0.48, 0.533, 0.501], [-0.372, 1.676, 0.602, 0.676, 0.567, 0.405], [-1.148, 1.569, 0.485, 0.349, 0.639, 0.495], [-0.821, 0.09, 0.693, 0.409, 0.4, 0.269], [1.644, -1.897, 1.107, 0.364, 0.18, 0.12], [1.307, -2.14, 1.134, 0.414, 0.578, 0.428], [0.763, -1.797, 0.957, 0.648, 0.49, 0.409], [1.155, -1.373, 0.909, 0.317, 0.441, 0.117], [-0.563, -2.579, 0.735, 0.309, 0.521, 0.447], [-1.263, -2.059, 0.721, 0.472, 0.232, 0.232], [-1.688, -3.278, 0.604, 0.595, 0.313, 0.401]]\nD: [[0.369, -0.147, 1.222, 0.22, 0.106, 0.249], [0.37, 1.261, 1.11, 0.14, 1.02, 0.894], [-0.639, -0.96, 0.333, 0.677, 0.877, 0.601], [0.112, 1.921, 0.621, 0.682, 0.214, 0.04], [0.061, 1.445, 0.485, 0.375, 0.738, 0.414], [-0.478, -0.871, 0.684, 0.362, 0.566, 0.762], [-0.314, 1.927, 0.136, 0.42, 0.773, 0.685], [-1.086, 1.078, 0.616, 0.363, 0.796, 0.02], [-0.78, 0.455, 1.075, -0.039, 0.211, 0.125], [1.409, -1.503, 1.252, 0.797, 0.258, -0.146], [1.115, -1.981, 0.929, 0.053, 0.518, 0.484], [1.215, -2.2, 1.257, 0.76, 0.293, 0.427], [1.189, -1.058, 0.631, 0.369, 0.328, -0.119], [-0.365, -2.692, 1.041, -0.142, 0.542, 0.05], [-0.833, -2.437, 0.641, 0.718, 0.012, 0.121], [-2.016, -3.644, 1.062, 0.946, -0.031, -0.016]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_45_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_45_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.844798, -0.442354, 0.301064], [-0.534849, 0.714819, -0.450523], [-0.015916, -0.541624, -0.84047]]; the translation vector: [3.085932, 7.995926, 1.934485], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.21, -0.485, 1.162, 0.745, 0.977, 0.286], [0.409, 1.308, 0.507, 0.07, 0.653, 0.254], [-1.112, -1.013, 0.307, 0.502, 0.872, 0.567], [-0.178, 2.738, 0.98, 0.567, 0.888, 0.53], [0.062, 1.603, 0.573, 0.692, 0.195, 0.048], [0.203, -0.892, 0.691, 0.653, 0.429, 0.535], [0.028, 1.784, 0.14, 1.147, 0.406, 0.851], [-1.458, 1.116, 0.904, -0.122, 0.156, 0.773], [-1.192, -0.149, 0.71, 0.375, 0.509, 0.581], [1.914, -2.0, 0.71, 0.384, 0.176, -0.331], [1.687, -2.156, 1.387, -0.058, 0.551, 0.368], [0.793, -1.724, 1.309, 1.148, 0.62, 0.588], [1.399, -0.955, 1.401, 0.399, 0.543, 0.388], [-0.785, -3.035, 1.174, 0.319, 0.082, 0.789], [-0.814, -2.329, 0.623, 0.245, 0.091, 0.496], [-1.62, -3.469, 0.316, 0.527, 0.537, -0.091]]\nB: [[-0.08, -0.154, 1.025, 0.591, 0.905, 0.775], [0.195, 0.928, 0.983, 0.833, 0.216, 0.071], [-0.777, -0.244, 0.921, 0.352, 0.434, 0.837], [-0.104, 1.99, 0.831, 0.825, 0.625, 0.159], [1.019, 2.186, 0.505, 0.763, 0.5, 0.673], [0.085, -0.695, 1.038, 0.323, 0.449, 0.684], [-0.014, 1.677, 0.448, 0.846, 0.305, -0.088], [-0.906, 1.351, 0.456, 0.541, 1.066, 0.626], [-1.282, 0.246, 0.87, 0.842, 0.096, -0.15], [1.42, -1.945, 0.918, 0.762, 0.341, 0.254], [1.009, -1.899, 1.409, -0.041, 0.531, 0.04], [0.874, -1.746, 1.047, 0.664, 0.437, 0.465], [0.723, -1.178, 0.705, 0.411, 0.715, 0.301], [-0.325, -2.808, 0.799, 0.443, 0.515, -0.023], [-1.586, -1.764, 0.236, 0.308, 0.382, 0.158], [-1.704, -3.657, 0.202, 0.579, -0.129, 0.217]]\nC: [[0.074, -0.449, 0.793, 0.473, 0.548, 0.492], [0.224, 1.038, 0.781, 0.486, 0.546, 0.522], [-0.941, -0.689, 0.578, 0.69, 0.615, 0.42], [-0.002, 2.387, 0.73, 0.7, 0.637, 0.471], [0.54, 1.814, 0.876, 0.434, 0.466, 0.544], [-0.295, -1.043, 0.729, 0.48, 0.533, 0.501], [-0.372, 1.676, 0.602, 0.676, 0.567, 0.405], [-1.148, 1.569, 0.485, 0.349, 0.639, 0.495], [-0.821, 0.09, 0.693, 0.409, 0.4, 0.269], [1.644, -1.897, 1.107, 0.364, 0.18, 0.12], [1.307, -2.14, 1.134, 0.414, 0.578, 0.428], [0.763, -1.797, 0.957, 0.648, 0.49, 0.409], [1.155, -1.373, 0.909, 0.317, 0.441, 0.117], [-0.563, -2.579, 0.735, 0.309, 0.521, 0.447], [-1.263, -2.059, 0.721, 0.472, 0.232, 0.232], [-1.688, -3.278, 0.604, 0.595, 0.313, 0.401]]\nD: [[0.369, -0.147, 1.222, 0.22, 0.106, 0.249], [0.37, 1.261, 1.11, 0.14, 1.02, 0.894], [-0.639, -0.96, 0.333, 0.677, 0.877, 0.601], [0.112, 1.921, 0.621, 0.682, 0.214, 0.04], [0.061, 1.445, 0.485, 0.375, 0.738, 0.414], [-0.478, -0.871, 0.684, 0.362, 0.566, 0.762], [-0.314, 1.927, 0.136, 0.42, 0.773, 0.685], [-1.086, 1.078, 0.616, 0.363, 0.796, 0.02], [-0.78, 0.455, 1.075, -0.039, 0.211, 0.125], [1.409, -1.503, 1.252, 0.797, 0.258, -0.146], [1.115, -1.981, 0.929, 0.053, 0.518, 0.484], [1.215, -2.2, 1.257, 0.76, 0.293, 0.427], [1.189, -1.058, 0.631, 0.369, 0.328, -0.119], [-0.365, -2.692, 1.041, -0.142, 0.542, 0.05], [-0.833, -2.437, 0.641, 0.718, 0.012, 0.121], [-2.016, -3.644, 1.062, 0.946, -0.031, -0.016]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.564, -1.252, 0.695, 0.857, 0.171, 1.483], [-0.805, -0.829, 1.211, 0.152, 2.16, 2.47], [-1.232, 0.204, 0.072, 0.257, 0.055, 0.162], [0.074, 0.233, 1.112, 1.831, 0.158, 2.315], [0.903, -0.43, 1.266, 0.188, 1.396, 2.012], [0.949, -1.643, 0.661, 0.101, 0.755, 1.399], [0.614, -1.995, 0.572, 0.724, 0.056, 1.162]]\nB: [[0.352, -1.05, 0.8, 0.589, 0.069, 1.588], [-0.381, -1.063, 0.88, -0.027, 2.004, 2.387], [-0.929, 0.409, 0.362, 0.309, 0.339, -0.301], [-0.26, 0.432, 1.078, 1.853, 0.513, 2.721], [1.171, -0.028, 1.724, -0.263, 0.948, 2.304], [1.072, -2.041, 1.024, -0.297, 0.869, 1.517], [0.352, -2.32, 0.85, 0.916, -0.424, 1.2]]\nC: [[0.424, -1.563, 1.009, 0.591, -0.023, 1.935], [-0.442, -0.344, 1.695, 0.23, 2.524, 2.736], [-1.205, 0.414, 0.154, -0.209, -0.177, -0.009], [-0.098, 0.328, 1.36, 1.735, 0.101, 1.922], [0.835, -0.195, 1.265, 0.532, 0.907, 2.267], [1.354, -1.455, 1.149, 0.399, 0.893, 1.521], [0.733, -1.909, 0.585, 1.055, -0.351, 1.621]]\nD: [[0.598, -1.618, 0.741, 0.612, 0.383, 1.422], [-0.532, -0.954, 1.597, 0.537, 2.362, 2.085], [-0.843, 0.31, -0.092, 0.065, -0.048, 0.556], [0.212, 0.31, 0.904, 1.605, 0.458, 1.973], [0.493, -0.221, 1.142, 0.015, 1.45, 2.441], [1.383, -2.104, 0.997, -0.035, 0.835, 1.803], [0.664, -2.077, 1.046, 1.1, 0.235, 1.396]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_46_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_46_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.982764, 0.054289, -0.17671], [0.184841, -0.27426, 0.943724], [0.002769, -0.960122, -0.279568]]; the translation vector: [4.072058, 1.220293, 1.47625], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.564, -1.252, 0.695, 0.857, 0.171, 1.483], [-0.805, -0.829, 1.211, 0.152, 2.16, 2.47], [-1.232, 0.204, 0.072, 0.257, 0.055, 0.162], [0.074, 0.233, 1.112, 1.831, 0.158, 2.315], [0.903, -0.43, 1.266, 0.188, 1.396, 2.012], [0.949, -1.643, 0.661, 0.101, 0.755, 1.399], [0.614, -1.995, 0.572, 0.724, 0.056, 1.162]]\nB: [[0.352, -1.05, 0.8, 0.589, 0.069, 1.588], [-0.381, -1.063, 0.88, -0.027, 2.004, 2.387], [-0.929, 0.409, 0.362, 0.309, 0.339, -0.301], [-0.26, 0.432, 1.078, 1.853, 0.513, 2.721], [1.171, -0.028, 1.724, -0.263, 0.948, 2.304], [1.072, -2.041, 1.024, -0.297, 0.869, 1.517], [0.352, -2.32, 0.85, 0.916, -0.424, 1.2]]\nC: [[0.424, -1.563, 1.009, 0.591, -0.023, 1.935], [-0.442, -0.344, 1.695, 0.23, 2.524, 2.736], [-1.205, 0.414, 0.154, -0.209, -0.177, -0.009], [-0.098, 0.328, 1.36, 1.735, 0.101, 1.922], [0.835, -0.195, 1.265, 0.532, 0.907, 2.267], [1.354, -1.455, 1.149, 0.399, 0.893, 1.521], [0.733, -1.909, 0.585, 1.055, -0.351, 1.621]]\nD: [[0.598, -1.618, 0.741, 0.612, 0.383, 1.422], [-0.532, -0.954, 1.597, 0.537, 2.362, 2.085], [-0.843, 0.31, -0.092, 0.065, -0.048, 0.556], [0.212, 0.31, 0.904, 1.605, 0.458, 1.973], [0.493, -0.221, 1.142, 0.015, 1.45, 2.441], [1.383, -2.104, 0.997, -0.035, 0.835, 1.803], [0.664, -2.077, 1.046, 1.1, 0.235, 1.396]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.784, -1.767, 0.476, 0.231, 0.24, 0.946], [-0.366, -0.774, 1.245, 0.024, 1.212, 0.879], [0.003, -1.957, 1.089, 0.298, 0.345, 0.685], [0.22, -0.648, 1.189, -0.053, 1.037, 0.737]]\nB: [[0.318, -1.739, 0.9, 0.365, 0.659, 0.502], [-0.143, -0.934, 0.904, 0.311, 0.754, 0.487], [-0.263, -1.46, 0.926, 0.248, 0.697, 0.452], [0.319, -1.069, 0.941, 0.277, 0.615, 0.5]]\nC: [[0.289, -1.409, 1.144, 0.67, 0.233, 0.02], [0.068, -0.634, 0.752, -0.119, 1.056, 0.899], [0.211, -1.754, 1.05, -0.206, 0.931, 0.732], [-0.148, -1.524, 1.046, -0.083, 1.07, 0.467]]\nD: [[0.118, -1.502, 0.988, 0.826, 0.676, 0.125], [-0.431, -1.392, 0.927, 0.243, 0.317, 0.128], [-0.041, -1.634, 0.476, -0.222, 0.764, 0.802], [0.199, -1.056, 1.182, 0.1, 0.287, 0.626]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_47_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_47_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the monitor in the scene. The camera pose information includes: the rotation matrix: [[-0.481759, -0.460793, 0.745371], [-0.875469, 0.290199, -0.386444], [-0.038235, -0.838722, -0.543216]]; the translation vector: [3.08436, 2.075189, 1.468295], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.784, -1.767, 0.476, 0.231, 0.24, 0.946], [-0.366, -0.774, 1.245, 0.024, 1.212, 0.879], [0.003, -1.957, 1.089, 0.298, 0.345, 0.685], [0.22, -0.648, 1.189, -0.053, 1.037, 0.737]]\nB: [[0.318, -1.739, 0.9, 0.365, 0.659, 0.502], [-0.143, -0.934, 0.904, 0.311, 0.754, 0.487], [-0.263, -1.46, 0.926, 0.248, 0.697, 0.452], [0.319, -1.069, 0.941, 0.277, 0.615, 0.5]]\nC: [[0.289, -1.409, 1.144, 0.67, 0.233, 0.02], [0.068, -0.634, 0.752, -0.119, 1.056, 0.899], [0.211, -1.754, 1.05, -0.206, 0.931, 0.732], [-0.148, -1.524, 1.046, -0.083, 1.07, 0.467]]\nD: [[0.118, -1.502, 0.988, 0.826, 0.676, 0.125], [-0.431, -1.392, 0.927, 0.243, 0.317, 0.128], [-0.041, -1.634, 0.476, -0.222, 0.764, 0.802], [0.199, -1.056, 1.182, 0.1, 0.287, 0.626]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.668, 1.082, 1.323, 0.017, 1.605, 2.936], [2.048, 0.314, 0.42, 0.03, 0.539, 1.761], [1.696, -0.749, 1.568, 0.626, 3.055, 2.333], [0.245, -2.087, 0.863, 3.306, 0.237, 1.624], [-1.623, -0.389, 0.569, -0.197, 4.462, 2.242], [-0.209, 2.531, 1.246, 3.173, 0.694, 2.668]]\nB: [[2.245, 1.07, 1.564, -0.254, 1.675, 2.343], [1.954, 0.142, 1.271, 0.081, 0.135, 1.547], [1.76, -0.992, 1.136, -0.068, 2.332, 2.454], [-0.223, -2.118, 0.902, 3.53, 0.211, 2.156], [-1.973, 0.007, 0.511, -0.091, 4.007, 2.11], [0.095, 1.842, 1.661, 2.856, 0.289, 2.599]]\nC: [[1.954, 0.955, 1.134, 0.469, 1.906, 3.136], [2.108, 0.777, 1.024, 0.49, 0.432, 1.736], [1.804, -1.161, 1.159, 0.579, 2.431, 2.741], [-0.075, -2.215, 1.141, 4.022, 0.647, 1.726], [-1.259, -0.097, 0.765, 0.259, 4.315, 1.492], [0.085, 2.397, 1.377, 2.831, 0.531, 2.459]]\nD: [[1.757, 1.207, 1.277, 0.171, 1.443, 2.662], [1.938, 0.553, 0.792, 0.372, 0.083, 1.699], [2.057, -0.732, 1.221, 0.308, 2.678, 2.532], [0.254, -2.161, 1.253, 3.68, 0.191, 2.014], [-1.595, -0.042, 0.831, 0.273, 4.363, 1.747], [0.19, 2.052, 1.297, 3.302, 0.408, 2.639]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_48_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_48_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.935878, -0.161972, 0.312885], [-0.352322, 0.433116, -0.829627], [-0.001139, -0.886666, -0.46241]]; the translation vector: [1.123681, 2.231354, 1.408983], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.668, 1.082, 1.323, 0.017, 1.605, 2.936], [2.048, 0.314, 0.42, 0.03, 0.539, 1.761], [1.696, -0.749, 1.568, 0.626, 3.055, 2.333], [0.245, -2.087, 0.863, 3.306, 0.237, 1.624], [-1.623, -0.389, 0.569, -0.197, 4.462, 2.242], [-0.209, 2.531, 1.246, 3.173, 0.694, 2.668]]\nB: [[2.245, 1.07, 1.564, -0.254, 1.675, 2.343], [1.954, 0.142, 1.271, 0.081, 0.135, 1.547], [1.76, -0.992, 1.136, -0.068, 2.332, 2.454], [-0.223, -2.118, 0.902, 3.53, 0.211, 2.156], [-1.973, 0.007, 0.511, -0.091, 4.007, 2.11], [0.095, 1.842, 1.661, 2.856, 0.289, 2.599]]\nC: [[1.954, 0.955, 1.134, 0.469, 1.906, 3.136], [2.108, 0.777, 1.024, 0.49, 0.432, 1.736], [1.804, -1.161, 1.159, 0.579, 2.431, 2.741], [-0.075, -2.215, 1.141, 4.022, 0.647, 1.726], [-1.259, -0.097, 0.765, 0.259, 4.315, 1.492], [0.085, 2.397, 1.377, 2.831, 0.531, 2.459]]\nD: [[1.757, 1.207, 1.277, 0.171, 1.443, 2.662], [1.938, 0.553, 0.792, 0.372, 0.083, 1.699], [2.057, -0.732, 1.221, 0.308, 2.678, 2.532], [0.254, -2.161, 1.253, 3.68, 0.191, 2.014], [-1.595, -0.042, 0.831, 0.273, 4.363, 1.747], [0.19, 2.052, 1.297, 3.302, 0.408, 2.639]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.874, 0.432, 0.675, 0.547, 4.466, 2.765], [2.077, 1.025, 0.96, 0.435, 5.53, 2.428], [-0.302, -2.355, 0.907, 2.255, -0.132, 2.065], [1.309, 2.97, 0.649, 2.105, 0.601, 1.706], [1.394, -2.794, 0.572, 0.929, 0.444, 0.269]]\nB: [[-1.774, -0.336, 1.363, 0.137, 4.327, 2.118], [2.024, 0.331, 0.678, 0.299, 6.038, 2.411], [-0.973, -2.453, 1.173, 2.209, -0.12, 1.889], [0.907, 3.079, 0.375, 1.445, 0.297, 1.527], [1.457, -2.684, 0.632, 0.896, -0.39, 0.289]]\nC: [[-2.285, -0.2, 1.099, -0.248, 4.908, 2.313], [2.096, 0.355, 1.235, 0.363, 5.974, 2.044], [-1.085, -2.082, 0.91, 2.454, 0.239, 1.438], [0.732, 3.157, 0.493, 1.665, 0.182, 1.592], [1.088, -2.467, -0.003, 0.673, 0.233, 0.167]]\nD: [[-1.815, -0.066, 1.137, 0.19, 4.502, 2.283], [1.738, 0.547, 0.94, 0.42, 5.701, 2.065], [-0.777, -2.286, 1.047, 2.091, 0.123, 1.885], [1.023, 3.389, 0.735, 1.699, 0.123, 1.528], [1.495, -2.301, 0.226, 0.722, 0.028, 0.613]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_49_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_49_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.934222, -0.219071, 0.281493], [-0.356558, -0.595286, 0.72007], [0.009823, -0.773073, -0.634241]]; the translation vector: [0.331108, 1.989283, 1.551545], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.874, 0.432, 0.675, 0.547, 4.466, 2.765], [2.077, 1.025, 0.96, 0.435, 5.53, 2.428], [-0.302, -2.355, 0.907, 2.255, -0.132, 2.065], [1.309, 2.97, 0.649, 2.105, 0.601, 1.706], [1.394, -2.794, 0.572, 0.929, 0.444, 0.269]]\nB: [[-1.774, -0.336, 1.363, 0.137, 4.327, 2.118], [2.024, 0.331, 0.678, 0.299, 6.038, 2.411], [-0.973, -2.453, 1.173, 2.209, -0.12, 1.889], [0.907, 3.079, 0.375, 1.445, 0.297, 1.527], [1.457, -2.684, 0.632, 0.896, -0.39, 0.289]]\nC: [[-2.285, -0.2, 1.099, -0.248, 4.908, 2.313], [2.096, 0.355, 1.235, 0.363, 5.974, 2.044], [-1.085, -2.082, 0.91, 2.454, 0.239, 1.438], [0.732, 3.157, 0.493, 1.665, 0.182, 1.592], [1.088, -2.467, -0.003, 0.673, 0.233, 0.167]]\nD: [[-1.815, -0.066, 1.137, 0.19, 4.502, 2.283], [1.738, 0.547, 0.94, 0.42, 5.701, 2.065], [-0.777, -2.286, 1.047, 2.091, 0.123, 1.885], [1.023, 3.389, 0.735, 1.699, 0.123, 1.528], [1.495, -2.301, 0.226, 0.722, 0.028, 0.613]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.355, 0.849, 0.484, 0.583, 0.026, -0.166]]\nB: [[-0.954, 0.48, 0.115, 0.22, 0.221, 0.246]]\nC: [[-0.886, 0.23, -0.323, 0.388, 0.524, 0.544]]\nD: [[-0.877, -0.009, -0.082, -0.196, 0.347, 0.57]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_50_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_50_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the trash can in the scene. The camera pose information includes: the rotation matrix: [[-0.986418, -0.051155, 0.156087], [-0.152905, 0.633099, -0.758819], [-0.060001, -0.772379, -0.632322]]; the translation vector: [2.055195, 1.600374, 1.268236], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.355, 0.849, 0.484, 0.583, 0.026, -0.166]]\nB: [[-0.954, 0.48, 0.115, 0.22, 0.221, 0.246]]\nC: [[-0.886, 0.23, -0.323, 0.388, 0.524, 0.544]]\nD: [[-0.877, -0.009, -0.082, -0.196, 0.347, 0.57]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.231, -1.891, 1.007, 2.627, 2.266, 2.036], [-0.307, -0.367, 0.926, 1.513, 1.063, 1.867]]\nB: [[0.718, -2.089, 0.687, 2.342, 2.265, 1.579], [-0.634, -0.345, 1.176, 1.11, 1.202, 2.075]]\nC: [[0.464, -1.663, 1.056, 2.135, 2.464, 2.098], [-0.781, -0.219, 1.071, 1.18, 1.361, 1.522]]\nD: [[-0.112, -2.192, 0.852, 2.525, 1.965, 2.377], [0.128, -0.805, 0.888, 1.396, 1.418, 2.338]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_51_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_51_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the bathroom stall in the scene. The camera pose information includes: the rotation matrix: [[-0.255252, -0.433184, 0.864406], [-0.966562, 0.137073, -0.216725], [-0.024605, -0.890821, -0.453687]]; the translation vector: [1.468232, 3.881342, 1.432686], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.231, -1.891, 1.007, 2.627, 2.266, 2.036], [-0.307, -0.367, 0.926, 1.513, 1.063, 1.867]]\nB: [[0.718, -2.089, 0.687, 2.342, 2.265, 1.579], [-0.634, -0.345, 1.176, 1.11, 1.202, 2.075]]\nC: [[0.464, -1.663, 1.056, 2.135, 2.464, 2.098], [-0.781, -0.219, 1.071, 1.18, 1.361, 1.522]]\nD: [[-0.112, -2.192, 0.852, 2.525, 1.965, 2.377], [0.128, -0.805, 0.888, 1.396, 1.418, 2.338]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.293, -1.301, 1.261, 0.557, 0.076, 0.181]]\nB: [[-2.3, -0.603, 0.539, 0.144, 0.291, 0.744]]\nC: [[-2.289, -1.004, 0.913, 0.094, 0.463, 0.318]]\nD: [[-2.447, -0.778, 0.56, -0.086, 0.586, 0.687]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_52_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_52_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the object in the scene. The camera pose information includes: the rotation matrix: [[0.140295, 0.625342, -0.767636], [0.990108, -0.090149, 0.107516], [-0.001967, -0.775126, -0.631804]]; the translation vector: [3.410891, 3.073526, 1.198756], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.293, -1.301, 1.261, 0.557, 0.076, 0.181]]\nB: [[-2.3, -0.603, 0.539, 0.144, 0.291, 0.744]]\nC: [[-2.289, -1.004, 0.913, 0.094, 0.463, 0.318]]\nD: [[-2.447, -0.778, 0.56, -0.086, 0.586, 0.687]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.354, 0.454, 0.921, -0.335, 0.498, 0.874], [0.185, -0.574, 0.751, 0.09, -0.38, 0.911], [0.667, 0.586, 2.143, 0.441, 0.23, 0.505]]\nB: [[-1.09, 0.059, 1.019, 0.117, 0.263, 0.377], [0.279, -1.061, 0.877, 0.477, 0.116, 0.622], [0.666, 0.093, 1.789, 0.132, 0.373, 0.347]]\nC: [[-1.434, -0.263, 0.532, 0.445, 0.024, 0.383], [-0.034, -1.535, 0.533, 0.655, 0.426, 0.876], [0.704, 0.231, 1.687, 0.279, -0.11, 0.575]]\nD: [[-0.897, -0.345, 1.454, 0.607, 0.705, 0.804], [0.146, -1.337, 0.587, 0.096, 0.382, 0.839], [0.567, -0.339, 1.673, 0.166, 0.534, 0.522]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_53_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_53_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the towel in the scene. The camera pose information includes: the rotation matrix: [[-0.221984, 0.421429, -0.879273], [0.97466, 0.121427, -0.187867], [0.027595, -0.898695, -0.437705]]; the translation vector: [3.155292, 0.483793, 1.35371], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.354, 0.454, 0.921, -0.335, 0.498, 0.874], [0.185, -0.574, 0.751, 0.09, -0.38, 0.911], [0.667, 0.586, 2.143, 0.441, 0.23, 0.505]]\nB: [[-1.09, 0.059, 1.019, 0.117, 0.263, 0.377], [0.279, -1.061, 0.877, 0.477, 0.116, 0.622], [0.666, 0.093, 1.789, 0.132, 0.373, 0.347]]\nC: [[-1.434, -0.263, 0.532, 0.445, 0.024, 0.383], [-0.034, -1.535, 0.533, 0.655, 0.426, 0.876], [0.704, 0.231, 1.687, 0.279, -0.11, 0.575]]\nD: [[-0.897, -0.345, 1.454, 0.607, 0.705, 0.804], [0.146, -1.337, 0.587, 0.096, 0.382, 0.839], [0.567, -0.339, 1.673, 0.166, 0.534, 0.522]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.872, -1.053, 1.714, -0.4, 0.547, -0.388]]\nB: [[-0.641, -0.865, 2.002, 0.06, 0.688, 0.05]]\nC: [[-0.24, -0.538, 2.349, -0.015, 0.604, 0.452]]\nD: [[-0.437, -0.89, 1.743, -0.382, 0.608, -0.394]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_54_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_54_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shower curtain rod in the scene. The camera pose information includes: the rotation matrix: [[0.173351, 0.592298, -0.78685], [0.984858, -0.105806, 0.137329], [-0.001913, -0.798742, -0.601671]]; the translation vector: [3.264189, 1.940071, 1.28435], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.872, -1.053, 1.714, -0.4, 0.547, -0.388]]\nB: [[-0.641, -0.865, 2.002, 0.06, 0.688, 0.05]]\nC: [[-0.24, -0.538, 2.349, -0.015, 0.604, 0.452]]\nD: [[-0.437, -0.89, 1.743, -0.382, 0.608, -0.394]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.74, 0.949, 0.805, 2.053, 0.051, 1.667], [-1.281, -0.065, 0.899, 0.112, 2.004, 1.85], [0.446, -1.039, 0.627, 1.399, 0.094, 1.27], [1.134, -0.792, 0.678, 0.041, 0.539, 1.365], [-1.434, 2.505, 0.652, 0.518, 0.203, 1.213]]\nB: [[1.029, 0.71, 0.916, 2.029, -0.176, 1.869], [-1.683, -0.445, 1.099, -0.259, 2.235, 1.713], [0.848, -1.08, 0.506, 1.798, 0.259, 1.153], [1.268, -0.42, 0.271, 0.287, 0.751, 1.048], [-1.043, 2.825, 0.333, 0.321, -0.246, 1.582]]\nC: [[0.966, 1.169, 0.637, 2.193, -0.193, 1.801], [-0.869, -0.535, 1.386, 0.092, 1.727, 2.164], [0.169, -1.108, 0.224, 1.056, -0.222, 1.304], [0.91, -1.037, 1.17, -0.025, 0.5, 1.639], [-0.958, 2.714, 0.971, 0.285, -0.285, 1.316]]\nD: [[0.741, 1.382, 0.663, 1.864, -0.249, 2.055], [-1.139, 0.311, 1.207, -0.23, 2.288, 2.067], [0.431, -1.158, 0.998, 1.247, 0.194, 1.309], [0.658, -1.111, 1.067, 0.365, 0.642, 0.899], [-1.437, 2.999, 0.509, 0.702, 0.182, 1.021]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_55_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_55_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.660671, 0.426343, -0.617856], [0.749322, -0.423957, 0.508701], [-0.045063, -0.799057, -0.599565]]; the translation vector: [1.739014, 2.260029, 1.323145], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.74, 0.949, 0.805, 2.053, 0.051, 1.667], [-1.281, -0.065, 0.899, 0.112, 2.004, 1.85], [0.446, -1.039, 0.627, 1.399, 0.094, 1.27], [1.134, -0.792, 0.678, 0.041, 0.539, 1.365], [-1.434, 2.505, 0.652, 0.518, 0.203, 1.213]]\nB: [[1.029, 0.71, 0.916, 2.029, -0.176, 1.869], [-1.683, -0.445, 1.099, -0.259, 2.235, 1.713], [0.848, -1.08, 0.506, 1.798, 0.259, 1.153], [1.268, -0.42, 0.271, 0.287, 0.751, 1.048], [-1.043, 2.825, 0.333, 0.321, -0.246, 1.582]]\nC: [[0.966, 1.169, 0.637, 2.193, -0.193, 1.801], [-0.869, -0.535, 1.386, 0.092, 1.727, 2.164], [0.169, -1.108, 0.224, 1.056, -0.222, 1.304], [0.91, -1.037, 1.17, -0.025, 0.5, 1.639], [-0.958, 2.714, 0.971, 0.285, -0.285, 1.316]]\nD: [[0.741, 1.382, 0.663, 1.864, -0.249, 2.055], [-1.139, 0.311, 1.207, -0.23, 2.288, 2.067], [0.431, -1.158, 0.998, 1.247, 0.194, 1.309], [0.658, -1.111, 1.067, 0.365, 0.642, 0.899], [-1.437, 2.999, 0.509, 0.702, 0.182, 1.021]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.289, 1.091, 1.235, 0.312, 0.445, 0.181], [2.614, -0.282, 1.115, 0.396, 0.952, 0.37], [2.915, -1.131, 1.323, 0.416, 1.127, 0.582]]\nB: [[2.865, 1.27, 1.277, 0.662, 1.126, 0.413], [2.634, -0.253, 1.185, 0.725, 0.57, 0.723], [2.702, -0.759, 1.232, 0.075, 0.776, 0.187]]\nC: [[2.596, 1.198, 1.179, 0.402, 0.868, 0.166], [2.565, 0.04, 1.202, 0.364, 0.895, 0.33], [2.601, -1.116, 1.104, 0.457, 0.792, 0.155]]\nD: [[2.413, 1.176, 1.278, 0.589, 0.574, -0.073], [2.181, 0.298, 1.094, 0.783, 1.368, 0.634], [2.395, -1.077, 1.082, 0.598, 1.002, 0.39]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_56_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_56_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the windowsill in the scene. The camera pose information includes: the rotation matrix: [[0.606468, -0.360414, 0.70873], [-0.789578, -0.16805, 0.590192], [-0.093612, -0.91753, -0.386492]]; the translation vector: [2.373669, 6.226582, 1.48631], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.289, 1.091, 1.235, 0.312, 0.445, 0.181], [2.614, -0.282, 1.115, 0.396, 0.952, 0.37], [2.915, -1.131, 1.323, 0.416, 1.127, 0.582]]\nB: [[2.865, 1.27, 1.277, 0.662, 1.126, 0.413], [2.634, -0.253, 1.185, 0.725, 0.57, 0.723], [2.702, -0.759, 1.232, 0.075, 0.776, 0.187]]\nC: [[2.596, 1.198, 1.179, 0.402, 0.868, 0.166], [2.565, 0.04, 1.202, 0.364, 0.895, 0.33], [2.601, -1.116, 1.104, 0.457, 0.792, 0.155]]\nD: [[2.413, 1.176, 1.278, 0.589, 0.574, -0.073], [2.181, 0.298, 1.094, 0.783, 1.368, 0.634], [2.395, -1.077, 1.082, 0.598, 1.002, 0.39]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.001, 2.948, 0.696, 1.051, 0.653, 1.017], [-1.218, 2.566, 0.334, 0.65, 1.143, 0.576], [-1.049, 4.68, 0.409, 0.92, 0.545, 1.203], [-0.848, -3.875, 0.54, 0.615, 0.936, 1.037], [1.041, 2.41, 0.443, 0.89, 0.787, 0.94], [1.624, -1.213, 0.004, 1.481, 1.102, 0.478], [1.287, 1.232, 0.545, 1.204, 1.228, 0.841], [1.818, -0.113, 0.532, 0.874, 0.706, 1.086], [0.092, -5.089, 0.693, 0.961, 0.819, 0.322], [-0.468, -1.491, 0.774, 0.97, 1.2, 1.024]]\nB: [[-0.126, 2.459, 0.086, 0.932, 1.114, 1.123], [-0.966, 2.226, 0.359, 1.537, 0.757, 0.462], [-1.21, 4.954, 0.104, 1.239, 0.624, 0.543], [-0.516, -4.249, 0.544, 1.157, 1.197, 1.269], [1.294, 2.428, 0.861, 1.276, 0.579, 0.451], [1.569, -1.608, 0.36, 0.726, 1.508, 0.636], [0.656, 1.11, -0.004, 0.679, 1.224, 0.752], [0.999, -0.375, 0.707, 0.664, 1.131, 0.788], [0.529, -5.125, 0.2, 0.899, 0.951, 0.927], [-1.058, -1.898, 0.447, 0.976, 1.149, 0.369]]\nC: [[0.237, 2.908, 0.463, 0.898, 0.83, 0.718], [-0.876, 2.53, 0.52, 1.072, 0.924, 0.781], [-1.088, 4.721, 0.492, 0.991, 0.901, 0.767], [-0.583, -3.833, 0.327, 0.92, 0.918, 0.773], [1.47, 1.953, 0.456, 0.894, 0.96, 0.795], [1.829, -1.442, 0.405, 1.045, 1.024, 0.748], [1.035, 0.766, 0.48, 0.857, 0.923, 0.799], [1.416, -0.318, 0.434, 1.021, 0.961, 0.774], [0.375, -5.051, 0.244, 0.861, 0.856, 0.761], [-0.588, -1.854, 0.411, 0.932, 0.952, 0.73]]\nD: [[0.31, 2.967, 0.642, 1.326, 0.654, 0.284], [-1.165, 2.181, 0.237, 1.304, 0.639, 0.395], [-0.721, 4.769, 0.925, 1.219, 0.928, 0.661], [-1.026, -3.416, 0.149, 0.806, 0.901, 0.778], [1.652, 1.761, 0.169, 1.115, 0.472, 1.022], [2.158, -1.036, 0.663, 0.749, 0.724, 1.014], [0.591, 0.853, 0.97, 1.294, 0.724, 0.816], [1.34, 0.03, 0.19, 1.304, 0.703, 0.552], [0.387, -4.975, 0.689, 0.413, 1.29, 0.685], [-0.424, -1.902, 0.121, 1.041, 0.562, 0.86]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_57_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_57_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the armchair in the scene. The camera pose information includes: the rotation matrix: [[0.974605, -0.106498, 0.196986], [-0.223762, -0.428932, 0.875185], [-0.008712, -0.897037, -0.44187]]; the translation vector: [2.006689, 0.552817, 1.711334], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.001, 2.948, 0.696, 1.051, 0.653, 1.017], [-1.218, 2.566, 0.334, 0.65, 1.143, 0.576], [-1.049, 4.68, 0.409, 0.92, 0.545, 1.203], [-0.848, -3.875, 0.54, 0.615, 0.936, 1.037], [1.041, 2.41, 0.443, 0.89, 0.787, 0.94], [1.624, -1.213, 0.004, 1.481, 1.102, 0.478], [1.287, 1.232, 0.545, 1.204, 1.228, 0.841], [1.818, -0.113, 0.532, 0.874, 0.706, 1.086], [0.092, -5.089, 0.693, 0.961, 0.819, 0.322], [-0.468, -1.491, 0.774, 0.97, 1.2, 1.024]]\nB: [[-0.126, 2.459, 0.086, 0.932, 1.114, 1.123], [-0.966, 2.226, 0.359, 1.537, 0.757, 0.462], [-1.21, 4.954, 0.104, 1.239, 0.624, 0.543], [-0.516, -4.249, 0.544, 1.157, 1.197, 1.269], [1.294, 2.428, 0.861, 1.276, 0.579, 0.451], [1.569, -1.608, 0.36, 0.726, 1.508, 0.636], [0.656, 1.11, -0.004, 0.679, 1.224, 0.752], [0.999, -0.375, 0.707, 0.664, 1.131, 0.788], [0.529, -5.125, 0.2, 0.899, 0.951, 0.927], [-1.058, -1.898, 0.447, 0.976, 1.149, 0.369]]\nC: [[0.237, 2.908, 0.463, 0.898, 0.83, 0.718], [-0.876, 2.53, 0.52, 1.072, 0.924, 0.781], [-1.088, 4.721, 0.492, 0.991, 0.901, 0.767], [-0.583, -3.833, 0.327, 0.92, 0.918, 0.773], [1.47, 1.953, 0.456, 0.894, 0.96, 0.795], [1.829, -1.442, 0.405, 1.045, 1.024, 0.748], [1.035, 0.766, 0.48, 0.857, 0.923, 0.799], [1.416, -0.318, 0.434, 1.021, 0.961, 0.774], [0.375, -5.051, 0.244, 0.861, 0.856, 0.761], [-0.588, -1.854, 0.411, 0.932, 0.952, 0.73]]\nD: [[0.31, 2.967, 0.642, 1.326, 0.654, 0.284], [-1.165, 2.181, 0.237, 1.304, 0.639, 0.395], [-0.721, 4.769, 0.925, 1.219, 0.928, 0.661], [-1.026, -3.416, 0.149, 0.806, 0.901, 0.778], [1.652, 1.761, 0.169, 1.115, 0.472, 1.022], [2.158, -1.036, 0.663, 0.749, 0.724, 1.014], [0.591, 0.853, 0.97, 1.294, 0.724, 0.816], [1.34, 0.03, 0.19, 1.304, 0.703, 0.552], [0.387, -4.975, 0.689, 0.413, 1.29, 0.685], [-0.424, -1.902, 0.121, 1.041, 0.562, 0.86]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.945, -0.877, -0.123, 1.546, 1.347, 0.112]]\nB: [[0.857, -1.188, 0.118, 1.684, 1.025, 0.263]]\nC: [[0.995, -0.398, -0.499, 1.213, 1.119, -0.036]]\nD: [[0.539, -0.587, -0.275, 1.351, 1.1, -0.319]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_58_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_58_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[-0.693623, 0.392298, -0.604144], [0.720137, 0.397492, -0.568686], [0.017048, -0.82952, -0.558217]]; the translation vector: [2.706242, 2.586761, 1.453005], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.945, -0.877, -0.123, 1.546, 1.347, 0.112]]\nB: [[0.857, -1.188, 0.118, 1.684, 1.025, 0.263]]\nC: [[0.995, -0.398, -0.499, 1.213, 1.119, -0.036]]\nD: [[0.539, -0.587, -0.275, 1.351, 1.1, -0.319]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.143, 1.575, 0.04, 0.27, 0.312, 0.15], [-0.377, 3.615, 0.346, 0.295, 0.569, 0.473], [-1.45, 0.655, 0.569, 0.701, 0.954, 0.318], [2.369, -0.854, 0.716, 0.861, 0.372, 0.384], [3.488, -0.639, 0.515, 0.374, 0.717, 0.642], [3.31, -2.144, 0.849, 0.423, 1.039, 0.351], [3.753, -1.021, 0.778, 0.709, 0.641, 0.792], [-1.949, 2.854, 0.124, 0.704, 0.146, 0.084]]\nB: [[-1.76, 1.841, 0.53, 0.73, 0.674, 0.544], [-0.679, 3.307, 0.476, 0.669, 0.734, 0.506], [-1.7, 0.568, 0.465, 0.694, 0.62, 0.52], [2.474, -1.195, 0.409, 0.606, 0.509, 0.689], [3.174, -0.614, 0.339, 0.542, 0.599, 0.782], [3.186, -2.158, 0.546, 0.503, 0.633, 0.516], [3.901, -1.236, 0.485, 0.592, 0.545, 0.635], [-1.787, 2.437, 0.508, 0.713, 0.589, 0.468]]\nC: [[-2.143, 1.685, 0.995, 0.615, 0.904, 0.263], [-1.005, 3.628, 0.394, 0.466, 0.405, 0.998], [-2.179, 0.615, 0.333, 0.233, 0.298, 0.889], [2.014, -1.057, 0.599, 0.68, 0.338, 0.974], [2.918, -0.471, 0.1, 0.575, 0.71, 0.376], [3.127, -2.436, 0.498, 0.497, 0.327, 0.902], [3.486, -1.558, 0.63, 0.593, 0.23, 0.81], [-1.407, 2.857, 0.881, 0.499, 1.07, 0.68]]\nD: [[-1.261, 2.229, 0.998, 1.215, 1.048, 0.703], [-1.092, 3.457, -0.005, 0.668, 1.114, 0.663], [-1.477, 0.865, 0.817, 0.301, 0.363, 0.292], [2.93, -1.308, 0.561, 1.073, 0.232, 1.069], [3.634, -0.503, -0.085, 0.796, 0.476, 0.342], [3.396, -2.322, 0.932, 0.945, 0.812, 0.616], [4.075, -1.495, 0.312, 0.703, 0.562, 0.973], [-2.275, 2.728, 0.786, 0.449, 0.77, 0.134]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_59_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_59_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.891251, 0.378307, -0.25011], [0.443048, 0.608538, -0.658323], [-0.096846, -0.697542, -0.709969]]; the translation vector: [4.935522, 3.588868, 1.45033], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.143, 1.575, 0.04, 0.27, 0.312, 0.15], [-0.377, 3.615, 0.346, 0.295, 0.569, 0.473], [-1.45, 0.655, 0.569, 0.701, 0.954, 0.318], [2.369, -0.854, 0.716, 0.861, 0.372, 0.384], [3.488, -0.639, 0.515, 0.374, 0.717, 0.642], [3.31, -2.144, 0.849, 0.423, 1.039, 0.351], [3.753, -1.021, 0.778, 0.709, 0.641, 0.792], [-1.949, 2.854, 0.124, 0.704, 0.146, 0.084]]\nB: [[-1.76, 1.841, 0.53, 0.73, 0.674, 0.544], [-0.679, 3.307, 0.476, 0.669, 0.734, 0.506], [-1.7, 0.568, 0.465, 0.694, 0.62, 0.52], [2.474, -1.195, 0.409, 0.606, 0.509, 0.689], [3.174, -0.614, 0.339, 0.542, 0.599, 0.782], [3.186, -2.158, 0.546, 0.503, 0.633, 0.516], [3.901, -1.236, 0.485, 0.592, 0.545, 0.635], [-1.787, 2.437, 0.508, 0.713, 0.589, 0.468]]\nC: [[-2.143, 1.685, 0.995, 0.615, 0.904, 0.263], [-1.005, 3.628, 0.394, 0.466, 0.405, 0.998], [-2.179, 0.615, 0.333, 0.233, 0.298, 0.889], [2.014, -1.057, 0.599, 0.68, 0.338, 0.974], [2.918, -0.471, 0.1, 0.575, 0.71, 0.376], [3.127, -2.436, 0.498, 0.497, 0.327, 0.902], [3.486, -1.558, 0.63, 0.593, 0.23, 0.81], [-1.407, 2.857, 0.881, 0.499, 1.07, 0.68]]\nD: [[-1.261, 2.229, 0.998, 1.215, 1.048, 0.703], [-1.092, 3.457, -0.005, 0.668, 1.114, 0.663], [-1.477, 0.865, 0.817, 0.301, 0.363, 0.292], [2.93, -1.308, 0.561, 1.073, 0.232, 1.069], [3.634, -0.503, -0.085, 0.796, 0.476, 0.342], [3.396, -2.322, 0.932, 0.945, 0.812, 0.616], [4.075, -1.495, 0.312, 0.703, 0.562, 0.973], [-2.275, 2.728, 0.786, 0.449, 0.77, 0.134]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.205, 1.797, 1.094, 1.154, 0.654, 1.112], [0.658, 1.295, 1.132, 1.277, 0.037, 0.601]]\nB: [[-0.63, 1.531, 1.06, 1.175, 0.329, 0.727], [0.734, 1.578, 0.984, 1.15, 0.266, 0.361]]\nC: [[-0.726, 1.106, 1.434, 1.522, 0.658, 0.308], [1.201, 1.481, 1.246, 0.828, 0.067, 0.371]]\nD: [[-0.719, 1.086, 1.264, 0.78, 0.793, 0.35], [0.868, 1.98, 0.75, 1.049, 0.201, 0.363]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_60_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_60_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the window in the scene. The camera pose information includes: the rotation matrix: [[0.081815, 0.638296, -0.765431], [0.996577, -0.061545, 0.055199], [-0.011875, -0.767327, -0.641146]]; the translation vector: [3.004073, 1.570726, 1.431248], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.205, 1.797, 1.094, 1.154, 0.654, 1.112], [0.658, 1.295, 1.132, 1.277, 0.037, 0.601]]\nB: [[-0.63, 1.531, 1.06, 1.175, 0.329, 0.727], [0.734, 1.578, 0.984, 1.15, 0.266, 0.361]]\nC: [[-0.726, 1.106, 1.434, 1.522, 0.658, 0.308], [1.201, 1.481, 1.246, 0.828, 0.067, 0.371]]\nD: [[-0.719, 1.086, 1.264, 0.78, 0.793, 0.35], [0.868, 1.98, 0.75, 1.049, 0.201, 0.363]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.288, 3.663, 0.885, 1.742, 0.266, 1.508], [-1.19, -3.017, 0.715, 0.179, 0.346, 1.183], [-2.568, -0.991, 0.862, 0.362, 2.842, 1.652], [-2.356, 0.313, 1.087, 0.573, 0.323, 1.151], [-2.078, 0.891, 0.947, 0.102, 1.17, 1.498]]\nB: [[1.699, 3.879, 0.807, 1.466, 0.26, 1.658], [-1.338, -3.016, 0.481, -0.091, 0.125, 1.346], [-2.791, -0.651, 0.722, 0.588, 2.782, 1.444], [-2.133, -0.174, 1.179, 0.831, 0.459, 1.476], [-2.564, 1.303, 0.485, 0.444, 1.6, 1.79]]\nC: [[1.384, 3.837, 1.191, 2.116, 0.64, 1.217], [-1.185, -3.083, 1.042, 0.674, 0.205, 0.788], [-2.424, -0.728, 0.743, -0.005, 2.436, 1.937], [-2.645, -0.046, 0.933, 0.095, 0.125, 1.323], [-2.483, 0.961, 0.887, 0.154, 0.979, 1.595]]\nD: [[1.755, 3.461, 0.788, 1.786, 0.256, 1.208], [-1.596, -3.184, 0.789, 0.372, -0.041, 1.319], [-2.923, -1.052, 1.266, 0.216, 3.322, 1.837], [-2.525, 0.237, 1.346, 0.938, 0.473, 0.759], [-2.569, 1.257, 0.568, 0.003, 1.424, 1.337]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_61_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_61_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.963317, 0.154363, -0.219528], [0.260086, 0.335369, -0.905474], [-0.066149, -0.929355, -0.363214]]; the translation vector: [5.972451, 2.818726, 1.468896], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.288, 3.663, 0.885, 1.742, 0.266, 1.508], [-1.19, -3.017, 0.715, 0.179, 0.346, 1.183], [-2.568, -0.991, 0.862, 0.362, 2.842, 1.652], [-2.356, 0.313, 1.087, 0.573, 0.323, 1.151], [-2.078, 0.891, 0.947, 0.102, 1.17, 1.498]]\nB: [[1.699, 3.879, 0.807, 1.466, 0.26, 1.658], [-1.338, -3.016, 0.481, -0.091, 0.125, 1.346], [-2.791, -0.651, 0.722, 0.588, 2.782, 1.444], [-2.133, -0.174, 1.179, 0.831, 0.459, 1.476], [-2.564, 1.303, 0.485, 0.444, 1.6, 1.79]]\nC: [[1.384, 3.837, 1.191, 2.116, 0.64, 1.217], [-1.185, -3.083, 1.042, 0.674, 0.205, 0.788], [-2.424, -0.728, 0.743, -0.005, 2.436, 1.937], [-2.645, -0.046, 0.933, 0.095, 0.125, 1.323], [-2.483, 0.961, 0.887, 0.154, 0.979, 1.595]]\nD: [[1.755, 3.461, 0.788, 1.786, 0.256, 1.208], [-1.596, -3.184, 0.789, 0.372, -0.041, 1.319], [-2.923, -1.052, 1.266, 0.216, 3.322, 1.837], [-2.525, 0.237, 1.346, 0.938, 0.473, 0.759], [-2.569, 1.257, 0.568, 0.003, 1.424, 1.337]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.195, 2.616, 0.764, 0.381, 0.904, 1.179], [1.178, 2.791, 1.06, 0.523, 1.876, 0.795], [2.137, -1.926, -0.065, 1.185, 1.084, 0.312], [-0.599, -1.923, 0.519, 0.294, 0.992, 1.206]]\nB: [[-1.068, 2.261, 1.292, 0.971, 1.029, 0.824], [1.489, 3.189, 0.594, 0.774, 1.161, 1.156], [1.843, -2.018, 0.792, 0.473, 1.372, 0.616], [-0.424, -2.012, 0.649, 0.681, 1.809, 0.928]]\nC: [[-0.884, 2.735, 0.809, 0.721, 1.274, 0.906], [1.496, 2.941, 0.628, 0.833, 1.571, 0.871], [1.868, -1.949, 0.395, 0.878, 0.892, 0.788], [-0.793, -2.3, 0.359, 0.741, 1.335, 0.757]]\nD: [[-1.225, 2.279, 0.349, 0.295, 0.789, 0.542], [1.131, 2.464, 0.4, 0.503, 1.911, 0.903], [1.57, -1.598, -0.016, 1.245, 1.391, 0.466], [-0.41, -2.691, 0.26, 1.21, 1.681, 0.98]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_62_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_62_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the couch in the scene. The camera pose information includes: the rotation matrix: [[-0.824719, -0.175736, 0.537546], [-0.564369, 0.316962, -0.762249], [-0.036427, -0.932015, -0.360584]]; the translation vector: [4.397487, 4.054199, 1.411764], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.195, 2.616, 0.764, 0.381, 0.904, 1.179], [1.178, 2.791, 1.06, 0.523, 1.876, 0.795], [2.137, -1.926, -0.065, 1.185, 1.084, 0.312], [-0.599, -1.923, 0.519, 0.294, 0.992, 1.206]]\nB: [[-1.068, 2.261, 1.292, 0.971, 1.029, 0.824], [1.489, 3.189, 0.594, 0.774, 1.161, 1.156], [1.843, -2.018, 0.792, 0.473, 1.372, 0.616], [-0.424, -2.012, 0.649, 0.681, 1.809, 0.928]]\nC: [[-0.884, 2.735, 0.809, 0.721, 1.274, 0.906], [1.496, 2.941, 0.628, 0.833, 1.571, 0.871], [1.868, -1.949, 0.395, 0.878, 0.892, 0.788], [-0.793, -2.3, 0.359, 0.741, 1.335, 0.757]]\nD: [[-1.225, 2.279, 0.349, 0.295, 0.789, 0.542], [1.131, 2.464, 0.4, 0.503, 1.911, 0.903], [1.57, -1.598, -0.016, 1.245, 1.391, 0.466], [-0.41, -2.691, 0.26, 1.21, 1.681, 0.98]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.115, -1.562, 0.911, 0.99, 0.176, 0.93], [0.903, -1.409, 1.128, 0.969, 0.316, 0.989]]\nB: [[0.419, -1.51, 1.351, 0.838, 0.104, 1.371], [0.797, -1.27, 0.95, 0.885, 0.56, 0.709]]\nC: [[-0.056, -1.325, 0.584, 1.397, 0.105, 0.436], [0.94, -1.633, 1.178, 0.648, 0.434, 1.044]]\nD: [[0.609, -1.088, 0.429, 1.463, 0.186, 0.54], [0.51, -1.323, 0.699, 1.115, 0.814, 1.432]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_63_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_63_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the window in the scene. The camera pose information includes: the rotation matrix: [[0.993805, -0.057016, 0.095394], [-0.110597, -0.423109, 0.899304], [-0.010913, -0.904283, -0.426794]]; the translation vector: [3.282054, 2.568905, 1.512321], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.115, -1.562, 0.911, 0.99, 0.176, 0.93], [0.903, -1.409, 1.128, 0.969, 0.316, 0.989]]\nB: [[0.419, -1.51, 1.351, 0.838, 0.104, 1.371], [0.797, -1.27, 0.95, 0.885, 0.56, 0.709]]\nC: [[-0.056, -1.325, 0.584, 1.397, 0.105, 0.436], [0.94, -1.633, 1.178, 0.648, 0.434, 1.044]]\nD: [[0.609, -1.088, 0.429, 1.463, 0.186, 0.54], [0.51, -1.323, 0.699, 1.115, 0.814, 1.432]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.753, 0.465, 1.403, 0.46, 4.996, 2.959], [-1.738, -1.218, 1.272, 1.062, 1.528, 2.501], [-0.405, 2.797, 1.433, 4.292, 0.332, 2.875], [-2.525, 2.379, 1.355, 0.074, 0.839, 1.664], [-2.109, 0.693, 1.971, 0.227, 2.364, 1.533], [0.282, -2.054, 1.197, 3.118, 0.28, 2.272], [0.151, -2.857, 1.262, 0.294, 1.776, 2.355]]\nB: [[1.644, 0.604, 1.06, 0.766, 5.332, 3.344], [-2.097, -1.683, 0.861, 1.264, 1.832, 2.762], [-0.249, 2.988, 1.171, 4.734, 0.777, 3.234], [-2.915, 2.214, 1.5, 0.285, 1.098, 1.997], [-1.7, 0.54, 1.692, 0.479, 2.794, 1.178], [0.193, -1.942, 1.679, 3.173, -0.143, 2.182], [0.278, -3.151, 1.749, -0.197, 1.898, 2.594]]\nC: [[1.329, 0.268, 1.849, 0.784, 4.719, 2.961], [-2.126, -1.458, 1.073, 0.788, 1.484, 2.789], [0.005, 2.714, 1.367, 3.948, 0.242, 2.522], [-2.545, 2.463, 1.604, 0.21, 1.144, 1.521], [-1.811, 0.332, 2.299, -0.123, 1.943, 1.085], [0.387, -2.373, 0.727, 2.861, -0.215, 2.059], [0.369, -3.057, 1.007, 0.316, 1.439, 1.965]]\nD: [[2.224, 0.601, 1.81, 0.297, 5.34, 2.544], [-1.441, -1.038, 1.648, 1.209, 1.768, 2.642], [-0.455, 2.785, 1.909, 4.119, 0.083, 3.179], [-2.32, 2.048, 1.417, -0.178, 0.398, 1.998], [-1.992, 0.619, 1.973, 0.251, 2.119, 1.3], [0.593, -1.72, 1.138, 2.733, 0.423, 2.378], [0.595, -2.424, 1.148, 0.122, 2.12, 2.512]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_64_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_64_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.994136, 0.036629, -0.101745], [0.107123, -0.462198, 0.880283], [-0.014782, -0.88602, -0.463411]]; the translation vector: [3.8191, 1.340951, 1.354002], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.753, 0.465, 1.403, 0.46, 4.996, 2.959], [-1.738, -1.218, 1.272, 1.062, 1.528, 2.501], [-0.405, 2.797, 1.433, 4.292, 0.332, 2.875], [-2.525, 2.379, 1.355, 0.074, 0.839, 1.664], [-2.109, 0.693, 1.971, 0.227, 2.364, 1.533], [0.282, -2.054, 1.197, 3.118, 0.28, 2.272], [0.151, -2.857, 1.262, 0.294, 1.776, 2.355]]\nB: [[1.644, 0.604, 1.06, 0.766, 5.332, 3.344], [-2.097, -1.683, 0.861, 1.264, 1.832, 2.762], [-0.249, 2.988, 1.171, 4.734, 0.777, 3.234], [-2.915, 2.214, 1.5, 0.285, 1.098, 1.997], [-1.7, 0.54, 1.692, 0.479, 2.794, 1.178], [0.193, -1.942, 1.679, 3.173, -0.143, 2.182], [0.278, -3.151, 1.749, -0.197, 1.898, 2.594]]\nC: [[1.329, 0.268, 1.849, 0.784, 4.719, 2.961], [-2.126, -1.458, 1.073, 0.788, 1.484, 2.789], [0.005, 2.714, 1.367, 3.948, 0.242, 2.522], [-2.545, 2.463, 1.604, 0.21, 1.144, 1.521], [-1.811, 0.332, 2.299, -0.123, 1.943, 1.085], [0.387, -2.373, 0.727, 2.861, -0.215, 2.059], [0.369, -3.057, 1.007, 0.316, 1.439, 1.965]]\nD: [[2.224, 0.601, 1.81, 0.297, 5.34, 2.544], [-1.441, -1.038, 1.648, 1.209, 1.768, 2.642], [-0.455, 2.785, 1.909, 4.119, 0.083, 3.179], [-2.32, 2.048, 1.417, -0.178, 0.398, 1.998], [-1.992, 0.619, 1.973, 0.251, 2.119, 1.3], [0.593, -1.72, 1.138, 2.733, 0.423, 2.378], [0.595, -2.424, 1.148, 0.122, 2.12, 2.512]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.229, -0.625, 1.789, 0.384, 0.619, 0.577], [-0.619, -2.375, 0.661, 0.567, 0.451, 0.111], [1.272, -2.43, 0.132, 0.447, 0.727, 0.3], [1.644, -2.684, 0.788, 0.577, 0.322, 0.308], [-0.576, -2.651, 0.078, 0.434, 0.523, 0.246], [0.194, -2.629, 0.081, 0.411, 0.494, 0.232], [0.495, -2.465, 0.021, 0.42, 0.459, 0.089], [-0.19, -2.648, 0.019, 0.384, 0.512, 0.116], [0.653, -2.701, 0.729, 0.282, 0.339, 0.17], [0.952, -2.696, 0.744, 0.349, 0.322, 0.22], [1.255, -2.766, 0.79, 0.368, 0.486, 0.32], [0.1, -2.746, 0.715, 0.314, 0.34, 0.156], [0.399, -2.748, 0.688, 0.296, 0.348, 0.103], [-0.127, -2.741, 0.687, 0.255, 0.368, 0.104], [-0.41, -2.745, 0.703, 0.341, 0.369, 0.132], [-1.782, -2.695, 0.512, 0.572, 0.485, 0.241], [-1.365, -2.686, 0.5, 0.492, 0.488, 0.277], [-1.027, -2.616, 0.39, 0.417, 0.378, 0.292], [-2.221, -0.682, 1.308, 0.347, 0.518, 0.497]]\nB: [[-2.177, -0.167, 1.791, 0.429, 0.305, 0.461], [-0.533, -2.428, 0.376, 0.625, 0.83, -0.181], [0.833, -2.571, -0.115, 0.273, 1.148, 0.126], [1.611, -2.253, 0.787, 0.359, 0.551, -0.134], [-1.058, -2.229, -0.315, 0.638, 0.268, -0.067], [0.333, -2.804, -0.071, 0.337, 0.161, 0.002], [0.886, -2.763, 0.464, 0.54, 0.824, 0.171], [0.083, -2.871, 0.059, 0.444, 0.352, 0.054], [0.665, -2.763, 0.558, 0.057, 0.308, 0.039], [0.563, -2.607, 1.101, 0.044, -0.169, 0.664], [1.085, -2.593, 0.464, 0.42, 0.951, 0.013], [-0.365, -2.365, 0.619, 0.59, 0.077, 0.369], [0.543, -2.864, 0.581, 0.554, 0.644, -0.05], [0.36, -3.102, 0.746, 0.301, -0.13, -0.221], [-0.22, -2.771, 1.165, 0.154, 0.295, 0.195], [-1.434, -2.444, 0.547, 0.734, 0.246, -0.108], [-0.997, -2.269, 0.094, 0.441, 0.845, 0.283], [-1.137, -2.213, 0.312, 0.148, 0.309, 0.772], [-2.498, -0.603, 1.369, 0.752, 0.555, 0.615]]\nC: [[-2.33, -1.056, 1.723, -0.065, 0.432, 0.415], [-0.968, -1.986, 0.569, 0.909, 0.497, 0.486], [1.32, -2.346, -0.114, 0.554, 0.588, 0.715], [1.949, -3.024, 0.857, 1.07, 0.018, 0.558], [-0.719, -2.255, 0.515, 0.899, 0.995, 0.643], [0.265, -2.28, -0.308, 0.384, 0.12, 0.468], [0.651, -2.056, -0.288, 0.45, 0.167, 0.402], [-0.058, -2.555, -0.352, 0.064, 0.242, 0.36], [0.516, -2.537, 1.033, 0.148, 0.192, 0.352], [0.983, -2.457, 0.904, -0.004, -0.102, -0.046], [1.601, -2.407, 0.354, 0.85, 0.773, 0.225], [0.09, -3.129, 0.278, 0.778, 0.065, 0.089], [0.498, -3.096, 0.49, 0.127, 0.025, 0.421], [0.282, -2.893, 0.585, 0.538, -0.078, 0.192], [-0.775, -2.875, 0.541, 0.822, 0.042, 0.614], [-1.444, -2.829, 0.956, 0.56, 0.015, 0.186], [-1.857, -2.941, 0.896, 0.404, 0.313, 0.437], [-1.23, -2.427, -0.01, 0.121, 0.029, 0.052], [-2.105, -0.861, 1.621, 0.843, 0.939, 0.137]]\nD: [[-2.187, -0.629, 2.131, 0.702, 0.488, 0.299], [-1.084, -2.454, 0.389, 0.263, 0.376, -0.0], [0.958, -2.794, -0.355, 0.189, 0.618, 0.078], [2.015, -2.977, 0.616, 0.785, -0.119, 0.807], [-0.732, -2.52, -0.405, 0.133, 0.556, -0.136], [0.099, -2.242, 0.21, 0.448, 0.703, 0.555], [0.296, -2.758, -0.175, 0.146, 0.559, 0.119], [0.107, -2.903, 0.259, 0.508, 0.683, 0.189], [0.807, -2.213, 0.988, -0.022, 0.827, 0.39], [1.137, -2.436, 0.849, 0.615, -0.156, -0.078], [1.291, -2.816, 0.462, 0.333, 0.002, 0.188], [-0.057, -2.486, 0.271, 0.707, 0.496, -0.343], [0.312, -2.462, 0.382, 0.486, 0.393, -0.299], [-0.367, -3.213, 1.027, 0.397, 0.32, -0.33], [-0.12, -2.591, 0.295, 0.767, -0.13, -0.295], [-1.934, -2.605, 0.653, 0.958, 0.354, 0.257], [-1.101, -2.538, 0.202, 0.148, 0.769, 0.141], [-0.928, -2.714, 0.387, 0.917, 0.787, 0.443], [-1.94, -0.799, 1.262, 0.381, 0.02, 0.723]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_65_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_65_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the box in the scene. The camera pose information includes: the rotation matrix: [[0.983299, 0.047874, -0.175588], [0.180439, -0.382417, 0.9062], [-0.023764, -0.922749, -0.384668]]; the translation vector: [2.208684, 3.483128, 1.468268], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.229, -0.625, 1.789, 0.384, 0.619, 0.577], [-0.619, -2.375, 0.661, 0.567, 0.451, 0.111], [1.272, -2.43, 0.132, 0.447, 0.727, 0.3], [1.644, -2.684, 0.788, 0.577, 0.322, 0.308], [-0.576, -2.651, 0.078, 0.434, 0.523, 0.246], [0.194, -2.629, 0.081, 0.411, 0.494, 0.232], [0.495, -2.465, 0.021, 0.42, 0.459, 0.089], [-0.19, -2.648, 0.019, 0.384, 0.512, 0.116], [0.653, -2.701, 0.729, 0.282, 0.339, 0.17], [0.952, -2.696, 0.744, 0.349, 0.322, 0.22], [1.255, -2.766, 0.79, 0.368, 0.486, 0.32], [0.1, -2.746, 0.715, 0.314, 0.34, 0.156], [0.399, -2.748, 0.688, 0.296, 0.348, 0.103], [-0.127, -2.741, 0.687, 0.255, 0.368, 0.104], [-0.41, -2.745, 0.703, 0.341, 0.369, 0.132], [-1.782, -2.695, 0.512, 0.572, 0.485, 0.241], [-1.365, -2.686, 0.5, 0.492, 0.488, 0.277], [-1.027, -2.616, 0.39, 0.417, 0.378, 0.292], [-2.221, -0.682, 1.308, 0.347, 0.518, 0.497]]\nB: [[-2.177, -0.167, 1.791, 0.429, 0.305, 0.461], [-0.533, -2.428, 0.376, 0.625, 0.83, -0.181], [0.833, -2.571, -0.115, 0.273, 1.148, 0.126], [1.611, -2.253, 0.787, 0.359, 0.551, -0.134], [-1.058, -2.229, -0.315, 0.638, 0.268, -0.067], [0.333, -2.804, -0.071, 0.337, 0.161, 0.002], [0.886, -2.763, 0.464, 0.54, 0.824, 0.171], [0.083, -2.871, 0.059, 0.444, 0.352, 0.054], [0.665, -2.763, 0.558, 0.057, 0.308, 0.039], [0.563, -2.607, 1.101, 0.044, -0.169, 0.664], [1.085, -2.593, 0.464, 0.42, 0.951, 0.013], [-0.365, -2.365, 0.619, 0.59, 0.077, 0.369], [0.543, -2.864, 0.581, 0.554, 0.644, -0.05], [0.36, -3.102, 0.746, 0.301, -0.13, -0.221], [-0.22, -2.771, 1.165, 0.154, 0.295, 0.195], [-1.434, -2.444, 0.547, 0.734, 0.246, -0.108], [-0.997, -2.269, 0.094, 0.441, 0.845, 0.283], [-1.137, -2.213, 0.312, 0.148, 0.309, 0.772], [-2.498, -0.603, 1.369, 0.752, 0.555, 0.615]]\nC: [[-2.33, -1.056, 1.723, -0.065, 0.432, 0.415], [-0.968, -1.986, 0.569, 0.909, 0.497, 0.486], [1.32, -2.346, -0.114, 0.554, 0.588, 0.715], [1.949, -3.024, 0.857, 1.07, 0.018, 0.558], [-0.719, -2.255, 0.515, 0.899, 0.995, 0.643], [0.265, -2.28, -0.308, 0.384, 0.12, 0.468], [0.651, -2.056, -0.288, 0.45, 0.167, 0.402], [-0.058, -2.555, -0.352, 0.064, 0.242, 0.36], [0.516, -2.537, 1.033, 0.148, 0.192, 0.352], [0.983, -2.457, 0.904, -0.004, -0.102, -0.046], [1.601, -2.407, 0.354, 0.85, 0.773, 0.225], [0.09, -3.129, 0.278, 0.778, 0.065, 0.089], [0.498, -3.096, 0.49, 0.127, 0.025, 0.421], [0.282, -2.893, 0.585, 0.538, -0.078, 0.192], [-0.775, -2.875, 0.541, 0.822, 0.042, 0.614], [-1.444, -2.829, 0.956, 0.56, 0.015, 0.186], [-1.857, -2.941, 0.896, 0.404, 0.313, 0.437], [-1.23, -2.427, -0.01, 0.121, 0.029, 0.052], [-2.105, -0.861, 1.621, 0.843, 0.939, 0.137]]\nD: [[-2.187, -0.629, 2.131, 0.702, 0.488, 0.299], [-1.084, -2.454, 0.389, 0.263, 0.376, -0.0], [0.958, -2.794, -0.355, 0.189, 0.618, 0.078], [2.015, -2.977, 0.616, 0.785, -0.119, 0.807], [-0.732, -2.52, -0.405, 0.133, 0.556, -0.136], [0.099, -2.242, 0.21, 0.448, 0.703, 0.555], [0.296, -2.758, -0.175, 0.146, 0.559, 0.119], [0.107, -2.903, 0.259, 0.508, 0.683, 0.189], [0.807, -2.213, 0.988, -0.022, 0.827, 0.39], [1.137, -2.436, 0.849, 0.615, -0.156, -0.078], [1.291, -2.816, 0.462, 0.333, 0.002, 0.188], [-0.057, -2.486, 0.271, 0.707, 0.496, -0.343], [0.312, -2.462, 0.382, 0.486, 0.393, -0.299], [-0.367, -3.213, 1.027, 0.397, 0.32, -0.33], [-0.12, -2.591, 0.295, 0.767, -0.13, -0.295], [-1.934, -2.605, 0.653, 0.958, 0.354, 0.257], [-1.101, -2.538, 0.202, 0.148, 0.769, 0.141], [-0.928, -2.714, 0.387, 0.917, 0.787, 0.443], [-1.94, -0.799, 1.262, 0.381, 0.02, 0.723]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.897, 0.522, 0.459, 3.876, 6.999, 0.483]]\nB: [[-1.103, 0.265, -0.086, 3.353, 6.924, 0.299]]\nC: [[-1.473, 0.764, 0.085, 3.499, 6.843, 0.726]]\nD: [[-1.038, 0.389, 0.109, 3.778, 6.648, 0.286]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_66_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_66_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[0.643628, -0.362528, 0.674031], [-0.765241, -0.290748, 0.574345], [-0.012243, -0.88546, -0.464555]]; the translation vector: [2.632762, 2.243425, 1.452714], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.897, 0.522, 0.459, 3.876, 6.999, 0.483]]\nB: [[-1.103, 0.265, -0.086, 3.353, 6.924, 0.299]]\nC: [[-1.473, 0.764, 0.085, 3.499, 6.843, 0.726]]\nD: [[-1.038, 0.389, 0.109, 3.778, 6.648, 0.286]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.591, 1.483, 1.058, 1.607, 0.601, 2.492], [-2.097, -1.408, 0.835, 0.5, 0.336, 2.389]]\nB: [[0.134, 2.153, 0.957, 1.663, 0.233, 2.443], [-2.254, -1.53, 0.609, 0.554, 0.988, 1.584]]\nC: [[-0.108, 1.926, 1.025, 1.195, 0.255, 2.095], [-1.989, -1.419, 0.985, 0.159, 0.822, 1.991]]\nD: [[0.21, 1.976, 1.369, 1.051, 0.491, 1.606], [-2.131, -1.497, 0.9, 0.432, 0.96, 1.514]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_67_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_67_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[-0.925351, 0.122106, -0.358909], [0.376741, 0.190476, -0.906524], [-0.042329, -0.974068, -0.222259]]; the translation vector: [4.735593, 2.732706, 1.21643], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.591, 1.483, 1.058, 1.607, 0.601, 2.492], [-2.097, -1.408, 0.835, 0.5, 0.336, 2.389]]\nB: [[0.134, 2.153, 0.957, 1.663, 0.233, 2.443], [-2.254, -1.53, 0.609, 0.554, 0.988, 1.584]]\nC: [[-0.108, 1.926, 1.025, 1.195, 0.255, 2.095], [-1.989, -1.419, 0.985, 0.159, 0.822, 1.991]]\nD: [[0.21, 1.976, 1.369, 1.051, 0.491, 1.606], [-2.131, -1.497, 0.9, 0.432, 0.96, 1.514]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.347, 0.112, 0.25, 1.296, 0.747, 0.377], [-0.83, 0.341, 0.317, 1.198, 1.401, 0.615], [-0.794, -1.292, 0.479, 1.485, 1.035, 1.271], [0.127, 1.653, 0.718, 0.98, 0.518, 0.445]]\nB: [[1.529, 0.501, 0.301, 0.944, 1.585, 0.652], [-1.117, 0.064, 0.625, 0.722, 0.832, 1.296], [-1.406, -0.586, 0.491, 0.928, 1.591, 0.802], [0.516, 1.562, 0.345, 0.823, 1.417, 0.468]]\nC: [[1.382, -0.298, 0.162, 0.586, 1.271, 1.153], [-1.729, -0.043, 0.911, 1.507, 1.118, 1.281], [-0.888, -0.525, -0.057, 1.572, 1.192, 0.468], [-0.205, 1.524, 0.606, 0.689, 0.914, 0.382]]\nD: [[1.322, 0.194, 0.453, 1.016, 1.117, 0.863], [-1.253, 0.172, 0.421, 1.029, 1.045, 0.876], [-1.049, -0.979, 0.44, 1.12, 1.131, 0.861], [0.221, 1.294, 0.424, 0.876, 0.92, 0.832]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_68_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_68_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the armchair in the scene. The camera pose information includes: the rotation matrix: [[0.748873, -0.374013, 0.547087], [-0.662404, -0.447673, 0.600675], [0.020256, -0.812221, -0.582998]]; the translation vector: [3.709567, 4.406117, 1.261793], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.347, 0.112, 0.25, 1.296, 0.747, 0.377], [-0.83, 0.341, 0.317, 1.198, 1.401, 0.615], [-0.794, -1.292, 0.479, 1.485, 1.035, 1.271], [0.127, 1.653, 0.718, 0.98, 0.518, 0.445]]\nB: [[1.529, 0.501, 0.301, 0.944, 1.585, 0.652], [-1.117, 0.064, 0.625, 0.722, 0.832, 1.296], [-1.406, -0.586, 0.491, 0.928, 1.591, 0.802], [0.516, 1.562, 0.345, 0.823, 1.417, 0.468]]\nC: [[1.382, -0.298, 0.162, 0.586, 1.271, 1.153], [-1.729, -0.043, 0.911, 1.507, 1.118, 1.281], [-0.888, -0.525, -0.057, 1.572, 1.192, 0.468], [-0.205, 1.524, 0.606, 0.689, 0.914, 0.382]]\nD: [[1.322, 0.194, 0.453, 1.016, 1.117, 0.863], [-1.253, 0.172, 0.421, 1.029, 1.045, 0.876], [-1.049, -0.979, 0.44, 1.12, 1.131, 0.861], [0.221, 1.294, 0.424, 0.876, 0.92, 0.832]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.579, -0.488, 0.759, 0.356, 0.729, 0.206], [-1.432, 0.41, 0.224, 0.816, -0.16, 0.054], [-1.128, 1.211, 0.876, 0.072, 0.472, -0.431], [-0.056, 1.335, 1.059, 0.219, -0.158, 0.294], [0.39, 0.373, 0.895, 0.659, 0.538, 0.377], [-1.237, 2.65, 0.314, 0.655, 0.335, -0.177]]\nB: [[-1.898, -0.166, 1.244, 0.693, 0.01, 0.135], [-2.054, 0.428, 0.961, 0.919, 0.356, 0.407], [-1.294, 1.065, 0.511, 0.811, -0.08, -0.323], [0.085, 0.558, 1.04, 0.703, -0.22, -0.384], [1.147, 0.956, 0.305, 0.157, 0.461, -0.367], [-1.796, 2.739, 0.408, 0.015, 0.305, -0.245]]\nC: [[-1.472, -0.634, 0.769, 0.41, 0.312, 0.075], [-1.766, 0.861, 0.684, 0.449, 0.16, 0.051], [-0.868, 0.879, 0.668, 0.414, 0.211, 0.046], [-0.148, 0.874, 0.644, 0.427, 0.151, 0.056], [0.744, 0.838, 0.607, 0.528, 0.174, 0.072], [-1.369, 2.612, 0.558, 0.426, 0.186, 0.029]]\nD: [[-1.326, -0.492, 0.759, 0.773, 0.113, -0.399], [-1.742, 0.884, 0.249, 0.825, 0.051, -0.219], [-0.59, 0.654, 0.814, 0.491, -0.041, -0.171], [-0.618, 1.322, 0.366, 0.807, 0.377, 0.225], [1.165, 1.152, 0.365, 0.032, 0.059, 0.012], [-1.206, 2.669, 0.552, 0.305, 0.052, 0.19]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_69_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_69_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the keyboard in the scene. The camera pose information includes: the rotation matrix: [[0.053762, 0.423971, -0.904079], [0.99709, -0.071809, 0.025618], [-0.05406, -0.902825, -0.426597]]; the translation vector: [3.696534, 7.381392, 1.65485], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.579, -0.488, 0.759, 0.356, 0.729, 0.206], [-1.432, 0.41, 0.224, 0.816, -0.16, 0.054], [-1.128, 1.211, 0.876, 0.072, 0.472, -0.431], [-0.056, 1.335, 1.059, 0.219, -0.158, 0.294], [0.39, 0.373, 0.895, 0.659, 0.538, 0.377], [-1.237, 2.65, 0.314, 0.655, 0.335, -0.177]]\nB: [[-1.898, -0.166, 1.244, 0.693, 0.01, 0.135], [-2.054, 0.428, 0.961, 0.919, 0.356, 0.407], [-1.294, 1.065, 0.511, 0.811, -0.08, -0.323], [0.085, 0.558, 1.04, 0.703, -0.22, -0.384], [1.147, 0.956, 0.305, 0.157, 0.461, -0.367], [-1.796, 2.739, 0.408, 0.015, 0.305, -0.245]]\nC: [[-1.472, -0.634, 0.769, 0.41, 0.312, 0.075], [-1.766, 0.861, 0.684, 0.449, 0.16, 0.051], [-0.868, 0.879, 0.668, 0.414, 0.211, 0.046], [-0.148, 0.874, 0.644, 0.427, 0.151, 0.056], [0.744, 0.838, 0.607, 0.528, 0.174, 0.072], [-1.369, 2.612, 0.558, 0.426, 0.186, 0.029]]\nD: [[-1.326, -0.492, 0.759, 0.773, 0.113, -0.399], [-1.742, 0.884, 0.249, 0.825, 0.051, -0.219], [-0.59, 0.654, 0.814, 0.491, -0.041, -0.171], [-0.618, 1.322, 0.366, 0.807, 0.377, 0.225], [1.165, 1.152, 0.365, 0.032, 0.059, 0.012], [-1.206, 2.669, 0.552, 0.305, 0.052, 0.19]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.062, 0.255, 0.974, 0.478, 0.305, 1.9]]\nB: [[0.289, 0.114, 0.997, 0.421, 0.269, 2.332]]\nC: [[-0.529, -0.167, 1.248, 0.711, 0.631, 1.869]]\nD: [[-0.117, 0.693, 1.129, 0.484, 0.656, 2.156]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_70_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_70_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shower curtain in the scene. The camera pose information includes: the rotation matrix: [[-0.95695, -0.100486, 0.272304], [-0.288986, 0.24231, -0.92616], [0.027085, -0.964981, -0.260918]]; the translation vector: [1.227478, 4.879099, 1.55452], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.062, 0.255, 0.974, 0.478, 0.305, 1.9]]\nB: [[0.289, 0.114, 0.997, 0.421, 0.269, 2.332]]\nC: [[-0.529, -0.167, 1.248, 0.711, 0.631, 1.869]]\nD: [[-0.117, 0.693, 1.129, 0.484, 0.656, 2.156]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.525, -2.231, 0.892, 0.349, 0.481, 0.091]]\nB: [[1.636, -2.317, 0.937, 0.292, 0.774, -0.26]]\nC: [[1.735, -2.218, 1.132, -0.039, 0.012, 0.228]]\nD: [[1.335, -2.53, 1.027, 0.634, 0.978, -0.278]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_71_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_71_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the book in the scene. The camera pose information includes: the rotation matrix: [[-0.863619, -0.252896, 0.436126], [-0.502889, 0.371124, -0.780621], [0.03556, -0.893482, -0.447688]]; the translation vector: [2.007098, 3.82416, 1.536992], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.525, -2.231, 0.892, 0.349, 0.481, 0.091]]\nB: [[1.636, -2.317, 0.937, 0.292, 0.774, -0.26]]\nC: [[1.735, -2.218, 1.132, -0.039, 0.012, 0.228]]\nD: [[1.335, -2.53, 1.027, 0.634, 0.978, -0.278]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-3.713, -2.322, 1.093, 0.437, 3.072, 2.045], [-1.081, -3.837, 1.495, 4.867, 0.501, 2.558], [2.258, -2.553, 1.174, 1.342, 1.564, 2.568], [3.141, 1.147, 1.716, 0.254, 5.221, 3.492], [1.44, -2.565, 1.73, 0.493, 2.338, 2.511], [1.459, -1.994, 0.755, 0.967, 0.884, 1.183], [1.27, 3.225, 1.429, 3.362, 0.06, 2.461], [2.687, -1.112, 0.928, 2.314, 0.606, 3.137], [3.573, 2.12, 0.945, -0.323, 1.165, 0.653]]\nB: [[-3.835, -1.629, 1.168, -0.265, 2.747, 2.333], [-1.318, -2.989, 1.688, 4.412, 0.48, 2.388], [2.689, -2.933, 1.545, 1.676, 2.18, 2.201], [3.228, 1.403, 1.452, 0.635, 4.562, 3.257], [1.389, -2.608, 0.976, 1.337, 2.222, 2.449], [1.683, -1.57, 0.448, 0.488, 1.125, 1.219], [1.286, 3.78, 1.634, 2.717, 0.735, 2.673], [2.077, -1.004, 0.831, 1.778, 0.571, 2.523], [3.367, 1.994, 0.998, 0.165, 1.01, 0.878]]\nC: [[-3.518, -1.854, 1.546, 0.215, 3.24, 2.228], [-1.249, -3.369, 1.199, 4.514, 0.422, 2.472], [2.581, -2.461, 1.261, 1.576, 1.946, 2.535], [3.098, 1.012, 1.522, 0.435, 4.946, 2.999], [1.343, -2.44, 1.234, 0.869, 1.985, 2.527], [1.357, -2.033, 0.708, 0.777, 1.087, 1.434], [1.727, 3.433, 1.218, 3.174, 0.459, 2.415], [2.388, -1.448, 1.321, 1.857, 0.151, 2.689], [3.207, 2.39, 1.139, 0.116, 1.457, 0.447]]\nD: [[-3.315, -1.725, 1.076, -0.072, 3.369, 2.316], [-1.509, -2.948, 1.263, 4.588, 0.454, 2.009], [2.958, -2.942, 0.867, 1.911, 2.392, 2.127], [3.566, 0.671, 1.618, 0.253, 5.112, 3.1], [1.816, -2.011, 1.094, 0.402, 1.679, 2.148], [0.957, -1.668, 0.579, 1.105, 0.683, 1.586], [1.676, 3.716, 1.075, 3.204, 0.902, 2.406], [2.655, -1.717, 0.827, 1.883, 0.155, 2.358], [3.511, 2.562, 1.175, -0.348, 1.486, 0.553]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_72_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_72_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.831143, 0.312948, -0.459636], [0.555586, 0.43327, -0.709649], [-0.022937, -0.845187, -0.533978]]; the translation vector: [2.360292, 3.05803, 1.315354], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-3.713, -2.322, 1.093, 0.437, 3.072, 2.045], [-1.081, -3.837, 1.495, 4.867, 0.501, 2.558], [2.258, -2.553, 1.174, 1.342, 1.564, 2.568], [3.141, 1.147, 1.716, 0.254, 5.221, 3.492], [1.44, -2.565, 1.73, 0.493, 2.338, 2.511], [1.459, -1.994, 0.755, 0.967, 0.884, 1.183], [1.27, 3.225, 1.429, 3.362, 0.06, 2.461], [2.687, -1.112, 0.928, 2.314, 0.606, 3.137], [3.573, 2.12, 0.945, -0.323, 1.165, 0.653]]\nB: [[-3.835, -1.629, 1.168, -0.265, 2.747, 2.333], [-1.318, -2.989, 1.688, 4.412, 0.48, 2.388], [2.689, -2.933, 1.545, 1.676, 2.18, 2.201], [3.228, 1.403, 1.452, 0.635, 4.562, 3.257], [1.389, -2.608, 0.976, 1.337, 2.222, 2.449], [1.683, -1.57, 0.448, 0.488, 1.125, 1.219], [1.286, 3.78, 1.634, 2.717, 0.735, 2.673], [2.077, -1.004, 0.831, 1.778, 0.571, 2.523], [3.367, 1.994, 0.998, 0.165, 1.01, 0.878]]\nC: [[-3.518, -1.854, 1.546, 0.215, 3.24, 2.228], [-1.249, -3.369, 1.199, 4.514, 0.422, 2.472], [2.581, -2.461, 1.261, 1.576, 1.946, 2.535], [3.098, 1.012, 1.522, 0.435, 4.946, 2.999], [1.343, -2.44, 1.234, 0.869, 1.985, 2.527], [1.357, -2.033, 0.708, 0.777, 1.087, 1.434], [1.727, 3.433, 1.218, 3.174, 0.459, 2.415], [2.388, -1.448, 1.321, 1.857, 0.151, 2.689], [3.207, 2.39, 1.139, 0.116, 1.457, 0.447]]\nD: [[-3.315, -1.725, 1.076, -0.072, 3.369, 2.316], [-1.509, -2.948, 1.263, 4.588, 0.454, 2.009], [2.958, -2.942, 0.867, 1.911, 2.392, 2.127], [3.566, 0.671, 1.618, 0.253, 5.112, 3.1], [1.816, -2.011, 1.094, 0.402, 1.679, 2.148], [0.957, -1.668, 0.579, 1.105, 0.683, 1.586], [1.676, 3.716, 1.075, 3.204, 0.902, 2.406], [2.655, -1.717, 0.827, 1.883, 0.155, 2.358], [3.511, 2.562, 1.175, -0.348, 1.486, 0.553]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.046, -0.307, 0.362, 0.784, -0.024, 0.785], [1.165, -2.351, 1.138, 0.213, 0.736, 0.353], [1.15, -2.093, 0.722, 0.4, 0.33, 0.205], [1.084, -0.85, 1.131, 0.451, -0.085, 0.317], [1.331, -1.435, 0.691, 0.675, 0.723, 0.254], [-1.236, 0.563, -0.088, 0.27, -0.102, 0.794]]\nB: [[1.265, -0.056, 0.282, 0.326, 0.027, 0.886], [1.533, -2.203, 0.449, 0.341, 0.914, 0.835], [0.973, -1.818, 0.452, -0.205, -0.0, 0.557], [1.212, -0.809, 0.364, 0.233, 0.14, 0.279], [0.952, -0.74, 0.435, -0.133, 0.174, 0.554], [-1.162, 0.16, 0.691, 0.327, -0.202, 0.736]]\nC: [[1.057, -0.394, 0.235, 0.507, 0.4, 0.47], [1.152, -1.942, 0.923, 0.249, 0.43, 0.441], [1.185, -1.67, 0.793, 0.195, 0.105, 0.183], [0.815, -0.905, 0.823, 0.231, 0.165, 0.244], [0.988, -0.991, 0.818, 0.253, 0.25, 0.209], [-1.265, 0.61, 0.238, 0.204, 0.16, 0.435]]\nD: [[1.051, -0.65, -0.171, 0.578, 0.483, 0.109], [0.936, -1.859, 0.474, -0.087, 0.06, 0.148], [1.334, -2.107, 0.81, 0.465, 0.412, 0.633], [0.554, -0.966, 0.763, 0.354, 0.344, 0.116], [1.173, -0.543, 0.619, 0.486, 0.296, 0.039], [-1.019, 0.12, 0.267, -0.232, -0.155, 0.735]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_73_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_73_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the object in the scene. The camera pose information includes: the rotation matrix: [[0.264492, -0.222038, 0.938479], [-0.962334, 0.002714, 0.271857], [-0.062909, -0.975034, -0.212957]]; the translation vector: [0.925816, 4.784833, 1.497389], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.046, -0.307, 0.362, 0.784, -0.024, 0.785], [1.165, -2.351, 1.138, 0.213, 0.736, 0.353], [1.15, -2.093, 0.722, 0.4, 0.33, 0.205], [1.084, -0.85, 1.131, 0.451, -0.085, 0.317], [1.331, -1.435, 0.691, 0.675, 0.723, 0.254], [-1.236, 0.563, -0.088, 0.27, -0.102, 0.794]]\nB: [[1.265, -0.056, 0.282, 0.326, 0.027, 0.886], [1.533, -2.203, 0.449, 0.341, 0.914, 0.835], [0.973, -1.818, 0.452, -0.205, -0.0, 0.557], [1.212, -0.809, 0.364, 0.233, 0.14, 0.279], [0.952, -0.74, 0.435, -0.133, 0.174, 0.554], [-1.162, 0.16, 0.691, 0.327, -0.202, 0.736]]\nC: [[1.057, -0.394, 0.235, 0.507, 0.4, 0.47], [1.152, -1.942, 0.923, 0.249, 0.43, 0.441], [1.185, -1.67, 0.793, 0.195, 0.105, 0.183], [0.815, -0.905, 0.823, 0.231, 0.165, 0.244], [0.988, -0.991, 0.818, 0.253, 0.25, 0.209], [-1.265, 0.61, 0.238, 0.204, 0.16, 0.435]]\nD: [[1.051, -0.65, -0.171, 0.578, 0.483, 0.109], [0.936, -1.859, 0.474, -0.087, 0.06, 0.148], [1.334, -2.107, 0.81, 0.465, 0.412, 0.633], [0.554, -0.966, 0.763, 0.354, 0.344, 0.116], [1.173, -0.543, 0.619, 0.486, 0.296, 0.039], [-1.019, 0.12, 0.267, -0.232, -0.155, 0.735]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.085, -0.215, 0.983, 0.671, 0.944, 0.637]]\nB: [[-1.195, -0.19, 1.175, 0.471, 1.343, 0.221]]\nC: [[-1.17, -0.298, 0.934, 0.962, 1.213, 0.413]]\nD: [[-1.39, -0.221, 0.693, 0.182, 1.277, 0.167]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_74_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_74_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the sink in the scene. The camera pose information includes: the rotation matrix: [[-0.409087, -0.112571, 0.905525], [-0.910894, 0.109148, -0.397943], [-0.05404, -0.987631, -0.147191]]; the translation vector: [4.421403, 3.579741, 1.526424], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.085, -0.215, 0.983, 0.671, 0.944, 0.637]]\nB: [[-1.195, -0.19, 1.175, 0.471, 1.343, 0.221]]\nC: [[-1.17, -0.298, 0.934, 0.962, 1.213, 0.413]]\nD: [[-1.39, -0.221, 0.693, 0.182, 1.277, 0.167]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.095, 1.592, 1.222, 1.568, 0.744, 2.142]]\nB: [[-0.877, 2.359, 1.301, 1.758, 0.807, 2.272]]\nC: [[-0.883, 2.133, 0.636, 0.867, 0.763, 2.547]]\nD: [[-1.101, 1.96, 1.128, 1.33, 0.454, 2.075]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_75_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_75_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the mirror doors in the scene. The camera pose information includes: the rotation matrix: [[-0.998134, -0.025826, -0.055325], [0.04389, 0.326427, -0.944203], [0.042444, -0.94487, -0.324684]]; the translation vector: [2.355182, 2.984659, 1.395898], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.095, 1.592, 1.222, 1.568, 0.744, 2.142]]\nB: [[-0.877, 2.359, 1.301, 1.758, 0.807, 2.272]]\nC: [[-0.883, 2.133, 0.636, 0.867, 0.763, 2.547]]\nD: [[-1.101, 1.96, 1.128, 1.33, 0.454, 2.075]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.917, 0.769, 0.393, 0.162, 0.916, 0.83], [2.158, 0.091, 1.885, 0.64, 0.369, 0.373]]\nB: [[1.7, 0.645, 0.863, 0.067, 1.221, 0.849], [2.328, 0.352, 2.246, 0.627, 0.498, 0.253]]\nC: [[1.798, 1.202, -0.093, 0.135, 0.516, 1.131], [2.029, 0.523, 2.037, 0.813, 0.35, 0.6]]\nD: [[1.675, 0.61, 0.316, -0.227, 0.481, 0.35], [2.253, 0.494, 1.51, 1.013, 0.177, 0.842]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_76_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_76_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the kitchen cabinet in the scene. The camera pose information includes: the rotation matrix: [[-0.399387, 0.327689, -0.856218], [0.9115, 0.041819, -0.409169], [-0.098274, -0.94386, -0.315391]]; the translation vector: [4.88233, 2.963563, 1.403722], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.917, 0.769, 0.393, 0.162, 0.916, 0.83], [2.158, 0.091, 1.885, 0.64, 0.369, 0.373]]\nB: [[1.7, 0.645, 0.863, 0.067, 1.221, 0.849], [2.328, 0.352, 2.246, 0.627, 0.498, 0.253]]\nC: [[1.798, 1.202, -0.093, 0.135, 0.516, 1.131], [2.029, 0.523, 2.037, 0.813, 0.35, 0.6]]\nD: [[1.675, 0.61, 0.316, -0.227, 0.481, 0.35], [2.253, 0.494, 1.51, 1.013, 0.177, 0.842]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.494, 1.559, 0.938, 0.656, 5.217, 2.076], [2.053, 1.519, 1.142, 0.022, 3.126, 1.696], [1.526, 0.314, 1.467, 0.388, 0.432, 1.965], [1.119, -0.324, 1.442, 0.427, 0.845, 2.198], [1.576, -0.035, 0.321, 0.045, 0.534, 1.117], [1.692, -0.958, 0.006, 0.205, 2.737, 0.651], [2.137, -2.631, 1.077, 0.184, 0.904, 1.508], [1.333, -3.255, 1.466, 0.791, -0.082, 1.662], [1.459, -3.425, 1.79, -0.348, -0.024, 0.359], [0.387, -3.416, 1.314, 3.646, -0.097, 1.995], [-1.825, -3.194, 1.168, 0.422, 1.404, 1.529], [-0.304, 4.179, 0.452, 3.264, -0.007, 1.066], [1.999, 3.872, 0.716, 0.498, 0.487, 1.358]]\nB: [[-1.693, 1.424, 1.03, 0.376, 5.083, 2.034], [1.765, 1.957, 1.138, 0.161, 3.199, 2.18], [1.589, 0.333, 0.987, 0.355, 0.095, 1.877], [1.425, 0.157, 1.015, 0.112, 0.477, 1.967], [1.63, -0.081, 0.672, 0.331, 0.259, 1.339], [1.705, -1.447, 0.484, 0.238, 2.779, 0.873], [1.951, -2.837, 1.012, 0.146, 0.69, 1.445], [1.797, -3.186, 1.022, 0.444, 0.092, 1.424], [1.591, -3.334, 1.384, 0.106, 0.324, 0.652], [-0.022, -3.519, 0.892, 3.311, 0.275, 1.699], [-1.705, -2.728, 0.676, 0.126, 1.402, 1.204], [-0.01, 3.839, 0.745, 3.147, 0.481, 1.347], [1.63, 3.568, 0.892, 0.411, 0.327, 1.532]]\nC: [[-1.26, 1.503, 0.893, 0.629, 5.544, 1.914], [2.175, 1.47, 1.47, 0.109, 3.382, 1.686], [1.982, -0.011, 0.916, 0.426, -0.326, 1.566], [1.181, 0.067, 1.21, 0.067, 0.005, 2.351], [1.524, 0.001, 0.471, 0.286, 0.408, 1.265], [1.238, -1.52, 0.419, 0.599, 3.184, 1.176], [1.553, -3.177, 0.653, 0.32, 0.427, 1.885], [1.383, -3.363, 1.432, 0.865, -0.009, 1.444], [1.288, -3.498, 1.769, -0.257, 0.218, 1.054], [0.393, -3.522, 1.337, 3.619, 0.242, 1.594], [-1.576, -3.113, 0.753, 0.379, 1.777, 1.195], [-0.268, 3.894, 0.852, 2.983, 0.721, 1.393], [1.465, 3.133, 0.435, 0.617, 0.63, 1.96]]\nD: [[-1.946, 1.454, 1.304, 0.285, 4.759, 1.584], [1.735, 2.118, 1.431, 0.5, 3.38, 2.198], [2.01, 0.269, 1.406, 0.118, -0.362, 2.255], [1.665, -0.294, 0.623, -0.295, 0.208, 2.363], [1.918, 0.112, 1.078, 0.599, 0.597, 0.896], [1.655, -1.698, 0.75, 0.063, 2.896, 0.441], [2.382, -2.981, 1.161, 0.203, 0.379, 1.162], [1.828, -2.97, 0.979, 0.706, -0.194, 1.801], [1.717, -3.159, 1.188, 0.204, 0.385, 0.448], [0.303, -3.389, 1.008, 3.649, 0.715, 1.331], [-1.467, -2.443, 0.641, 0.545, 0.903, 1.371], [-0.151, 3.761, 0.508, 3.288, 0.802, 1.225], [1.797, 3.579, 1.179, 0.009, 0.008, 1.708]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_77_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_77_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.810147, -0.229725, 0.539341], [-0.586224, 0.314131, -0.746769], [0.002128, -0.921167, -0.389162]]; the translation vector: [3.108561, 2.950706, 1.466118], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.494, 1.559, 0.938, 0.656, 5.217, 2.076], [2.053, 1.519, 1.142, 0.022, 3.126, 1.696], [1.526, 0.314, 1.467, 0.388, 0.432, 1.965], [1.119, -0.324, 1.442, 0.427, 0.845, 2.198], [1.576, -0.035, 0.321, 0.045, 0.534, 1.117], [1.692, -0.958, 0.006, 0.205, 2.737, 0.651], [2.137, -2.631, 1.077, 0.184, 0.904, 1.508], [1.333, -3.255, 1.466, 0.791, -0.082, 1.662], [1.459, -3.425, 1.79, -0.348, -0.024, 0.359], [0.387, -3.416, 1.314, 3.646, -0.097, 1.995], [-1.825, -3.194, 1.168, 0.422, 1.404, 1.529], [-0.304, 4.179, 0.452, 3.264, -0.007, 1.066], [1.999, 3.872, 0.716, 0.498, 0.487, 1.358]]\nB: [[-1.693, 1.424, 1.03, 0.376, 5.083, 2.034], [1.765, 1.957, 1.138, 0.161, 3.199, 2.18], [1.589, 0.333, 0.987, 0.355, 0.095, 1.877], [1.425, 0.157, 1.015, 0.112, 0.477, 1.967], [1.63, -0.081, 0.672, 0.331, 0.259, 1.339], [1.705, -1.447, 0.484, 0.238, 2.779, 0.873], [1.951, -2.837, 1.012, 0.146, 0.69, 1.445], [1.797, -3.186, 1.022, 0.444, 0.092, 1.424], [1.591, -3.334, 1.384, 0.106, 0.324, 0.652], [-0.022, -3.519, 0.892, 3.311, 0.275, 1.699], [-1.705, -2.728, 0.676, 0.126, 1.402, 1.204], [-0.01, 3.839, 0.745, 3.147, 0.481, 1.347], [1.63, 3.568, 0.892, 0.411, 0.327, 1.532]]\nC: [[-1.26, 1.503, 0.893, 0.629, 5.544, 1.914], [2.175, 1.47, 1.47, 0.109, 3.382, 1.686], [1.982, -0.011, 0.916, 0.426, -0.326, 1.566], [1.181, 0.067, 1.21, 0.067, 0.005, 2.351], [1.524, 0.001, 0.471, 0.286, 0.408, 1.265], [1.238, -1.52, 0.419, 0.599, 3.184, 1.176], [1.553, -3.177, 0.653, 0.32, 0.427, 1.885], [1.383, -3.363, 1.432, 0.865, -0.009, 1.444], [1.288, -3.498, 1.769, -0.257, 0.218, 1.054], [0.393, -3.522, 1.337, 3.619, 0.242, 1.594], [-1.576, -3.113, 0.753, 0.379, 1.777, 1.195], [-0.268, 3.894, 0.852, 2.983, 0.721, 1.393], [1.465, 3.133, 0.435, 0.617, 0.63, 1.96]]\nD: [[-1.946, 1.454, 1.304, 0.285, 4.759, 1.584], [1.735, 2.118, 1.431, 0.5, 3.38, 2.198], [2.01, 0.269, 1.406, 0.118, -0.362, 2.255], [1.665, -0.294, 0.623, -0.295, 0.208, 2.363], [1.918, 0.112, 1.078, 0.599, 0.597, 0.896], [1.655, -1.698, 0.75, 0.063, 2.896, 0.441], [2.382, -2.981, 1.161, 0.203, 0.379, 1.162], [1.828, -2.97, 0.979, 0.706, -0.194, 1.801], [1.717, -3.159, 1.188, 0.204, 0.385, 0.448], [0.303, -3.389, 1.008, 3.649, 0.715, 1.331], [-1.467, -2.443, 0.641, 0.545, 0.903, 1.371], [-0.151, 3.761, 0.508, 3.288, 0.802, 1.225], [1.797, 3.579, 1.179, 0.009, 0.008, 1.708]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.793, 1.247, 0.29, 0.296, 0.279, -0.014], [1.019, 0.024, 1.569, 0.553, 0.236, 0.679]]\nB: [[-0.837, 1.73, 0.172, 0.311, 0.446, 0.446], [0.579, -0.45, 1.284, 0.394, 0.372, 0.858]]\nC: [[-0.983, 2.19, 0.493, -0.03, 0.329, 0.928], [0.864, -0.587, 1.773, 0.118, 0.794, 0.799]]\nD: [[-0.553, 2.216, 0.459, 0.267, 0.459, 0.522], [0.806, 0.026, 1.267, 0.403, 0.702, 0.558]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_78_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_78_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the clothes in the scene. The camera pose information includes: the rotation matrix: [[-0.187285, -0.627824, 0.755488], [-0.982305, 0.118515, -0.145025], [0.001514, -0.76928, -0.63891]]; the translation vector: [1.001752, 1.17634, 1.437838], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.793, 1.247, 0.29, 0.296, 0.279, -0.014], [1.019, 0.024, 1.569, 0.553, 0.236, 0.679]]\nB: [[-0.837, 1.73, 0.172, 0.311, 0.446, 0.446], [0.579, -0.45, 1.284, 0.394, 0.372, 0.858]]\nC: [[-0.983, 2.19, 0.493, -0.03, 0.329, 0.928], [0.864, -0.587, 1.773, 0.118, 0.794, 0.799]]\nD: [[-0.553, 2.216, 0.459, 0.267, 0.459, 0.522], [0.806, 0.026, 1.267, 0.403, 0.702, 0.558]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.858, -1.05, -0.049, 0.631, 1.092, 1.022], [0.969, 2.457, 0.703, 0.33, 0.355, 0.535], [1.292, 0.687, 0.943, 0.724, 0.324, 1.126], [1.537, -0.024, 0.37, 0.738, 0.769, 0.806], [2.91, -1.195, 1.375, 0.242, 1.166, 0.582], [2.799, -1.708, 0.863, 0.877, 0.364, 0.812], [2.158, -1.992, 0.634, 0.411, 0.065, 1.19], [-2.861, 0.973, 1.098, 0.744, 0.232, 0.595], [-3.055, 1.702, 0.901, 0.639, 0.173, 0.718], [3.451, -0.934, 1.096, 0.502, 0.89, 0.387]]\nB: [[-2.77, -0.712, 0.41, 0.782, 0.713, 0.859], [1.367, 2.116, 0.842, 0.257, 0.504, 0.248], [1.716, 0.519, 0.519, 0.661, 0.573, 0.903], [1.577, -0.324, 0.811, 0.462, 0.54, 0.431], [3.037, -1.452, 0.953, 0.581, 0.687, 0.531], [2.669, -1.872, 0.986, 0.552, 0.48, 0.568], [2.211, -1.887, 0.725, 0.677, 0.554, 1.018], [-2.956, 0.672, 0.826, 0.436, 0.319, 0.465], [-2.626, 1.651, 0.53, 0.537, 0.47, 0.924], [2.995, -0.435, 0.615, 0.566, 0.706, 0.886]]\nC: [[-2.925, -0.243, 0.295, 0.519, 0.44, 0.711], [1.485, 1.766, 1.018, 0.081, 0.848, 0.483], [1.717, 0.68, 0.214, 0.236, 1.037, 0.434], [1.205, -0.323, 1.125, 0.097, 0.642, 0.242], [3.189, -1.068, 0.599, 0.36, 1.144, 0.939], [2.418, -1.941, 1.167, 0.598, 0.698, 0.702], [1.723, -2.159, 0.821, 0.484, 0.884, 0.696], [-3.03, 0.47, 1.025, 0.789, 0.045, 0.278], [-2.913, 1.461, 0.819, 0.202, 0.085, 1.03], [2.826, -0.221, 0.951, 0.339, 0.752, 1.266]]\nD: [[-3.135, -0.575, -0.082, 0.411, 0.399, 1.112], [1.76, 1.636, 0.661, -0.118, 0.316, 0.196], [2.067, 0.976, 0.67, 0.22, 0.315, 1.158], [1.439, -0.283, 0.584, 0.087, 0.218, 0.206], [2.848, -1.357, 1.295, 0.653, 0.266, 0.059], [2.99, -1.86, 1.333, 0.578, 0.108, 0.112], [2.118, -1.567, 1.178, 0.323, 0.289, 0.96], [-3.43, 1.005, 1.071, 0.331, 0.71, 0.959], [-3.114, 1.972, 0.571, 0.075, 0.864, 0.441], [2.987, 0.022, 0.923, 0.173, 0.274, 0.482]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_79_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_79_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[0.515401, -0.339121, 0.786994], [-0.847541, -0.337435, 0.40965], [0.126638, -0.878143, -0.461333]]; the translation vector: [4.776819, 1.138867, 1.280463], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.858, -1.05, -0.049, 0.631, 1.092, 1.022], [0.969, 2.457, 0.703, 0.33, 0.355, 0.535], [1.292, 0.687, 0.943, 0.724, 0.324, 1.126], [1.537, -0.024, 0.37, 0.738, 0.769, 0.806], [2.91, -1.195, 1.375, 0.242, 1.166, 0.582], [2.799, -1.708, 0.863, 0.877, 0.364, 0.812], [2.158, -1.992, 0.634, 0.411, 0.065, 1.19], [-2.861, 0.973, 1.098, 0.744, 0.232, 0.595], [-3.055, 1.702, 0.901, 0.639, 0.173, 0.718], [3.451, -0.934, 1.096, 0.502, 0.89, 0.387]]\nB: [[-2.77, -0.712, 0.41, 0.782, 0.713, 0.859], [1.367, 2.116, 0.842, 0.257, 0.504, 0.248], [1.716, 0.519, 0.519, 0.661, 0.573, 0.903], [1.577, -0.324, 0.811, 0.462, 0.54, 0.431], [3.037, -1.452, 0.953, 0.581, 0.687, 0.531], [2.669, -1.872, 0.986, 0.552, 0.48, 0.568], [2.211, -1.887, 0.725, 0.677, 0.554, 1.018], [-2.956, 0.672, 0.826, 0.436, 0.319, 0.465], [-2.626, 1.651, 0.53, 0.537, 0.47, 0.924], [2.995, -0.435, 0.615, 0.566, 0.706, 0.886]]\nC: [[-2.925, -0.243, 0.295, 0.519, 0.44, 0.711], [1.485, 1.766, 1.018, 0.081, 0.848, 0.483], [1.717, 0.68, 0.214, 0.236, 1.037, 0.434], [1.205, -0.323, 1.125, 0.097, 0.642, 0.242], [3.189, -1.068, 0.599, 0.36, 1.144, 0.939], [2.418, -1.941, 1.167, 0.598, 0.698, 0.702], [1.723, -2.159, 0.821, 0.484, 0.884, 0.696], [-3.03, 0.47, 1.025, 0.789, 0.045, 0.278], [-2.913, 1.461, 0.819, 0.202, 0.085, 1.03], [2.826, -0.221, 0.951, 0.339, 0.752, 1.266]]\nD: [[-3.135, -0.575, -0.082, 0.411, 0.399, 1.112], [1.76, 1.636, 0.661, -0.118, 0.316, 0.196], [2.067, 0.976, 0.67, 0.22, 0.315, 1.158], [1.439, -0.283, 0.584, 0.087, 0.218, 0.206], [2.848, -1.357, 1.295, 0.653, 0.266, 0.059], [2.99, -1.86, 1.333, 0.578, 0.108, 0.112], [2.118, -1.567, 1.178, 0.323, 0.289, 0.96], [-3.43, 1.005, 1.071, 0.331, 0.71, 0.959], [-3.114, 1.972, 0.571, 0.075, 0.864, 0.441], [2.987, 0.022, 0.923, 0.173, 0.274, 0.482]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.456, -1.689, 0.986, 1.238, 0.639, 1.143], [-1.189, -1.791, 0.864, 0.611, 1.379, 1.148]]\nB: [[1.709, -1.624, 1.232, 0.531, 0.409, 1.263], [-0.537, -2.035, 0.965, 0.162, 1.273, 1.399]]\nC: [[1.92, -1.614, 0.415, 0.301, 0.956, 1.133], [-0.648, -1.783, 0.191, 0.47, 1.3, 1.09]]\nD: [[1.863, -1.557, 0.74, 0.792, 0.462, 1.459], [-0.873, -1.717, 0.611, 0.169, 1.157, 1.341]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_80_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_80_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[0.348231, 0.123124, -0.929288], [0.936413, -1.6e-05, 0.350899], [0.043189, -0.992391, -0.1153]]; the translation vector: [2.712005, 2.075202, 1.464169], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.456, -1.689, 0.986, 1.238, 0.639, 1.143], [-1.189, -1.791, 0.864, 0.611, 1.379, 1.148]]\nB: [[1.709, -1.624, 1.232, 0.531, 0.409, 1.263], [-0.537, -2.035, 0.965, 0.162, 1.273, 1.399]]\nC: [[1.92, -1.614, 0.415, 0.301, 0.956, 1.133], [-0.648, -1.783, 0.191, 0.47, 1.3, 1.09]]\nD: [[1.863, -1.557, 0.74, 0.792, 0.462, 1.459], [-0.873, -1.717, 0.611, 0.169, 1.157, 1.341]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.91, 0.435, 1.317, 0.162, -0.028, 0.372], [-1.612, 0.781, 1.119, -0.027, 0.477, 0.779], [-0.879, 0.442, 1.028, 0.015, 0.023, 0.191], [-1.689, 1.721, 1.33, 0.202, 0.203, 0.899]]\nB: [[-1.22, 0.565, 1.527, 0.13, 0.316, 0.334], [-1.214, 0.573, 1.041, 0.138, 0.311, 0.395], [-1.241, 0.926, 1.496, 0.134, 0.334, 0.376], [-1.254, 1.276, 1.499, 0.14, 0.375, 0.407]]\nC: [[-0.897, 0.321, 1.25, -0.192, -0.085, 0.628], [-1.027, 0.54, 0.746, 0.155, 0.593, 0.872], [-1.661, 1.141, 1.852, 0.038, 0.687, 0.36], [-1.716, 1.739, 1.744, 0.171, 0.366, 0.735]]\nD: [[-0.881, 0.818, 1.879, -0.183, 0.463, 0.205], [-0.767, 0.607, 0.616, 0.203, 0.246, 0.191], [-0.822, 0.77, 1.534, -0.248, 0.163, 0.71], [-1.508, 0.961, 1.625, -0.148, 0.39, 0.839]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_81_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_81_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the rack in the scene. The camera pose information includes: the rotation matrix: [[-0.937403, 0.174354, -0.301457], [0.34768, 0.517889, -0.781607], [0.019845, -0.837491, -0.54609]]; the translation vector: [1.513881, 1.499843, 1.388066], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.91, 0.435, 1.317, 0.162, -0.028, 0.372], [-1.612, 0.781, 1.119, -0.027, 0.477, 0.779], [-0.879, 0.442, 1.028, 0.015, 0.023, 0.191], [-1.689, 1.721, 1.33, 0.202, 0.203, 0.899]]\nB: [[-1.22, 0.565, 1.527, 0.13, 0.316, 0.334], [-1.214, 0.573, 1.041, 0.138, 0.311, 0.395], [-1.241, 0.926, 1.496, 0.134, 0.334, 0.376], [-1.254, 1.276, 1.499, 0.14, 0.375, 0.407]]\nC: [[-0.897, 0.321, 1.25, -0.192, -0.085, 0.628], [-1.027, 0.54, 0.746, 0.155, 0.593, 0.872], [-1.661, 1.141, 1.852, 0.038, 0.687, 0.36], [-1.716, 1.739, 1.744, 0.171, 0.366, 0.735]]\nD: [[-0.881, 0.818, 1.879, -0.183, 0.463, 0.205], [-0.767, 0.607, 0.616, 0.203, 0.246, 0.191], [-0.822, 0.77, 1.534, -0.248, 0.163, 0.71], [-1.508, 0.961, 1.625, -0.148, 0.39, 0.839]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.79, -0.98, 1.163, 0.352, 0.978, 2.049]]\nB: [[1.303, -0.943, 0.81, 0.085, 1.431, 2.157]]\nC: [[0.918, -1.038, 0.78, -0.022, 1.276, 1.887]]\nD: [[1.132, -1.26, 0.803, 0.268, 1.192, 2.17]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_82_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_82_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[-0.15851, 0.420096, -0.893529], [0.981106, -0.034663, -0.190342], [-0.110934, -0.906817, -0.406664]]; the translation vector: [4.004256, 0.910349, 2.578562], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.79, -0.98, 1.163, 0.352, 0.978, 2.049]]\nB: [[1.303, -0.943, 0.81, 0.085, 1.431, 2.157]]\nC: [[0.918, -1.038, 0.78, -0.022, 1.276, 1.887]]\nD: [[1.132, -1.26, 0.803, 0.268, 1.192, 2.17]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.027, 0.959, 0.116, 0.065, 0.01, 0.668], [-1.502, 1.757, 0.887, 0.168, -0.298, 0.404], [-1.464, 1.693, 1.349, 0.508, -0.033, 0.751], [-1.515, 1.819, 1.174, 0.619, -0.056, 0.931], [-1.32, 1.579, 1.138, 0.221, -0.036, 0.586]]\nB: [[-1.555, 1.321, -0.005, -0.086, -0.066, 0.68], [-1.503, 1.647, 1.497, 0.094, 0.629, 0.772], [-1.545, 2.057, 0.682, 0.091, -0.365, -0.177], [-1.817, 2.125, 0.639, 0.421, 0.176, 0.148], [-2.148, 2.167, 0.268, 0.654, -0.085, 0.81]]\nC: [[-1.921, 0.926, 0.476, 0.205, 0.401, 1.004], [-1.317, 1.461, 1.183, 0.482, -0.087, -0.114], [-0.981, 1.858, 0.937, -0.085, -0.01, 0.117], [-1.804, 1.654, 1.126, 0.091, 0.345, 0.125], [-2.134, 1.498, 0.297, 0.016, 0.463, 0.232]]\nD: [[-2.011, 1.284, 0.385, 0.186, 0.39, 0.566], [-1.266, 1.943, 1.101, 0.313, 0.196, 0.371], [-1.224, 1.994, 0.869, 0.351, 0.116, 0.277], [-1.583, 1.923, 1.035, 0.381, 0.288, 0.498], [-1.707, 1.925, 0.764, 0.426, 0.259, 0.583]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_83_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_83_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the bag in the scene. The camera pose information includes: the rotation matrix: [[0.82141, -0.124481, 0.556588], [-0.562763, -0.33543, 0.755503], [0.092651, -0.933805, -0.345579]]; the translation vector: [1.795382, 2.457259, 1.379582], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.027, 0.959, 0.116, 0.065, 0.01, 0.668], [-1.502, 1.757, 0.887, 0.168, -0.298, 0.404], [-1.464, 1.693, 1.349, 0.508, -0.033, 0.751], [-1.515, 1.819, 1.174, 0.619, -0.056, 0.931], [-1.32, 1.579, 1.138, 0.221, -0.036, 0.586]]\nB: [[-1.555, 1.321, -0.005, -0.086, -0.066, 0.68], [-1.503, 1.647, 1.497, 0.094, 0.629, 0.772], [-1.545, 2.057, 0.682, 0.091, -0.365, -0.177], [-1.817, 2.125, 0.639, 0.421, 0.176, 0.148], [-2.148, 2.167, 0.268, 0.654, -0.085, 0.81]]\nC: [[-1.921, 0.926, 0.476, 0.205, 0.401, 1.004], [-1.317, 1.461, 1.183, 0.482, -0.087, -0.114], [-0.981, 1.858, 0.937, -0.085, -0.01, 0.117], [-1.804, 1.654, 1.126, 0.091, 0.345, 0.125], [-2.134, 1.498, 0.297, 0.016, 0.463, 0.232]]\nD: [[-2.011, 1.284, 0.385, 0.186, 0.39, 0.566], [-1.266, 1.943, 1.101, 0.313, 0.196, 0.371], [-1.224, 1.994, 0.869, 0.351, 0.116, 0.277], [-1.583, 1.923, 1.035, 0.381, 0.288, 0.498], [-1.707, 1.925, 0.764, 0.426, 0.259, 0.583]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.036, 1.866, 1.489, 0.307, 0.157, 2.451], [-1.162, -0.721, 0.524, 0.061, 0.505, 0.591], [-1.692, -0.087, 1.909, 0.08, 0.301, 0.1], [-1.275, -0.78, -0.299, 0.662, 0.631, -0.319]]\nB: [[-1.306, 1.944, 1.27, 0.242, 0.415, 1.919], [-1.846, -0.095, 0.652, 0.668, -0.011, -0.065], [-1.708, -0.182, 1.324, -0.259, 0.382, 0.757], [-0.989, -0.521, 0.267, 0.114, 0.569, -0.144]]\nC: [[-1.606, 1.5, 1.094, 0.082, 0.444, 2.163], [-1.349, -0.456, 0.266, 0.226, 0.434, 0.139], [-1.295, -0.266, 1.634, 0.118, 0.05, 0.32], [-1.418, -0.408, 0.197, 0.3, 0.329, 0.161]]\nD: [[-1.696, 1.738, 0.967, 0.285, -0.051, 2.413], [-0.922, -0.569, 0.642, 0.33, 0.259, -0.242], [-0.853, -0.408, 2.052, 0.046, 0.488, 0.615], [-1.165, -0.273, 0.19, 0.107, 0.57, 0.605]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_84_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_84_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the book in the scene. The camera pose information includes: the rotation matrix: [[0.954506, 0.05554, -0.292973], [0.288831, -0.41644, 0.862064], [-0.074127, -0.907465, -0.413536]]; the translation vector: [2.66447, 1.005586, 1.476015], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.036, 1.866, 1.489, 0.307, 0.157, 2.451], [-1.162, -0.721, 0.524, 0.061, 0.505, 0.591], [-1.692, -0.087, 1.909, 0.08, 0.301, 0.1], [-1.275, -0.78, -0.299, 0.662, 0.631, -0.319]]\nB: [[-1.306, 1.944, 1.27, 0.242, 0.415, 1.919], [-1.846, -0.095, 0.652, 0.668, -0.011, -0.065], [-1.708, -0.182, 1.324, -0.259, 0.382, 0.757], [-0.989, -0.521, 0.267, 0.114, 0.569, -0.144]]\nC: [[-1.606, 1.5, 1.094, 0.082, 0.444, 2.163], [-1.349, -0.456, 0.266, 0.226, 0.434, 0.139], [-1.295, -0.266, 1.634, 0.118, 0.05, 0.32], [-1.418, -0.408, 0.197, 0.3, 0.329, 0.161]]\nD: [[-1.696, 1.738, 0.967, 0.285, -0.051, 2.413], [-0.922, -0.569, 0.642, 0.33, 0.259, -0.242], [-0.853, -0.408, 2.052, 0.046, 0.488, 0.615], [-1.165, -0.273, 0.19, 0.107, 0.57, 0.605]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.105, -2.287, 1.298, 5.216, 0.87, 2.807], [1.522, -1.359, 1.295, 0.692, 3.211, 2.519], [-1.482, 0.95, 0.954, -0.118, 3.651, 2.351], [2.186, 1.699, 1.104, -0.162, 3.237, 1.812], [-2.278, -2.05, 0.889, 0.84, 1.577, 2.045], [1.673, 0.416, 0.493, 2.003, 0.505, 2.036], [0.961, 0.497, 0.688, -0.227, 0.745, 1.241], [-2.744, -1.281, 0.568, 1.033, 0.219, 1.748], [-1.203, 3.13, 0.749, 0.354, 1.039, 2.281], [0.453, 3.938, 1.358, 3.143, -0.207, 1.101], [1.577, -0.17, 0.647, 1.504, 0.197, 0.91], [0.55, 0.958, 0.74, -0.022, 0.508, 1.915], [-0.28, -2.346, 2.125, 4.551, 0.291, 0.411], [-2.245, 3.374, 2.167, 0.315, 0.57, 1.021], [-1.883, 4.031, 0.791, 0.575, 0.114, 1.338]]\nB: [[-0.045, -2.933, 0.729, 5.273, 0.38, 1.87], [1.815, -0.872, 1.2, 0.381, 3.199, 2.451], [-1.835, 0.525, 0.883, 0.663, 4.217, 2.299], [1.852, 1.563, 0.117, 0.236, 3.143, 0.945], [-2.89, -2.063, 0.836, 0.206, 1.672, 2.238], [1.326, 0.326, 0.98, 1.535, 0.601, 1.665], [0.245, 0.603, 0.825, -0.004, 1.064, 1.817], [-1.862, -0.49, 1.467, 1.026, 0.012, 1.363], [-1.379, 3.112, 1.213, 0.486, 0.543, 1.682], [0.858, 3.952, 1.318, 3.11, 0.53, 1.733], [1.684, 0.251, 1.226, 1.531, 0.586, 0.576], [0.354, 1.015, 0.82, 0.415, 0.222, 1.857], [-0.273, -2.214, 2.258, 4.248, 0.77, 0.29], [-2.467, 2.65, 1.797, 0.149, 0.618, 1.025], [-2.164, 4.048, 1.03, 0.675, 0.141, 1.166]]\nC: [[-0.372, -2.705, 1.171, 4.784, 0.513, 2.321], [1.95, -1.245, 1.075, 0.31, 2.969, 2.221], [-1.736, 0.974, 1.065, 0.251, 4.086, 2.141], [2.079, 1.895, 0.614, 0.176, 3.361, 1.364], [-2.712, -1.857, 1.256, 0.344, 1.764, 2.405], [1.315, 0.187, 0.814, 1.511, 0.109, 1.639], [0.561, 0.619, 0.751, 0.075, 0.858, 1.522], [-2.331, -0.947, 0.996, 1.029, 0.105, 1.863], [-0.884, 3.244, 0.946, 0.263, 0.884, 1.941], [0.617, 3.626, 1.612, 2.853, 0.244, 1.318], [1.37, 0.273, 0.995, 1.377, 0.117, 0.425], [0.697, 0.781, 1.082, 0.286, 0.193, 2.222], [-0.516, -2.505, 2.299, 4.488, 0.335, 0.203], [-2.543, 2.889, 1.67, 0.173, 0.883, 0.685], [-1.977, 3.626, 1.246, 0.551, 0.106, 1.503]]\nD: [[0.035, -2.86, 0.861, 4.728, 0.233, 1.918], [1.546, -1.395, 0.841, 0.548, 3.249, 1.737], [-1.897, 0.52, 1.01, -0.225, 4.45, 2.412], [1.773, 2.047, 0.256, 0.066, 3.328, 1.231], [-3.131, -2.108, 0.842, 0.6, 1.535, 2.741], [1.218, -0.29, 0.461, 1.245, -0.153, 2.098], [0.922, 0.65, 1.084, -0.181, 0.59, 1.506], [-2.276, -0.909, 0.599, 1.207, -0.285, 1.54], [-0.665, 3.431, 1.123, 0.223, 0.621, 1.641], [0.797, 3.806, 2.013, 2.472, 0.677, 1.495], [1.111, 0.293, 1.457, 1.431, 0.551, 0.85], [0.877, 1.185, 1.451, 0.625, -0.09, 2.43], [-0.138, -2.632, 2.484, 4.711, -0.137, 0.648], [-3.024, 2.792, 1.538, -0.201, 1.018, 0.323], [-2.319, 3.937, 1.522, 0.199, 0.289, 1.095]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_85_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_85_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.99336, -0.011945, -0.114427], [0.103059, -0.349694, 0.931178], [-0.051137, -0.936788, -0.346141]]; the translation vector: [2.948285, 4.432959, 1.460427], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.105, -2.287, 1.298, 5.216, 0.87, 2.807], [1.522, -1.359, 1.295, 0.692, 3.211, 2.519], [-1.482, 0.95, 0.954, -0.118, 3.651, 2.351], [2.186, 1.699, 1.104, -0.162, 3.237, 1.812], [-2.278, -2.05, 0.889, 0.84, 1.577, 2.045], [1.673, 0.416, 0.493, 2.003, 0.505, 2.036], [0.961, 0.497, 0.688, -0.227, 0.745, 1.241], [-2.744, -1.281, 0.568, 1.033, 0.219, 1.748], [-1.203, 3.13, 0.749, 0.354, 1.039, 2.281], [0.453, 3.938, 1.358, 3.143, -0.207, 1.101], [1.577, -0.17, 0.647, 1.504, 0.197, 0.91], [0.55, 0.958, 0.74, -0.022, 0.508, 1.915], [-0.28, -2.346, 2.125, 4.551, 0.291, 0.411], [-2.245, 3.374, 2.167, 0.315, 0.57, 1.021], [-1.883, 4.031, 0.791, 0.575, 0.114, 1.338]]\nB: [[-0.045, -2.933, 0.729, 5.273, 0.38, 1.87], [1.815, -0.872, 1.2, 0.381, 3.199, 2.451], [-1.835, 0.525, 0.883, 0.663, 4.217, 2.299], [1.852, 1.563, 0.117, 0.236, 3.143, 0.945], [-2.89, -2.063, 0.836, 0.206, 1.672, 2.238], [1.326, 0.326, 0.98, 1.535, 0.601, 1.665], [0.245, 0.603, 0.825, -0.004, 1.064, 1.817], [-1.862, -0.49, 1.467, 1.026, 0.012, 1.363], [-1.379, 3.112, 1.213, 0.486, 0.543, 1.682], [0.858, 3.952, 1.318, 3.11, 0.53, 1.733], [1.684, 0.251, 1.226, 1.531, 0.586, 0.576], [0.354, 1.015, 0.82, 0.415, 0.222, 1.857], [-0.273, -2.214, 2.258, 4.248, 0.77, 0.29], [-2.467, 2.65, 1.797, 0.149, 0.618, 1.025], [-2.164, 4.048, 1.03, 0.675, 0.141, 1.166]]\nC: [[-0.372, -2.705, 1.171, 4.784, 0.513, 2.321], [1.95, -1.245, 1.075, 0.31, 2.969, 2.221], [-1.736, 0.974, 1.065, 0.251, 4.086, 2.141], [2.079, 1.895, 0.614, 0.176, 3.361, 1.364], [-2.712, -1.857, 1.256, 0.344, 1.764, 2.405], [1.315, 0.187, 0.814, 1.511, 0.109, 1.639], [0.561, 0.619, 0.751, 0.075, 0.858, 1.522], [-2.331, -0.947, 0.996, 1.029, 0.105, 1.863], [-0.884, 3.244, 0.946, 0.263, 0.884, 1.941], [0.617, 3.626, 1.612, 2.853, 0.244, 1.318], [1.37, 0.273, 0.995, 1.377, 0.117, 0.425], [0.697, 0.781, 1.082, 0.286, 0.193, 2.222], [-0.516, -2.505, 2.299, 4.488, 0.335, 0.203], [-2.543, 2.889, 1.67, 0.173, 0.883, 0.685], [-1.977, 3.626, 1.246, 0.551, 0.106, 1.503]]\nD: [[0.035, -2.86, 0.861, 4.728, 0.233, 1.918], [1.546, -1.395, 0.841, 0.548, 3.249, 1.737], [-1.897, 0.52, 1.01, -0.225, 4.45, 2.412], [1.773, 2.047, 0.256, 0.066, 3.328, 1.231], [-3.131, -2.108, 0.842, 0.6, 1.535, 2.741], [1.218, -0.29, 0.461, 1.245, -0.153, 2.098], [0.922, 0.65, 1.084, -0.181, 0.59, 1.506], [-2.276, -0.909, 0.599, 1.207, -0.285, 1.54], [-0.665, 3.431, 1.123, 0.223, 0.621, 1.641], [0.797, 3.806, 2.013, 2.472, 0.677, 1.495], [1.111, 0.293, 1.457, 1.431, 0.551, 0.85], [0.877, 1.185, 1.451, 0.625, -0.09, 2.43], [-0.138, -2.632, 2.484, 4.711, -0.137, 0.648], [-3.024, 2.792, 1.538, -0.201, 1.018, 0.323], [-2.319, 3.937, 1.522, 0.199, 0.289, 1.095]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.057, -0.804, 1.454, 0.442, 9.194, 2.993], [-0.24, 3.939, 1.662, 3.88, 0.819, 2.915], [1.686, 1.778, 1.614, 0.375, 4.14, 2.879], [1.518, -0.292, 1.39, 0.502, 0.183, 1.29], [1.407, -0.606, 1.045, 0.392, 0.791, 2.018], [1.569, -2.479, 1.01, 0.478, 3.37, 1.883]]\nB: [[-1.712, -0.852, 0.991, 0.707, 9.653, 3.103], [0.145, 4.4, 1.88, 4.062, 1.231, 2.667], [1.473, 2.151, 1.876, 0.37, 4.413, 3.184], [1.75, -0.649, 1.384, 0.602, -0.213, 1.435], [1.139, -0.573, 1.304, 0.885, 0.718, 2.242], [1.077, -2.453, 0.735, 0.583, 3.786, 1.438]]\nC: [[-1.586, -0.802, 1.264, 0.785, 8.752, 2.813], [-0.226, 4.305, 1.323, 3.698, 1.086, 3.015], [1.969, 1.342, 1.623, -0.075, 3.888, 3.299], [1.213, -0.465, 1.751, 0.015, 0.594, 1.001], [0.993, -0.822, 1.254, 0.504, 1.181, 1.943], [1.069, -2.03, 1.336, 0.651, 3.224, 1.602]]\nD: [[-2.191, -0.396, 1.663, 0.009, 8.751, 3.114], [0.038, 3.888, 1.488, 4.056, 0.477, 3.26], [2.082, 1.991, 1.998, -0.123, 3.891, 2.467], [1.903, -0.079, 0.895, 0.439, 0.291, 0.791], [1.022, -0.776, 0.73, 0.121, 0.449, 1.843], [1.7, -2.034, 1.291, 0.089, 3.481, 2.087]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_86_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_86_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.908726, 0.150598, -0.389277], [0.406624, 0.108936, -0.907078], [-0.094198, -0.982575, -0.16023]]; the translation vector: [8.822721, 3.830595, 1.476402], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.057, -0.804, 1.454, 0.442, 9.194, 2.993], [-0.24, 3.939, 1.662, 3.88, 0.819, 2.915], [1.686, 1.778, 1.614, 0.375, 4.14, 2.879], [1.518, -0.292, 1.39, 0.502, 0.183, 1.29], [1.407, -0.606, 1.045, 0.392, 0.791, 2.018], [1.569, -2.479, 1.01, 0.478, 3.37, 1.883]]\nB: [[-1.712, -0.852, 0.991, 0.707, 9.653, 3.103], [0.145, 4.4, 1.88, 4.062, 1.231, 2.667], [1.473, 2.151, 1.876, 0.37, 4.413, 3.184], [1.75, -0.649, 1.384, 0.602, -0.213, 1.435], [1.139, -0.573, 1.304, 0.885, 0.718, 2.242], [1.077, -2.453, 0.735, 0.583, 3.786, 1.438]]\nC: [[-1.586, -0.802, 1.264, 0.785, 8.752, 2.813], [-0.226, 4.305, 1.323, 3.698, 1.086, 3.015], [1.969, 1.342, 1.623, -0.075, 3.888, 3.299], [1.213, -0.465, 1.751, 0.015, 0.594, 1.001], [0.993, -0.822, 1.254, 0.504, 1.181, 1.943], [1.069, -2.03, 1.336, 0.651, 3.224, 1.602]]\nD: [[-2.191, -0.396, 1.663, 0.009, 8.751, 3.114], [0.038, 3.888, 1.488, 4.056, 0.477, 3.26], [2.082, 1.991, 1.998, -0.123, 3.891, 2.467], [1.903, -0.079, 0.895, 0.439, 0.291, 0.791], [1.022, -0.776, 0.73, 0.121, 0.449, 1.843], [1.7, -2.034, 1.291, 0.089, 3.481, 2.087]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.406, -0.499, 0.127, 1.547, 0.014, 1.403], [1.954, 0.044, 0.911, 0.502, 4.499, 1.21], [0.82, -1.82, 0.463, 3.057, 0.123, 1.62], [-1.403, -1.396, 1.129, 0.78, 1.058, 1.608], [-1.077, 2.261, 0.337, 0.558, -0.028, 0.985]]\nB: [[-1.494, -0.876, 0.44, 1.936, 0.339, 1.286], [1.687, 0.61, 0.241, 0.363, 4.812, 0.869], [0.73, -1.681, 0.677, 2.541, -0.283, 1.252], [-1.274, -1.409, 0.377, 0.698, 1.296, 0.85], [-1.137, 2.544, 0.383, 0.033, 0.761, 1.018]]\nC: [[-2.268, -0.745, 0.912, 1.628, 0.264, 0.904], [1.958, 0.486, 0.503, -0.211, 4.549, 1.566], [-0.072, -2.097, 0.667, 2.893, 0.559, 1.549], [-0.724, -1.085, 0.723, 0.553, 1.77, 1.227], [-0.926, 2.697, 1.076, 0.821, 0.34, 1.204]]\nD: [[-1.791, -0.394, 0.511, 1.684, 0.11, 0.995], [1.786, 0.422, 0.664, 0.168, 4.577, 1.321], [0.388, -1.877, 0.715, 2.729, 0.147, 1.176], [-1.044, -1.126, 0.648, 0.431, 1.546, 1.277], [-1.081, 2.203, 0.66, 0.386, 0.403, 0.882]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_87_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_87_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.997112, 0.02462, 0.071841], [-0.04661, 0.548461, -0.834876], [-0.059957, -0.835814, -0.545729]]; the translation vector: [4.834615, 3.436689, 1.398379], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.406, -0.499, 0.127, 1.547, 0.014, 1.403], [1.954, 0.044, 0.911, 0.502, 4.499, 1.21], [0.82, -1.82, 0.463, 3.057, 0.123, 1.62], [-1.403, -1.396, 1.129, 0.78, 1.058, 1.608], [-1.077, 2.261, 0.337, 0.558, -0.028, 0.985]]\nB: [[-1.494, -0.876, 0.44, 1.936, 0.339, 1.286], [1.687, 0.61, 0.241, 0.363, 4.812, 0.869], [0.73, -1.681, 0.677, 2.541, -0.283, 1.252], [-1.274, -1.409, 0.377, 0.698, 1.296, 0.85], [-1.137, 2.544, 0.383, 0.033, 0.761, 1.018]]\nC: [[-2.268, -0.745, 0.912, 1.628, 0.264, 0.904], [1.958, 0.486, 0.503, -0.211, 4.549, 1.566], [-0.072, -2.097, 0.667, 2.893, 0.559, 1.549], [-0.724, -1.085, 0.723, 0.553, 1.77, 1.227], [-0.926, 2.697, 1.076, 0.821, 0.34, 1.204]]\nD: [[-1.791, -0.394, 0.511, 1.684, 0.11, 0.995], [1.786, 0.422, 0.664, 0.168, 4.577, 1.321], [0.388, -1.877, 0.715, 2.729, 0.147, 1.176], [-1.044, -1.126, 0.648, 0.431, 1.546, 1.277], [-1.081, 2.203, 0.66, 0.386, 0.403, 0.882]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.113, 0.087, 0.564, 0.343, 0.527, 0.305], [0.507, 0.467, 0.458, 0.596, 0.504, 0.317], [0.58, 0.988, 0.644, 0.601, 0.651, 0.477], [0.182, 1.04, 0.677, 0.777, 0.505, 0.512], [1.732, 0.733, 0.527, 0.634, 0.573, 0.263], [1.609, 1.049, 0.659, 0.686, 0.387, 0.426]]\nB: [[0.129, 0.521, 0.187, 0.313, 0.856, 0.592], [0.981, 0.918, 0.313, 0.429, 0.812, 0.551], [0.233, 0.816, 0.228, 0.26, 0.574, 0.165], [-0.257, 0.76, 1.031, 0.337, 0.304, 1.005], [1.703, 1.1, 0.991, 1.058, 0.84, 0.596], [1.167, 0.943, 0.538, 0.487, 0.187, 0.143]]\nC: [[0.577, 0.356, 0.8, 0.107, 0.25, -0.032], [0.156, 0.937, 0.399, 0.676, 0.726, 0.633], [0.215, 0.658, 0.629, 0.763, 0.937, 0.472], [0.377, 0.594, 0.698, 1.038, 0.047, 0.378], [1.421, 1.109, 0.213, 0.954, 0.857, -0.124], [1.144, 1.512, 0.746, 0.326, 0.254, -0.001]]\nD: [[-0.375, 0.568, 0.757, 0.525, 0.71, 0.684], [0.596, 0.141, 0.679, 0.896, 0.714, 0.623], [0.506, 1.007, 0.844, 0.63, 0.899, 0.696], [-0.281, 1.187, 1.15, 1.186, 0.539, 1.005], [1.823, 0.702, 0.5, 0.724, 0.202, 0.553], [1.882, 1.516, 0.881, 1.085, 0.712, 0.444]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_88_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_88_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the pillow in the scene. The camera pose information includes: the rotation matrix: [[-0.971613, -0.06682, 0.226943], [-0.235147, 0.378036, -0.89543], [-0.02596, -0.923376, -0.383017]]; the translation vector: [2.775299, 4.618156, 1.427592], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.113, 0.087, 0.564, 0.343, 0.527, 0.305], [0.507, 0.467, 0.458, 0.596, 0.504, 0.317], [0.58, 0.988, 0.644, 0.601, 0.651, 0.477], [0.182, 1.04, 0.677, 0.777, 0.505, 0.512], [1.732, 0.733, 0.527, 0.634, 0.573, 0.263], [1.609, 1.049, 0.659, 0.686, 0.387, 0.426]]\nB: [[0.129, 0.521, 0.187, 0.313, 0.856, 0.592], [0.981, 0.918, 0.313, 0.429, 0.812, 0.551], [0.233, 0.816, 0.228, 0.26, 0.574, 0.165], [-0.257, 0.76, 1.031, 0.337, 0.304, 1.005], [1.703, 1.1, 0.991, 1.058, 0.84, 0.596], [1.167, 0.943, 0.538, 0.487, 0.187, 0.143]]\nC: [[0.577, 0.356, 0.8, 0.107, 0.25, -0.032], [0.156, 0.937, 0.399, 0.676, 0.726, 0.633], [0.215, 0.658, 0.629, 0.763, 0.937, 0.472], [0.377, 0.594, 0.698, 1.038, 0.047, 0.378], [1.421, 1.109, 0.213, 0.954, 0.857, -0.124], [1.144, 1.512, 0.746, 0.326, 0.254, -0.001]]\nD: [[-0.375, 0.568, 0.757, 0.525, 0.71, 0.684], [0.596, 0.141, 0.679, 0.896, 0.714, 0.623], [0.506, 1.007, 0.844, 0.63, 0.899, 0.696], [-0.281, 1.187, 1.15, 1.186, 0.539, 1.005], [1.823, 0.702, 0.5, 0.724, 0.202, 0.553], [1.882, 1.516, 0.881, 1.085, 0.712, 0.444]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.081, -2.379, 1.113, 0.296, 0.447, 1.93], [-2.179, -2.328, 1.113, 1.155, -0.055, 2.191], [0.187, -2.593, 1.0, 1.525, -0.011, 1.61]]\nB: [[0.156, -1.688, 0.672, 1.099, 0.512, 1.787], [-1.601, -2.386, 1.059, 0.494, 0.257, 2.331], [1.089, -2.9, 1.408, 0.896, 0.19, 1.392]]\nC: [[-0.153, -1.917, 0.934, 0.637, 0.572, 1.999], [-2.071, -2.511, 0.942, 0.893, 0.199, 2.089], [0.673, -2.564, 1.392, 1.046, 0.131, 1.552]]\nD: [[0.141, -1.904, 0.579, 0.681, 0.731, 2.01], [-1.907, -2.369, 0.924, 0.512, 0.574, 2.053], [0.733, -2.632, 1.326, 0.995, 0.386, 1.64]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_89_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_89_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[-0.086843, 0.425015, -0.901011], [0.995696, 0.066429, -0.064634], [0.032383, -0.902745, -0.428955]]; the translation vector: [4.261571, 5.85756, 1.66629], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.081, -2.379, 1.113, 0.296, 0.447, 1.93], [-2.179, -2.328, 1.113, 1.155, -0.055, 2.191], [0.187, -2.593, 1.0, 1.525, -0.011, 1.61]]\nB: [[0.156, -1.688, 0.672, 1.099, 0.512, 1.787], [-1.601, -2.386, 1.059, 0.494, 0.257, 2.331], [1.089, -2.9, 1.408, 0.896, 0.19, 1.392]]\nC: [[-0.153, -1.917, 0.934, 0.637, 0.572, 1.999], [-2.071, -2.511, 0.942, 0.893, 0.199, 2.089], [0.673, -2.564, 1.392, 1.046, 0.131, 1.552]]\nD: [[0.141, -1.904, 0.579, 0.681, 0.731, 2.01], [-1.907, -2.369, 0.924, 0.512, 0.574, 2.053], [0.733, -2.632, 1.326, 0.995, 0.386, 1.64]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.066, -4.092, 0.322, 1.809, 0.144, 0.711], [-0.452, -3.998, 0.399, -0.022, -0.207, 0.313]]\nB: [[0.967, -4.137, 0.415, 1.862, 0.896, 0.809], [-0.541, -3.922, 0.752, 0.892, 0.695, 1.151]]\nC: [[0.859, -4.189, 1.178, 1.424, -0.037, 1.276], [-0.399, -4.209, 0.397, 0.399, 0.232, 1.02]]\nD: [[0.733, -4.146, 0.771, 1.808, 0.42, 0.818], [-0.752, -4.266, 0.836, 0.396, 0.285, 0.778]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_90_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_90_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the window in the scene. The camera pose information includes: the rotation matrix: [[0.504428, 0.479717, -0.717931], [0.860003, -0.204862, 0.467362], [0.077124, -0.853173, -0.515896]]; the translation vector: [4.973708, 0.412451, 1.573636], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.066, -4.092, 0.322, 1.809, 0.144, 0.711], [-0.452, -3.998, 0.399, -0.022, -0.207, 0.313]]\nB: [[0.967, -4.137, 0.415, 1.862, 0.896, 0.809], [-0.541, -3.922, 0.752, 0.892, 0.695, 1.151]]\nC: [[0.859, -4.189, 1.178, 1.424, -0.037, 1.276], [-0.399, -4.209, 0.397, 0.399, 0.232, 1.02]]\nD: [[0.733, -4.146, 0.771, 1.808, 0.42, 0.818], [-0.752, -4.266, 0.836, 0.396, 0.285, 0.778]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.356, -1.033, 0.487, 1.053, 0.829, 1.337]]\nB: [[-1.861, -0.729, 0.172, 1.066, 1.25, 1.068]]\nC: [[-2.075, -0.604, 0.467, 1.418, 0.63, 1.288]]\nD: [[-2.244, -1.03, 0.539, 1.266, 0.775, 0.934]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_91_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_91_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the cabinet in the scene. The camera pose information includes: the rotation matrix: [[-0.132001, -0.567775, 0.812532], [-0.991224, 0.069667, -0.112349], [0.007182, -0.820231, -0.571988]]; the translation vector: [2.407685, 4.450429, 1.359714], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.356, -1.033, 0.487, 1.053, 0.829, 1.337]]\nB: [[-1.861, -0.729, 0.172, 1.066, 1.25, 1.068]]\nC: [[-2.075, -0.604, 0.467, 1.418, 0.63, 1.288]]\nD: [[-2.244, -1.03, 0.539, 1.266, 0.775, 0.934]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.569, 0.211, 0.319, 0.687, 0.401, 0.55], [-0.378, 2.451, 0.757, 1.108, 0.785, 1.152], [-0.442, -3.047, 0.599, 0.595, 0.53, 0.698], [-0.671, -2.103, 0.492, 0.589, 0.785, 1.436], [-0.536, -2.312, 0.381, 0.676, 0.927, 0.8], [0.694, -2.162, -0.024, 0.318, 0.238, 1.069], [0.8, -2.531, 0.157, 0.887, 0.472, 0.605], [-0.017, 0.764, 0.766, 0.464, 0.143, 1.084]]\nB: [[-0.14, -0.504, 0.958, 0.996, 0.333, 0.616], [-0.523, 2.406, 0.116, 1.014, 1.032, 0.584], [-1.041, -3.534, 0.221, 1.124, 0.509, 0.64], [-1.178, -1.955, 0.316, 0.454, 0.967, 0.762], [-0.074, -2.655, 0.057, 0.407, 0.341, 0.817], [0.498, -1.8, 0.525, 0.171, 1.003, 0.793], [0.349, -2.636, 0.785, 0.651, 0.822, 0.565], [-0.067, 1.46, 0.267, 0.865, 0.829, 0.524]]\nC: [[0.244, -0.138, 0.489, 0.688, 0.662, 1.02], [-0.663, 2.462, 0.398, 0.618, 0.647, 0.654], [-0.762, -3.211, 0.433, 0.631, 0.73, 0.899], [-0.866, -2.412, 0.459, 0.652, 0.663, 0.995], [-0.182, -2.73, 0.386, 0.664, 0.667, 0.841], [0.386, -2.023, 0.44, 0.586, 0.689, 0.943], [0.543, -2.581, 0.583, 0.445, 0.548, 0.641], [0.339, 1.261, 0.575, 0.571, 0.572, 0.783]]\nD: [[0.09, 0.046, 0.862, 0.335, 0.771, 1.401], [-0.263, 2.607, 0.862, 0.364, 1.092, 0.886], [-1.02, -3.334, 0.931, 1.001, 0.759, 0.875], [-0.888, -2.153, 0.017, 0.223, 0.261, 0.633], [-0.543, -2.555, 0.32, 1.086, 0.816, 0.575], [0.862, -2.2, 0.258, 0.465, 0.987, 0.866], [0.065, -2.865, 0.495, 0.697, 0.945, 0.331], [0.317, 1.592, 1.019, 0.326, 0.876, 0.791]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_92_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_92_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the office chair in the scene. The camera pose information includes: the rotation matrix: [[0.672393, -0.274439, 0.687438], [-0.739855, -0.221079, 0.635404], [-0.022402, -0.935846, -0.351697]]; the translation vector: [3.802358, 2.110255, 1.494557], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.569, 0.211, 0.319, 0.687, 0.401, 0.55], [-0.378, 2.451, 0.757, 1.108, 0.785, 1.152], [-0.442, -3.047, 0.599, 0.595, 0.53, 0.698], [-0.671, -2.103, 0.492, 0.589, 0.785, 1.436], [-0.536, -2.312, 0.381, 0.676, 0.927, 0.8], [0.694, -2.162, -0.024, 0.318, 0.238, 1.069], [0.8, -2.531, 0.157, 0.887, 0.472, 0.605], [-0.017, 0.764, 0.766, 0.464, 0.143, 1.084]]\nB: [[-0.14, -0.504, 0.958, 0.996, 0.333, 0.616], [-0.523, 2.406, 0.116, 1.014, 1.032, 0.584], [-1.041, -3.534, 0.221, 1.124, 0.509, 0.64], [-1.178, -1.955, 0.316, 0.454, 0.967, 0.762], [-0.074, -2.655, 0.057, 0.407, 0.341, 0.817], [0.498, -1.8, 0.525, 0.171, 1.003, 0.793], [0.349, -2.636, 0.785, 0.651, 0.822, 0.565], [-0.067, 1.46, 0.267, 0.865, 0.829, 0.524]]\nC: [[0.244, -0.138, 0.489, 0.688, 0.662, 1.02], [-0.663, 2.462, 0.398, 0.618, 0.647, 0.654], [-0.762, -3.211, 0.433, 0.631, 0.73, 0.899], [-0.866, -2.412, 0.459, 0.652, 0.663, 0.995], [-0.182, -2.73, 0.386, 0.664, 0.667, 0.841], [0.386, -2.023, 0.44, 0.586, 0.689, 0.943], [0.543, -2.581, 0.583, 0.445, 0.548, 0.641], [0.339, 1.261, 0.575, 0.571, 0.572, 0.783]]\nD: [[0.09, 0.046, 0.862, 0.335, 0.771, 1.401], [-0.263, 2.607, 0.862, 0.364, 1.092, 0.886], [-1.02, -3.334, 0.931, 1.001, 0.759, 0.875], [-0.888, -2.153, 0.017, 0.223, 0.261, 0.633], [-0.543, -2.555, 0.32, 1.086, 0.816, 0.575], [0.862, -2.2, 0.258, 0.465, 0.987, 0.866], [0.065, -2.865, 0.495, 0.697, 0.945, 0.331], [0.317, 1.592, 1.019, 0.326, 0.876, 0.791]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.126, -1.376, 0.347, 0.441, 1.044, 0.747]]\nB: [[-0.707, -1.056, 0.436, 0.481, 0.775, 0.862]]\nC: [[-1.072, -0.581, 0.729, 0.634, 0.411, 0.815]]\nD: [[-1.2, -0.714, 0.073, 0.598, 1.239, 1.356]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_93_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_93_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the toilet in the scene. The camera pose information includes: the rotation matrix: [[-0.943065, -0.17817, 0.280864], [-0.332105, 0.550897, -0.765649], [-0.018311, -0.815333, -0.578703]]; the translation vector: [2.74599, 1.673222, 1.294065], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.126, -1.376, 0.347, 0.441, 1.044, 0.747]]\nB: [[-0.707, -1.056, 0.436, 0.481, 0.775, 0.862]]\nC: [[-1.072, -0.581, 0.729, 0.634, 0.411, 0.815]]\nD: [[-1.2, -0.714, 0.073, 0.598, 1.239, 1.356]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.746, -0.676, 1.548, 0.354, 0.507, 0.554], [1.278, -0.21, 2.039, 0.253, 0.159, 0.277], [1.354, -0.174, 2.085, 0.187, 0.25, 0.284], [1.365, 0.302, 2.07, 0.178, 0.146, 0.195], [1.395, 1.775, 0.709, 0.116, 0.082, 0.239], [0.108, -1.232, 0.61, 0.37, 0.243, 0.232]]\nB: [[-2.116, -0.405, 1.974, 0.197, 0.992, 0.793], [1.595, -0.115, 1.898, 0.53, -0.095, 0.207], [1.74, -0.462, 1.811, 0.459, 0.366, 0.195], [1.756, -0.03, 2.139, 0.506, -0.218, -0.14], [1.496, 1.894, 0.22, -0.344, -0.274, 0.329], [0.12, -1.361, 0.247, 0.677, 0.431, 0.41]]\nC: [[-2.099, -0.677, 1.826, 0.111, 0.048, 0.88], [1.179, -0.084, 2.064, 0.353, -0.335, 0.047], [1.283, -0.017, 2.251, 0.548, 0.539, -0.139], [1.054, -0.131, 1.995, -0.052, 0.135, -0.266], [1.813, 1.809, 0.298, 0.268, -0.092, 0.575], [0.507, -1.135, 0.122, 0.102, 0.682, -0.107]]\nD: [[-2.013, -0.781, 2.031, 0.552, 0.053, 0.962], [1.49, 0.048, 1.694, 0.076, -0.303, 0.184], [1.646, 0.043, 2.403, 0.082, 0.014, 0.773], [1.068, 0.187, 2.309, 0.672, -0.201, 0.291], [1.861, 1.412, 0.913, 0.343, -0.022, 0.312], [-0.111, -1.095, 0.386, 0.723, 0.064, 0.108]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_94_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_94_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the object in the scene. The camera pose information includes: the rotation matrix: [[0.493838, -0.420518, 0.76111], [-0.864926, -0.147366, 0.479777], [-0.089593, -0.895236, -0.436493]]; the translation vector: [0.736944, 2.108944, 1.402726], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.746, -0.676, 1.548, 0.354, 0.507, 0.554], [1.278, -0.21, 2.039, 0.253, 0.159, 0.277], [1.354, -0.174, 2.085, 0.187, 0.25, 0.284], [1.365, 0.302, 2.07, 0.178, 0.146, 0.195], [1.395, 1.775, 0.709, 0.116, 0.082, 0.239], [0.108, -1.232, 0.61, 0.37, 0.243, 0.232]]\nB: [[-2.116, -0.405, 1.974, 0.197, 0.992, 0.793], [1.595, -0.115, 1.898, 0.53, -0.095, 0.207], [1.74, -0.462, 1.811, 0.459, 0.366, 0.195], [1.756, -0.03, 2.139, 0.506, -0.218, -0.14], [1.496, 1.894, 0.22, -0.344, -0.274, 0.329], [0.12, -1.361, 0.247, 0.677, 0.431, 0.41]]\nC: [[-2.099, -0.677, 1.826, 0.111, 0.048, 0.88], [1.179, -0.084, 2.064, 0.353, -0.335, 0.047], [1.283, -0.017, 2.251, 0.548, 0.539, -0.139], [1.054, -0.131, 1.995, -0.052, 0.135, -0.266], [1.813, 1.809, 0.298, 0.268, -0.092, 0.575], [0.507, -1.135, 0.122, 0.102, 0.682, -0.107]]\nD: [[-2.013, -0.781, 2.031, 0.552, 0.053, 0.962], [1.49, 0.048, 1.694, 0.076, -0.303, 0.184], [1.646, 0.043, 2.403, 0.082, 0.014, 0.773], [1.068, 0.187, 2.309, 0.672, -0.201, 0.291], [1.861, 1.412, 0.913, 0.343, -0.022, 0.312], [-0.111, -1.095, 0.386, 0.723, 0.064, 0.108]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.095, 0.369, 0.896, 1.864, 0.389, -0.037]]\nB: [[0.821, 1.024, 0.461, 1.589, 1.059, 0.417]]\nC: [[0.235, 0.419, 0.494, 1.232, 0.977, 0.5]]\nD: [[0.531, 0.805, 0.846, 1.569, 0.745, 0.229]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_95_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_95_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the counter in the scene. The camera pose information includes: the rotation matrix: [[0.882784, 0.25224, -0.396318], [0.469583, -0.498211, 0.728888], [-0.013595, -0.829554, -0.55826]]; the translation vector: [3.463734, 1.394934, 1.262723], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.095, 0.369, 0.896, 1.864, 0.389, -0.037]]\nB: [[0.821, 1.024, 0.461, 1.589, 1.059, 0.417]]\nC: [[0.235, 0.419, 0.494, 1.232, 0.977, 0.5]]\nD: [[0.531, 0.805, 0.846, 1.569, 0.745, 0.229]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.71, -0.427, 1.41, -0.084, 0.707, -0.208]]\nB: [[-1.108, -0.854, 1.201, 0.423, 0.471, 0.68]]\nC: [[-1.305, -0.718, 1.12, 0.437, 0.021, 0.653]]\nD: [[-1.106, -0.393, 0.937, 0.241, 0.317, 0.242]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_96_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_96_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the tray in the scene. The camera pose information includes: the rotation matrix: [[-0.998162, -0.007354, -0.06016], [0.055338, 0.294228, -0.954132], [0.024717, -0.955707, -0.293281]]; the translation vector: [1.687981, 4.43329, 1.569003], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.71, -0.427, 1.41, -0.084, 0.707, -0.208]]\nB: [[-1.108, -0.854, 1.201, 0.423, 0.471, 0.68]]\nC: [[-1.305, -0.718, 1.12, 0.437, 0.021, 0.653]]\nD: [[-1.106, -0.393, 0.937, 0.241, 0.317, 0.242]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.882, 1.56, 0.388, 0.537, 1.676, 0.722], [-0.93, 1.385, 0.286, 0.589, 0.589, 0.521], [-0.937, -1.858, 0.442, 0.583, 0.58, 0.542]]\nB: [[1.943, 1.267, 0.682, 0.711, 1.577, 0.374], [-1.208, 1.812, -0.196, 1.059, 0.169, 0.521], [-1.321, -1.601, 0.071, 0.85, 0.083, 0.59]]\nC: [[2.195, 1.182, 0.758, 0.43, 1.952, 0.35], [-1.23, 1.71, 0.54, 0.173, 0.389, 0.39], [-0.765, -1.788, 0.133, 0.882, 0.65, 0.803]]\nD: [[1.615, 1.264, -0.077, 0.87, 1.187, 0.662], [-0.905, 1.561, 0.641, 0.894, 0.612, 0.112], [-0.628, -2.319, 0.352, 0.102, 0.924, 0.919]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_97_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_97_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the table in the scene. The camera pose information includes: the rotation matrix: [[-0.530794, 0.426739, -0.732224], [0.841151, 0.159702, -0.516681], [-0.10355, -0.890162, -0.443721]]; the translation vector: [5.418979, 4.373359, 1.385162], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.882, 1.56, 0.388, 0.537, 1.676, 0.722], [-0.93, 1.385, 0.286, 0.589, 0.589, 0.521], [-0.937, -1.858, 0.442, 0.583, 0.58, 0.542]]\nB: [[1.943, 1.267, 0.682, 0.711, 1.577, 0.374], [-1.208, 1.812, -0.196, 1.059, 0.169, 0.521], [-1.321, -1.601, 0.071, 0.85, 0.083, 0.59]]\nC: [[2.195, 1.182, 0.758, 0.43, 1.952, 0.35], [-1.23, 1.71, 0.54, 0.173, 0.389, 0.39], [-0.765, -1.788, 0.133, 0.882, 0.65, 0.803]]\nD: [[1.615, 1.264, -0.077, 0.87, 1.187, 0.662], [-0.905, 1.561, 0.641, 0.894, 0.612, 0.112], [-0.628, -2.319, 0.352, 0.102, 0.924, 0.919]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.419, -1.385, 0.468, 0.603, 0.548, -0.002]]\nB: [[0.568, -0.742, 0.118, 0.677, 0.387, 0.514]]\nC: [[0.186, -1.693, 0.012, 1.09, 0.395, 0.456]]\nD: [[0.461, -1.208, 0.23, 0.711, 0.358, 0.459]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_98_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_98_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the toilet in the scene. The camera pose information includes: the rotation matrix: [[0.695296, -0.421579, 0.582095], [-0.717067, -0.351947, 0.601622], [-0.048765, -0.835707, -0.547007]]; the translation vector: [2.470866, 0.652559, 1.473924], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.419, -1.385, 0.468, 0.603, 0.548, -0.002]]\nB: [[0.568, -0.742, 0.118, 0.677, 0.387, 0.514]]\nC: [[0.186, -1.693, 0.012, 1.09, 0.395, 0.456]]\nD: [[0.461, -1.208, 0.23, 0.711, 0.358, 0.459]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.786, 0.016, 0.222, 1.217, 0.653, 0.431]]\nB: [[-1.037, -0.227, 0.31, 1.564, 0.876, 0.857]]\nC: [[-1.111, -0.46, 0.292, 1.65, 0.975, 0.105]]\nD: [[-0.725, -0.136, -0.167, 0.877, 0.479, 0.631]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_99_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_99_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the coffee table in the scene. The camera pose information includes: the rotation matrix: [[0.408988, -0.323891, 0.853126], [-0.912443, -0.158736, 0.37716], [0.013263, -0.932683, -0.360453]]; the translation vector: [3.672612, 2.990265, 1.494339], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.786, 0.016, 0.222, 1.217, 0.653, 0.431]]\nB: [[-1.037, -0.227, 0.31, 1.564, 0.876, 0.857]]\nC: [[-1.111, -0.46, 0.292, 1.65, 0.975, 0.105]]\nD: [[-0.725, -0.136, -0.167, 0.877, 0.479, 0.631]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.78, -0.369, 0.924, 0.56, 5.207, 1.466], [-1.469, 1.84, 1.529, 0.1, 3.46, 1.026], [1.603, 3.517, 0.975, 0.924, 0.045, 0.875], [-1.453, 3.239, 1.664, 0.75, -0.019, 1.939], [-1.132, -0.282, 1.01, 0.6, 1.619, 1.743], [-0.943, -1.737, 1.065, 0.938, -0.129, 1.932], [-0.462, -1.272, 0.976, 0.13, 0.263, 2.111], [-0.647, -3.37, 0.86, -0.005, 1.127, 1.632], [0.759, -3.788, 0.905, 0.472, 0.723, 1.787], [1.365, -2.765, 1.105, 0.336, 0.467, 1.277]]\nB: [[1.074, -0.516, 1.118, -0.086, 5.725, 2.158], [-1.396, 2.343, 1.47, -0.008, 3.692, 1.57], [1.315, 3.398, 1.101, 1.035, 0.612, 0.867], [-0.97, 3.198, 1.01, 0.853, 0.483, 1.503], [-1.961, -0.579, 0.799, 0.47, 0.959, 1.354], [-0.792, -1.093, 0.831, 0.98, 0.15, 1.422], [-0.764, -1.052, 0.538, 0.169, -0.099, 1.83], [-0.948, -3.534, 0.813, 0.512, 1.974, 2.262], [1.309, -3.86, 1.13, 0.074, 1.177, 0.95], [0.842, -2.685, 1.111, 0.225, 0.622, 1.342]]\nC: [[1.398, -0.078, 0.847, 0.238, 5.699, 1.741], [-1.453, 1.912, 1.74, 0.206, 3.243, 1.354], [1.514, 3.636, 0.972, 1.079, 0.266, 0.762], [-1.064, 3.584, 1.382, 0.689, 0.248, 1.654], [-1.552, -0.739, 0.879, 0.227, 1.257, 1.692], [-1.211, -1.342, 0.86, 0.655, 0.096, 1.73], [-0.902, -1.484, 0.9, 0.087, 0.331, 1.816], [-0.874, -3.114, 1.006, 0.184, 1.508, 2.084], [0.921, -3.404, 0.668, 0.136, 1.137, 1.434], [1.157, -2.863, 0.703, 0.531, 0.128, 1.521]]\nD: [[1.025, -0.536, 0.699, 0.592, 5.958, 2.064], [-1.605, 1.792, 2.153, -0.235, 3.185, 1.084], [1.02, 3.68, 1.082, 1.526, 0.082, 0.582], [-1.08, 3.95, 0.986, 0.299, -0.139, 1.856], [-1.893, -0.998, 0.689, 0.259, 1.727, 1.918], [-1.034, -1.551, 0.605, 0.948, 0.46, 1.541], [-1.095, -1.908, 1.355, 0.164, 0.298, 1.555], [-0.914, -3.165, 0.928, -0.077, 1.779, 1.639], [0.568, -3.209, 0.575, 0.598, 1.246, 1.226], [1.226, -3.252, 0.43, 0.831, 0.263, 1.38]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_100_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_100_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.52463, -0.231347, 0.819293], [-0.850589, 0.102279, -0.515789], [0.03553, -0.96748, -0.25044]]; the translation vector: [5.897326, 2.792535, 1.553822], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.78, -0.369, 0.924, 0.56, 5.207, 1.466], [-1.469, 1.84, 1.529, 0.1, 3.46, 1.026], [1.603, 3.517, 0.975, 0.924, 0.045, 0.875], [-1.453, 3.239, 1.664, 0.75, -0.019, 1.939], [-1.132, -0.282, 1.01, 0.6, 1.619, 1.743], [-0.943, -1.737, 1.065, 0.938, -0.129, 1.932], [-0.462, -1.272, 0.976, 0.13, 0.263, 2.111], [-0.647, -3.37, 0.86, -0.005, 1.127, 1.632], [0.759, -3.788, 0.905, 0.472, 0.723, 1.787], [1.365, -2.765, 1.105, 0.336, 0.467, 1.277]]\nB: [[1.074, -0.516, 1.118, -0.086, 5.725, 2.158], [-1.396, 2.343, 1.47, -0.008, 3.692, 1.57], [1.315, 3.398, 1.101, 1.035, 0.612, 0.867], [-0.97, 3.198, 1.01, 0.853, 0.483, 1.503], [-1.961, -0.579, 0.799, 0.47, 0.959, 1.354], [-0.792, -1.093, 0.831, 0.98, 0.15, 1.422], [-0.764, -1.052, 0.538, 0.169, -0.099, 1.83], [-0.948, -3.534, 0.813, 0.512, 1.974, 2.262], [1.309, -3.86, 1.13, 0.074, 1.177, 0.95], [0.842, -2.685, 1.111, 0.225, 0.622, 1.342]]\nC: [[1.398, -0.078, 0.847, 0.238, 5.699, 1.741], [-1.453, 1.912, 1.74, 0.206, 3.243, 1.354], [1.514, 3.636, 0.972, 1.079, 0.266, 0.762], [-1.064, 3.584, 1.382, 0.689, 0.248, 1.654], [-1.552, -0.739, 0.879, 0.227, 1.257, 1.692], [-1.211, -1.342, 0.86, 0.655, 0.096, 1.73], [-0.902, -1.484, 0.9, 0.087, 0.331, 1.816], [-0.874, -3.114, 1.006, 0.184, 1.508, 2.084], [0.921, -3.404, 0.668, 0.136, 1.137, 1.434], [1.157, -2.863, 0.703, 0.531, 0.128, 1.521]]\nD: [[1.025, -0.536, 0.699, 0.592, 5.958, 2.064], [-1.605, 1.792, 2.153, -0.235, 3.185, 1.084], [1.02, 3.68, 1.082, 1.526, 0.082, 0.582], [-1.08, 3.95, 0.986, 0.299, -0.139, 1.856], [-1.893, -0.998, 0.689, 0.259, 1.727, 1.918], [-1.034, -1.551, 0.605, 0.948, 0.46, 1.541], [-1.095, -1.908, 1.355, 0.164, 0.298, 1.555], [-0.914, -3.165, 0.928, -0.077, 1.779, 1.639], [0.568, -3.209, 0.575, 0.598, 1.246, 1.226], [1.226, -3.252, 0.43, 0.831, 0.263, 1.38]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.346, -1.632, 0.468, 0.228, 0.435, 0.463], [1.166, -1.379, 0.353, 0.04, 0.901, -0.25], [1.058, -1.308, -0.427, 0.416, 0.148, -0.261], [1.588, -1.671, -0.302, 0.296, 0.611, 0.478], [1.29, -1.513, 0.294, 0.468, 0.683, 0.487]]\nB: [[1.331, -1.83, 0.338, 0.317, 0.297, 0.192], [1.086, -1.365, 0.034, 0.38, 0.508, 0.129], [1.22, -1.567, 0.058, 0.382, 0.375, 0.145], [1.153, -2.04, 0.055, 0.29, 0.371, 0.11], [1.391, -1.481, 0.041, 0.386, 0.621, 0.13]]\nC: [[1.118, -1.374, 0.329, -0.089, 0.113, 0.27], [1.322, -1.418, -0.243, 0.677, 0.961, -0.031], [1.042, -1.495, -0.402, 0.189, 0.317, 0.229], [1.027, -2.005, 0.379, 0.337, 0.077, -0.062], [1.617, -1.294, 0.41, -0.08, 0.836, 0.171]]\nD: [[1.601, -1.491, 0.468, 0.181, 0.51, -0.093], [0.965, -1.654, 0.463, 0.875, 0.478, 0.252], [1.604, -1.87, -0.185, 0.098, 0.676, 0.612], [1.637, -2.272, -0.12, 0.307, 0.185, 0.124], [1.563, -1.727, 0.204, 0.781, 0.373, 0.021]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_101_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_101_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shoes in the scene. The camera pose information includes: the rotation matrix: [[-0.079656, -0.319192, 0.944337], [-0.994012, 0.096527, -0.051219], [-0.074805, -0.942762, -0.324969]]; the translation vector: [4.3352, 2.935251, 1.464921], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.346, -1.632, 0.468, 0.228, 0.435, 0.463], [1.166, -1.379, 0.353, 0.04, 0.901, -0.25], [1.058, -1.308, -0.427, 0.416, 0.148, -0.261], [1.588, -1.671, -0.302, 0.296, 0.611, 0.478], [1.29, -1.513, 0.294, 0.468, 0.683, 0.487]]\nB: [[1.331, -1.83, 0.338, 0.317, 0.297, 0.192], [1.086, -1.365, 0.034, 0.38, 0.508, 0.129], [1.22, -1.567, 0.058, 0.382, 0.375, 0.145], [1.153, -2.04, 0.055, 0.29, 0.371, 0.11], [1.391, -1.481, 0.041, 0.386, 0.621, 0.13]]\nC: [[1.118, -1.374, 0.329, -0.089, 0.113, 0.27], [1.322, -1.418, -0.243, 0.677, 0.961, -0.031], [1.042, -1.495, -0.402, 0.189, 0.317, 0.229], [1.027, -2.005, 0.379, 0.337, 0.077, -0.062], [1.617, -1.294, 0.41, -0.08, 0.836, 0.171]]\nD: [[1.601, -1.491, 0.468, 0.181, 0.51, -0.093], [0.965, -1.654, 0.463, 0.875, 0.478, 0.252], [1.604, -1.87, -0.185, 0.098, 0.676, 0.612], [1.637, -2.272, -0.12, 0.307, 0.185, 0.124], [1.563, -1.727, 0.204, 0.781, 0.373, 0.021]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.756, -0.319, 1.042, 0.429, 0.322, 0.448]]\nB: [[-0.968, 0.035, 0.911, 0.64, 0.607, 0.13]]\nC: [[-0.447, -0.667, 1.37, -0.048, 0.547, 0.66]]\nD: [[-0.868, -0.191, 0.853, 0.715, 0.451, 0.897]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_102_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_102_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the coffee maker in the scene. The camera pose information includes: the rotation matrix: [[-0.848489, -0.131122, 0.512712], [-0.527579, 0.133483, -0.838954], [0.041567, -0.982339, -0.182436]]; the translation vector: [2.702568, 1.718074, 1.602473], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.756, -0.319, 1.042, 0.429, 0.322, 0.448]]\nB: [[-0.968, 0.035, 0.911, 0.64, 0.607, 0.13]]\nC: [[-0.447, -0.667, 1.37, -0.048, 0.547, 0.66]]\nD: [[-0.868, -0.191, 0.853, 0.715, 0.451, 0.897]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.912, -2.332, 1.905, 0.812, 1.477, 1.176], [1.567, 2.82, 1.32, 0.327, 1.345, 2.178]]\nB: [[1.7, -2.606, 1.565, 0.678, 1.073, 1.573], [1.645, 3.122, 1.233, 0.724, 1.059, 2.428]]\nC: [[2.101, -2.524, 1.207, 0.883, 0.819, 1.727], [1.696, 3.351, 1.474, 0.479, 1.235, 2.058]]\nD: [[1.681, -2.688, 1.507, 0.221, 0.833, 1.776], [1.4, 2.653, 1.693, 1.075, 1.288, 2.071]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_103_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_103_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the cabinet in the scene. The camera pose information includes: the rotation matrix: [[0.606497, 0.359513, -0.709163], [0.793947, -0.321582, 0.515978], [-0.042553, -0.875977, -0.480473]]; the translation vector: [5.898605, 1.464963, 1.329018], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.912, -2.332, 1.905, 0.812, 1.477, 1.176], [1.567, 2.82, 1.32, 0.327, 1.345, 2.178]]\nB: [[1.7, -2.606, 1.565, 0.678, 1.073, 1.573], [1.645, 3.122, 1.233, 0.724, 1.059, 2.428]]\nC: [[2.101, -2.524, 1.207, 0.883, 0.819, 1.727], [1.696, 3.351, 1.474, 0.479, 1.235, 2.058]]\nD: [[1.681, -2.688, 1.507, 0.221, 0.833, 1.776], [1.4, 2.653, 1.693, 1.075, 1.288, 2.071]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.693, -1.201, 1.187, 0.828, 0.813, 1.996]]\nB: [[-1.283, -1.49, 1.157, 1.223, 0.635, 2.337]]\nC: [[-1.607, -1.608, 0.733, 1.415, 0.912, 2.422]]\nD: [[-1.367, -1.969, 1.373, 1.253, 1.096, 1.909]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_104_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_104_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the cabinets in the scene. The camera pose information includes: the rotation matrix: [[0.349467, 0.022881, -0.936669], [0.936944, -0.011774, 0.349282], [-0.003037, -0.999669, -0.025553]]; the translation vector: [3.08553, 2.787215, 1.609269], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.693, -1.201, 1.187, 0.828, 0.813, 1.996]]\nB: [[-1.283, -1.49, 1.157, 1.223, 0.635, 2.337]]\nC: [[-1.607, -1.608, 0.733, 1.415, 0.912, 2.422]]\nD: [[-1.367, -1.969, 1.373, 1.253, 1.096, 1.909]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.858, -0.632, 0.828, 0.126, 1.643, 1.687], [-1.33, 0.028, 0.915, 0.226, 2.888, 1.864], [-0.174, -1.42, 0.865, 2.224, 0.121, 1.722], [0.61, 1.413, 0.874, 4.003, 0.17, 1.77], [2.563, 1.11, 0.788, 0.118, 0.484, 1.649]]\nB: [[1.405, -0.208, 0.598, -0.114, 2.093, 1.602], [-1.061, 0.394, 1.019, -0.16, 3.193, 1.369], [-0.359, -0.986, 0.414, 1.802, -0.111, 1.429], [1.035, 1.154, 1.154, 3.812, 0.204, 2.113], [2.12, 1.579, 1.171, -0.054, 0.234, 1.478]]\nC: [[1.89, -0.153, 0.406, 0.028, 1.816, 1.93], [-1.451, -0.417, 1.393, -0.113, 3.307, 1.683], [-0.295, -1.25, 0.577, 1.985, -0.098, 1.447], [0.348, 1.382, 0.753, 3.885, 0.441, 1.993], [2.183, 0.625, 0.617, 0.117, 0.723, 1.324]]\nD: [[2.301, -0.62, 1.122, 0.26, 2.124, 2.126], [-0.834, -0.412, 1.071, -0.118, 2.484, 1.498], [-0.094, -1.494, 0.531, 2.098, -0.018, 2.208], [0.226, 1.164, 1.047, 4.422, 0.121, 1.595], [2.644, 1.556, 0.635, 0.354, 0.125, 1.662]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_105_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_105_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.991592, 0.052224, -0.118397], [0.1292, -0.348306, 0.928435], [0.007248, -0.935925, -0.352124]]; the translation vector: [2.177373, 2.142725, 1.46728], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.858, -0.632, 0.828, 0.126, 1.643, 1.687], [-1.33, 0.028, 0.915, 0.226, 2.888, 1.864], [-0.174, -1.42, 0.865, 2.224, 0.121, 1.722], [0.61, 1.413, 0.874, 4.003, 0.17, 1.77], [2.563, 1.11, 0.788, 0.118, 0.484, 1.649]]\nB: [[1.405, -0.208, 0.598, -0.114, 2.093, 1.602], [-1.061, 0.394, 1.019, -0.16, 3.193, 1.369], [-0.359, -0.986, 0.414, 1.802, -0.111, 1.429], [1.035, 1.154, 1.154, 3.812, 0.204, 2.113], [2.12, 1.579, 1.171, -0.054, 0.234, 1.478]]\nC: [[1.89, -0.153, 0.406, 0.028, 1.816, 1.93], [-1.451, -0.417, 1.393, -0.113, 3.307, 1.683], [-0.295, -1.25, 0.577, 1.985, -0.098, 1.447], [0.348, 1.382, 0.753, 3.885, 0.441, 1.993], [2.183, 0.625, 0.617, 0.117, 0.723, 1.324]]\nD: [[2.301, -0.62, 1.122, 0.26, 2.124, 2.126], [-0.834, -0.412, 1.071, -0.118, 2.484, 1.498], [-0.094, -1.494, 0.531, 2.098, -0.018, 2.208], [0.226, 1.164, 1.047, 4.422, 0.121, 1.595], [2.644, 1.556, 0.635, 0.354, 0.125, 1.662]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.401, -1.054, 0.005, 0.193, -0.189, 0.608], [-1.764, -0.727, 0.562, 0.549, 0.36, 0.374], [-2.181, 0.328, -0.167, 0.351, 0.064, 0.119]]\nB: [[1.152, -0.296, 0.418, 0.864, 0.385, 0.356], [-2.324, -0.32, 0.424, 0.485, 0.66, -0.082], [-1.955, 0.121, 0.148, 0.369, 0.415, 0.131]]\nC: [[1.282, -0.743, 0.129, 0.493, 0.257, 0.293], [-1.968, -0.763, 0.156, 0.467, 0.241, 0.31], [-1.95, 0.267, 0.16, 0.231, 0.318, 0.302]]\nD: [[1.109, -0.73, -0.038, 0.564, 0.587, 0.172], [-2.259, -0.589, 0.46, 0.771, -0.144, -0.09], [-1.478, 0.494, 0.535, 0.374, 0.223, 0.643]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_106_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_106_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the trash can in the scene. The camera pose information includes: the rotation matrix: [[-0.789457, 0.162095, -0.592016], [0.613764, 0.197318, -0.764434], [-0.007096, -0.966846, -0.255262]]; the translation vector: [5.114759, 3.17533, 1.386193], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.401, -1.054, 0.005, 0.193, -0.189, 0.608], [-1.764, -0.727, 0.562, 0.549, 0.36, 0.374], [-2.181, 0.328, -0.167, 0.351, 0.064, 0.119]]\nB: [[1.152, -0.296, 0.418, 0.864, 0.385, 0.356], [-2.324, -0.32, 0.424, 0.485, 0.66, -0.082], [-1.955, 0.121, 0.148, 0.369, 0.415, 0.131]]\nC: [[1.282, -0.743, 0.129, 0.493, 0.257, 0.293], [-1.968, -0.763, 0.156, 0.467, 0.241, 0.31], [-1.95, 0.267, 0.16, 0.231, 0.318, 0.302]]\nD: [[1.109, -0.73, -0.038, 0.564, 0.587, 0.172], [-2.259, -0.589, 0.46, 0.771, -0.144, -0.09], [-1.478, 0.494, 0.535, 0.374, 0.223, 0.643]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.628, -0.574, 0.244, 0.629, 0.377, 0.613]]\nB: [[-0.255, -0.118, 0.331, 1.064, 0.829, 0.169]]\nC: [[-0.907, -0.799, 0.595, 1.106, -0.043, 0.376]]\nD: [[-0.149, -0.775, 0.103, 0.329, 0.393, 1.004]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_107_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_107_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the toilet in the scene. The camera pose information includes: the rotation matrix: [[-0.881415, -0.308012, 0.3581], [-0.47008, 0.646119, -0.601294], [-0.046169, -0.698325, -0.71429]]; the translation vector: [3.147524, 1.689608, 1.273114], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.628, -0.574, 0.244, 0.629, 0.377, 0.613]]\nB: [[-0.255, -0.118, 0.331, 1.064, 0.829, 0.169]]\nC: [[-0.907, -0.799, 0.595, 1.106, -0.043, 0.376]]\nD: [[-0.149, -0.775, 0.103, 0.329, 0.393, 1.004]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.984, -0.791, 0.71, -0.025, 0.146, 1.95], [-2.845, 0.826, 0.79, -0.018, 2.045, 2.096], [-3.127, 1.767, 1.058, 0.668, 0.751, 1.034], [-2.598, 2.204, 1.456, 0.337, 0.226, 0.622], [-1.681, 2.399, 0.33, 0.897, -0.155, 1.345], [-3.032, -0.879, 1.701, 0.543, 0.324, 1.619], [-2.871, -1.012, 1.112, 0.372, 0.525, 1.953], [-1.989, -1.505, 1.05, 1.013, 0.57, 2.119], [-2.342, -1.032, 1.573, 0.099, 0.665, 2.113], [-1.812, -1.549, 0.936, 0.03, 0.702, 1.331], [-0.897, -1.334, 1.032, -0.375, 0.838, 1.222], [1.073, -1.243, 1.222, 0.074, 0.103, 1.889], [0.684, -1.361, 0.842, 0.81, 0.134, 2.122], [1.681, -0.985, 0.915, -0.15, 0.046, 2.017], [1.499, -1.143, 1.225, 1.125, 0.255, 2.053], [2.518, -0.611, 1.251, 0.147, 0.376, 1.463], [2.78, -0.555, 1.623, 0.321, 0.5, 1.453], [2.607, 0.422, 0.93, 0.428, 2.768, 1.865], [2.929, 2.246, 1.615, 0.791, 0.45, 0.84], [2.894, 2.607, 0.679, 0.418, 0.185, 1.771], [1.91, 1.95, 0.631, 1.792, 0.498, 2.141], [0.968, 2.657, -0.02, 0.142, 0.513, 1.049], [2.739, 1.468, 0.752, 0.297, 1.124, 0.649], [1.999, -0.528, 0.795, 0.19, 0.625, 0.528]]\nB: [[-1.697, -1.101, 1.32, 0.519, 0.607, 2.31], [-2.793, 0.346, 1.419, 0.299, 1.423, 2.149], [-2.764, 1.638, 1.178, 0.604, 0.448, 0.518], [-2.94, 2.09, 1.372, -0.13, -0.272, 0.296], [-1.734, 2.804, 0.582, 1.396, 0.541, 0.939], [-2.549, -0.196, 1.12, 0.785, 0.411, 1.926], [-2.871, -1.014, 0.799, 0.56, 0.597, 1.935], [-2.659, -0.762, 1.356, 0.825, 0.021, 2.649], [-1.977, -1.011, 1.131, 0.465, 0.035, 2.324], [-1.526, -1.598, 1.392, 0.441, -0.118, 2.102], [-1.353, -0.868, 0.591, 0.125, 0.493, 1.476], [1.173, -1.254, 0.599, -0.335, 0.938, 1.499], [1.444, -1.618, 1.332, 0.376, 0.369, 1.68], [1.582, -1.255, 0.456, -0.034, -0.048, 2.138], [2.452, -1.152, 1.16, 0.41, -0.305, 2.162], [2.246, -1.101, 0.993, 0.065, 0.725, 2.256], [2.414, -0.99, 1.12, 0.836, 0.744, 1.026], [2.607, 0.594, 0.728, -0.103, 2.445, 1.796], [2.075, 1.78, 1.433, 0.826, 1.27, 1.569], [2.842, 2.47, 1.179, 0.437, 0.717, 1.714], [2.073, 1.959, 0.513, 1.293, -0.057, 1.28], [0.684, 2.546, 0.647, 0.281, 0.423, 0.403], [1.985, 2.256, 0.609, 0.323, 0.304, 0.186], [1.904, -0.439, 0.116, 0.205, 0.913, 1.076]]\nC: [[-0.882, -0.996, 0.828, 0.066, -0.03, 2.259], [-2.906, 0.486, 0.584, 0.338, 1.448, 2.228], [-2.335, 1.79, 1.402, 0.799, 0.604, 0.979], [-2.492, 2.696, 0.852, 0.385, -0.119, 0.551], [-2.113, 2.369, 0.634, 1.634, -0.378, 1.33], [-2.9, -0.382, 1.544, 0.229, 0.561, 1.896], [-2.46, -0.938, 0.92, 0.562, 0.836, 1.812], [-2.236, -1.122, 1.385, 0.806, -0.301, 1.756], [-1.859, -1.439, 0.978, -0.087, 0.007, 2.232], [-1.477, -1.605, 1.119, -0.203, 0.225, 1.352], [-0.558, -1.702, 0.427, 0.133, 0.668, 1.46], [0.818, -0.885, 1.161, 0.455, 0.101, 1.667], [0.552, -1.308, 0.707, 0.978, 0.615, 1.676], [2.109, -1.305, 1.008, -0.007, 0.224, 2.013], [2.016, -1.577, 1.004, 0.572, 0.061, 2.141], [1.754, -1.027, 1.286, 0.147, 0.165, 1.509], [2.849, -0.613, 0.987, 0.617, 1.099, 1.162], [2.281, 0.428, 1.287, 0.612, 2.792, 1.8], [2.1, 1.909, 1.627, 0.042, 0.641, 1.338], [2.025, 1.994, 0.97, 0.816, 0.372, 1.93], [1.678, 2.705, 1.241, 1.93, -0.063, 1.837], [0.582, 2.314, 0.279, 0.554, 0.013, 1.151], [2.245, 1.504, 0.631, 0.05, 1.008, 1.066], [2.09, -0.514, 0.622, -0.006, 1.061, 1.06]]\nD: [[-1.212, -1.13, 1.017, 0.465, 0.161, 2.011], [-2.56, 0.64, 0.971, 0.201, 1.804, 1.935], [-2.744, 1.914, 1.197, 0.349, 0.771, 0.66], [-2.606, 2.363, 1.087, 0.038, 0.219, 0.424], [-1.931, 2.472, 0.667, 1.366, 0.094, 1.255], [-2.729, -0.603, 1.38, 0.39, 0.792, 1.541], [-2.531, -0.93, 1.084, 0.175, 0.507, 2.172], [-2.227, -1.13, 1.087, 0.723, 0.142, 2.167], [-1.887, -1.279, 1.074, 0.181, 0.413, 2.133], [-1.395, -1.301, 1.124, 0.117, 0.289, 1.814], [-0.99, -1.313, 0.763, 0.122, 0.477, 1.511], [0.768, -1.372, 0.865, 0.144, 0.573, 1.696], [0.958, -1.124, 0.866, 0.51, 0.163, 1.704], [1.687, -1.284, 0.89, 0.172, 0.422, 1.772], [1.97, -1.137, 0.897, 0.705, 0.139, 1.81], [2.237, -1.017, 0.895, 0.302, 0.335, 1.807], [2.506, -0.615, 1.189, 0.456, 0.783, 1.228], [2.295, 0.463, 0.865, 0.248, 2.384, 1.746], [2.549, 1.9, 1.178, 0.43, 0.874, 1.13], [2.396, 2.329, 0.87, 0.323, 0.269, 1.739], [1.621, 2.45, 0.875, 1.651, 0.189, 1.735], [0.789, 2.563, 0.425, 0.113, 0.177, 0.782], [2.336, 1.911, 0.338, 0.211, 0.688, 0.678], [2.287, -0.576, 0.338, 0.14, 0.728, 0.71]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_108_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_108_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.731293, 0.384445, -0.563394], [0.682011, 0.401944, -0.610984], [-0.008437, -0.831049, -0.556135]]; the translation vector: [5.176627, 2.209938, 1.427488], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.984, -0.791, 0.71, -0.025, 0.146, 1.95], [-2.845, 0.826, 0.79, -0.018, 2.045, 2.096], [-3.127, 1.767, 1.058, 0.668, 0.751, 1.034], [-2.598, 2.204, 1.456, 0.337, 0.226, 0.622], [-1.681, 2.399, 0.33, 0.897, -0.155, 1.345], [-3.032, -0.879, 1.701, 0.543, 0.324, 1.619], [-2.871, -1.012, 1.112, 0.372, 0.525, 1.953], [-1.989, -1.505, 1.05, 1.013, 0.57, 2.119], [-2.342, -1.032, 1.573, 0.099, 0.665, 2.113], [-1.812, -1.549, 0.936, 0.03, 0.702, 1.331], [-0.897, -1.334, 1.032, -0.375, 0.838, 1.222], [1.073, -1.243, 1.222, 0.074, 0.103, 1.889], [0.684, -1.361, 0.842, 0.81, 0.134, 2.122], [1.681, -0.985, 0.915, -0.15, 0.046, 2.017], [1.499, -1.143, 1.225, 1.125, 0.255, 2.053], [2.518, -0.611, 1.251, 0.147, 0.376, 1.463], [2.78, -0.555, 1.623, 0.321, 0.5, 1.453], [2.607, 0.422, 0.93, 0.428, 2.768, 1.865], [2.929, 2.246, 1.615, 0.791, 0.45, 0.84], [2.894, 2.607, 0.679, 0.418, 0.185, 1.771], [1.91, 1.95, 0.631, 1.792, 0.498, 2.141], [0.968, 2.657, -0.02, 0.142, 0.513, 1.049], [2.739, 1.468, 0.752, 0.297, 1.124, 0.649], [1.999, -0.528, 0.795, 0.19, 0.625, 0.528]]\nB: [[-1.697, -1.101, 1.32, 0.519, 0.607, 2.31], [-2.793, 0.346, 1.419, 0.299, 1.423, 2.149], [-2.764, 1.638, 1.178, 0.604, 0.448, 0.518], [-2.94, 2.09, 1.372, -0.13, -0.272, 0.296], [-1.734, 2.804, 0.582, 1.396, 0.541, 0.939], [-2.549, -0.196, 1.12, 0.785, 0.411, 1.926], [-2.871, -1.014, 0.799, 0.56, 0.597, 1.935], [-2.659, -0.762, 1.356, 0.825, 0.021, 2.649], [-1.977, -1.011, 1.131, 0.465, 0.035, 2.324], [-1.526, -1.598, 1.392, 0.441, -0.118, 2.102], [-1.353, -0.868, 0.591, 0.125, 0.493, 1.476], [1.173, -1.254, 0.599, -0.335, 0.938, 1.499], [1.444, -1.618, 1.332, 0.376, 0.369, 1.68], [1.582, -1.255, 0.456, -0.034, -0.048, 2.138], [2.452, -1.152, 1.16, 0.41, -0.305, 2.162], [2.246, -1.101, 0.993, 0.065, 0.725, 2.256], [2.414, -0.99, 1.12, 0.836, 0.744, 1.026], [2.607, 0.594, 0.728, -0.103, 2.445, 1.796], [2.075, 1.78, 1.433, 0.826, 1.27, 1.569], [2.842, 2.47, 1.179, 0.437, 0.717, 1.714], [2.073, 1.959, 0.513, 1.293, -0.057, 1.28], [0.684, 2.546, 0.647, 0.281, 0.423, 0.403], [1.985, 2.256, 0.609, 0.323, 0.304, 0.186], [1.904, -0.439, 0.116, 0.205, 0.913, 1.076]]\nC: [[-0.882, -0.996, 0.828, 0.066, -0.03, 2.259], [-2.906, 0.486, 0.584, 0.338, 1.448, 2.228], [-2.335, 1.79, 1.402, 0.799, 0.604, 0.979], [-2.492, 2.696, 0.852, 0.385, -0.119, 0.551], [-2.113, 2.369, 0.634, 1.634, -0.378, 1.33], [-2.9, -0.382, 1.544, 0.229, 0.561, 1.896], [-2.46, -0.938, 0.92, 0.562, 0.836, 1.812], [-2.236, -1.122, 1.385, 0.806, -0.301, 1.756], [-1.859, -1.439, 0.978, -0.087, 0.007, 2.232], [-1.477, -1.605, 1.119, -0.203, 0.225, 1.352], [-0.558, -1.702, 0.427, 0.133, 0.668, 1.46], [0.818, -0.885, 1.161, 0.455, 0.101, 1.667], [0.552, -1.308, 0.707, 0.978, 0.615, 1.676], [2.109, -1.305, 1.008, -0.007, 0.224, 2.013], [2.016, -1.577, 1.004, 0.572, 0.061, 2.141], [1.754, -1.027, 1.286, 0.147, 0.165, 1.509], [2.849, -0.613, 0.987, 0.617, 1.099, 1.162], [2.281, 0.428, 1.287, 0.612, 2.792, 1.8], [2.1, 1.909, 1.627, 0.042, 0.641, 1.338], [2.025, 1.994, 0.97, 0.816, 0.372, 1.93], [1.678, 2.705, 1.241, 1.93, -0.063, 1.837], [0.582, 2.314, 0.279, 0.554, 0.013, 1.151], [2.245, 1.504, 0.631, 0.05, 1.008, 1.066], [2.09, -0.514, 0.622, -0.006, 1.061, 1.06]]\nD: [[-1.212, -1.13, 1.017, 0.465, 0.161, 2.011], [-2.56, 0.64, 0.971, 0.201, 1.804, 1.935], [-2.744, 1.914, 1.197, 0.349, 0.771, 0.66], [-2.606, 2.363, 1.087, 0.038, 0.219, 0.424], [-1.931, 2.472, 0.667, 1.366, 0.094, 1.255], [-2.729, -0.603, 1.38, 0.39, 0.792, 1.541], [-2.531, -0.93, 1.084, 0.175, 0.507, 2.172], [-2.227, -1.13, 1.087, 0.723, 0.142, 2.167], [-1.887, -1.279, 1.074, 0.181, 0.413, 2.133], [-1.395, -1.301, 1.124, 0.117, 0.289, 1.814], [-0.99, -1.313, 0.763, 0.122, 0.477, 1.511], [0.768, -1.372, 0.865, 0.144, 0.573, 1.696], [0.958, -1.124, 0.866, 0.51, 0.163, 1.704], [1.687, -1.284, 0.89, 0.172, 0.422, 1.772], [1.97, -1.137, 0.897, 0.705, 0.139, 1.81], [2.237, -1.017, 0.895, 0.302, 0.335, 1.807], [2.506, -0.615, 1.189, 0.456, 0.783, 1.228], [2.295, 0.463, 0.865, 0.248, 2.384, 1.746], [2.549, 1.9, 1.178, 0.43, 0.874, 1.13], [2.396, 2.329, 0.87, 0.323, 0.269, 1.739], [1.621, 2.45, 0.875, 1.651, 0.189, 1.735], [0.789, 2.563, 0.425, 0.113, 0.177, 0.782], [2.336, 1.911, 0.338, 0.211, 0.688, 0.678], [2.287, -0.576, 0.338, 0.14, 0.728, 0.71]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.196, -0.211, 0.68, 0.711, 0.576, 2.155]]\nB: [[-0.409, 0.533, 1.267, -0.113, 0.263, 1.631]]\nC: [[-0.799, 0.234, 0.962, 0.275, 0.234, 1.923]]\nD: [[-1.167, 0.457, 0.799, -0.179, 0.573, 2.357]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_109_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_109_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shower curtain in the scene. The camera pose information includes: the rotation matrix: [[-0.506976, -0.449046, 0.735753], [-0.861802, 0.247713, -0.442646], [0.016513, -0.858485, -0.512574]]; the translation vector: [1.568574, 4.423309, 1.333385], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.196, -0.211, 0.68, 0.711, 0.576, 2.155]]\nB: [[-0.409, 0.533, 1.267, -0.113, 0.263, 1.631]]\nC: [[-0.799, 0.234, 0.962, 0.275, 0.234, 1.923]]\nD: [[-1.167, 0.457, 0.799, -0.179, 0.573, 2.357]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.94, 1.68, 0.837, 0.663, 0.508, 0.307]]\nB: [[-1.567, 0.924, 0.596, -0.078, 0.24, 0.881]]\nC: [[-1.847, 1.274, 0.842, 0.196, 0.441, 0.778]]\nD: [[-2.041, 1.755, 1.288, 0.168, 0.884, 0.741]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_110_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_110_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the object in the scene. The camera pose information includes: the rotation matrix: [[0.233902, -0.58763, 0.774584], [-0.967246, -0.059828, 0.246692], [-0.098622, -0.806915, -0.582377]]; the translation vector: [0.860343, 3.117731, 1.418568], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.94, 1.68, 0.837, 0.663, 0.508, 0.307]]\nB: [[-1.567, 0.924, 0.596, -0.078, 0.24, 0.881]]\nC: [[-1.847, 1.274, 0.842, 0.196, 0.441, 0.778]]\nD: [[-2.041, 1.755, 1.288, 0.168, 0.884, 0.741]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.505, -0.116, 0.747, 0.409, 0.695, 0.297], [-1.441, 0.909, 0.606, 0.695, 0.528, 0.24], [1.536, 0.64, 0.715, 0.483, 0.851, 0.175], [1.546, -0.374, 0.796, 0.374, 0.77, 0.35], [-1.45, 0.754, 0.484, 0.85, 0.766, 0.215]]\nB: [[1.607, 0.304, 0.83, 0.898, 0.58, 0.697], [-1.406, 0.619, 0.763, 0.933, 0.149, 0.108], [1.448, 0.861, 0.699, 0.254, 0.441, 0.026], [1.945, -0.851, 0.97, 0.08, 1.051, 0.781], [-1.319, 0.842, 0.31, 1.314, 0.811, 0.161]]\nC: [[1.451, -0.224, 1.202, 0.474, 0.259, 0.177], [-1.303, 1.145, 0.291, 1.141, 0.346, 0.272], [1.763, 0.401, 0.944, 0.92, 1.062, -0.044], [1.663, -0.056, 0.805, 0.848, 1.189, 0.211], [-1.93, 0.603, 0.76, 0.741, 0.586, -0.206]]\nD: [[1.888, 0.164, 1.08, 0.295, 0.332, 0.729], [-1.781, 1.348, 0.164, 0.674, 0.738, 0.722], [1.997, 0.742, 0.991, 0.029, 0.449, -0.1], [1.487, 0.076, 0.6, 0.156, 0.445, 0.145], [-1.75, 1.16, 0.275, 0.799, 1.235, 0.304]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_111_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_111_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the pillow in the scene. The camera pose information includes: the rotation matrix: [[0.484778, 0.389748, -0.782998], [0.874059, -0.248441, 0.417491], [-0.031813, -0.886777, -0.461102]]; the translation vector: [2.948564, 2.712566, 1.480667], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.505, -0.116, 0.747, 0.409, 0.695, 0.297], [-1.441, 0.909, 0.606, 0.695, 0.528, 0.24], [1.536, 0.64, 0.715, 0.483, 0.851, 0.175], [1.546, -0.374, 0.796, 0.374, 0.77, 0.35], [-1.45, 0.754, 0.484, 0.85, 0.766, 0.215]]\nB: [[1.607, 0.304, 0.83, 0.898, 0.58, 0.697], [-1.406, 0.619, 0.763, 0.933, 0.149, 0.108], [1.448, 0.861, 0.699, 0.254, 0.441, 0.026], [1.945, -0.851, 0.97, 0.08, 1.051, 0.781], [-1.319, 0.842, 0.31, 1.314, 0.811, 0.161]]\nC: [[1.451, -0.224, 1.202, 0.474, 0.259, 0.177], [-1.303, 1.145, 0.291, 1.141, 0.346, 0.272], [1.763, 0.401, 0.944, 0.92, 1.062, -0.044], [1.663, -0.056, 0.805, 0.848, 1.189, 0.211], [-1.93, 0.603, 0.76, 0.741, 0.586, -0.206]]\nD: [[1.888, 0.164, 1.08, 0.295, 0.332, 0.729], [-1.781, 1.348, 0.164, 0.674, 0.738, 0.722], [1.997, 0.742, 0.991, 0.029, 0.449, -0.1], [1.487, 0.076, 0.6, 0.156, 0.445, 0.145], [-1.75, 1.16, 0.275, 0.799, 1.235, 0.304]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.148, -1.819, 0.681, 1.73, 0.9, 0.465], [-1.44, 2.208, 0.821, 0.893, 2.303, 0.634], [0.765, 1.362, 0.255, 2.131, 1.052, 0.327], [-1.998, -1.691, 0.062, 1.757, 1.835, 0.718]]\nB: [[1.542, -1.233, 0.854, 2.268, 1.021, 0.755], [-2.098, 1.815, 0.076, 0.977, 1.531, 0.579], [1.499, 1.894, 0.799, 1.364, 1.243, 0.606], [-1.591, -1.777, -0.089, 1.375, 2.302, 0.818]]\nC: [[1.019, -1.513, 0.012, 1.939, 1.04, 0.603], [-1.397, 1.894, 0.192, 1.788, 2.263, 0.963], [0.794, 1.72, 0.728, 1.503, 1.344, 0.994], [-1.899, -1.035, 0.107, 1.802, 1.941, 0.705]]\nD: [[1.181, -1.566, 0.434, 1.91, 1.342, 0.847], [-1.636, 1.86, 0.387, 1.322, 1.894, 0.782], [1.234, 1.651, 0.4, 1.847, 1.393, 0.784], [-1.767, -1.535, 0.407, 1.331, 1.981, 0.802]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_112_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_112_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the table in the scene. The camera pose information includes: the rotation matrix: [[0.996822, -0.027813, -0.074656], [0.056495, -0.413943, 0.908548], [-0.056173, -0.909878, -0.411056]]; the translation vector: [4.405487, 5.403347, 1.494535], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.148, -1.819, 0.681, 1.73, 0.9, 0.465], [-1.44, 2.208, 0.821, 0.893, 2.303, 0.634], [0.765, 1.362, 0.255, 2.131, 1.052, 0.327], [-1.998, -1.691, 0.062, 1.757, 1.835, 0.718]]\nB: [[1.542, -1.233, 0.854, 2.268, 1.021, 0.755], [-2.098, 1.815, 0.076, 0.977, 1.531, 0.579], [1.499, 1.894, 0.799, 1.364, 1.243, 0.606], [-1.591, -1.777, -0.089, 1.375, 2.302, 0.818]]\nC: [[1.019, -1.513, 0.012, 1.939, 1.04, 0.603], [-1.397, 1.894, 0.192, 1.788, 2.263, 0.963], [0.794, 1.72, 0.728, 1.503, 1.344, 0.994], [-1.899, -1.035, 0.107, 1.802, 1.941, 0.705]]\nD: [[1.181, -1.566, 0.434, 1.91, 1.342, 0.847], [-1.636, 1.86, 0.387, 1.322, 1.894, 0.782], [1.234, 1.651, 0.4, 1.847, 1.393, 0.784], [-1.767, -1.535, 0.407, 1.331, 1.981, 0.802]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.632, 2.861, 0.973, 0.851, 0.88, 0.553]]\nB: [[2.217, 3.039, 0.859, 0.578, 0.679, 0.811]]\nC: [[2.372, 2.508, 1.395, 0.466, 0.758, 0.941]]\nD: [[2.418, 3.313, 1.363, 0.462, 1.217, 0.869]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_113_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_113_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the tv in the scene. The camera pose information includes: the rotation matrix: [[-0.869565, 0.231948, -0.435955], [0.492522, 0.471291, -0.731647], [0.035758, -0.850932, -0.524058]]; the translation vector: [2.750575, 3.154689, 1.290553], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.632, 2.861, 0.973, 0.851, 0.88, 0.553]]\nB: [[2.217, 3.039, 0.859, 0.578, 0.679, 0.811]]\nC: [[2.372, 2.508, 1.395, 0.466, 0.758, 0.941]]\nD: [[2.418, 3.313, 1.363, 0.462, 1.217, 0.869]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.523, -0.73, 1.669, 0.473, 3.389, 1.19]]\nB: [[-2.737, -0.956, 1.441, 0.102, 2.891, 0.9]]\nC: [[-2.415, -1.042, 1.71, -0.167, 2.518, 1.307]]\nD: [[-3.121, -1.319, 1.73, 0.166, 2.406, 0.532]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_114_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_114_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the board in the scene. The camera pose information includes: the rotation matrix: [[0.896132, -0.052356, 0.440688], [-0.436974, -0.277444, 0.855616], [0.07747, -0.959314, -0.271505]]; the translation vector: [3.211431, 3.110947, 1.584554], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.523, -0.73, 1.669, 0.473, 3.389, 1.19]]\nB: [[-2.737, -0.956, 1.441, 0.102, 2.891, 0.9]]\nC: [[-2.415, -1.042, 1.71, -0.167, 2.518, 1.307]]\nD: [[-3.121, -1.319, 1.73, 0.166, 2.406, 0.532]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.919, 2.881, 0.5, 1.076, 0.198, 0.512], [-0.387, 3.021, 0.763, 1.206, 0.18, 1.044]]\nB: [[0.967, 3.235, 0.454, 1.103, -0.268, 0.912], [-0.093, 2.837, 0.491, 1.606, 0.643, 1.265]]\nC: [[1.146, 2.813, 0.895, 1.333, -0.231, 0.884], [-0.108, 2.697, 0.646, 1.144, -0.245, 0.801]]\nD: [[1.405, 2.769, 0.583, 0.816, -0.053, 0.839], [-0.646, 2.953, 0.434, 1.464, 0.436, 0.68]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_115_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_115_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the mirror in the scene. The camera pose information includes: the rotation matrix: [[-0.880278, -0.246293, 0.405524], [-0.473973, 0.417832, -0.775091], [0.021459, -0.874503, -0.484545]]; the translation vector: [3.281806, 2.754624, 1.352781], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.919, 2.881, 0.5, 1.076, 0.198, 0.512], [-0.387, 3.021, 0.763, 1.206, 0.18, 1.044]]\nB: [[0.967, 3.235, 0.454, 1.103, -0.268, 0.912], [-0.093, 2.837, 0.491, 1.606, 0.643, 1.265]]\nC: [[1.146, 2.813, 0.895, 1.333, -0.231, 0.884], [-0.108, 2.697, 0.646, 1.144, -0.245, 0.801]]\nD: [[1.405, 2.769, 0.583, 0.816, -0.053, 0.839], [-0.646, 2.953, 0.434, 1.464, 0.436, 0.68]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.099, -1.623, 0.8, 1.091, 0.185, 1.674]]\nB: [[0.028, -1.324, 1.283, 0.847, -0.251, 1.976]]\nC: [[0.008, -1.165, 1.014, 1.132, -0.028, 1.19]]\nD: [[0.219, -1.325, 0.313, 1.01, 0.321, 1.757]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_116_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_116_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the doorframe in the scene. The camera pose information includes: the rotation matrix: [[-0.874867, -0.0675, 0.479638], [-0.482919, 0.197999, -0.852987], [-0.037391, -0.977875, -0.205819]]; the translation vector: [2.397274, 1.722858, 1.486845], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.099, -1.623, 0.8, 1.091, 0.185, 1.674]]\nB: [[0.028, -1.324, 1.283, 0.847, -0.251, 1.976]]\nC: [[0.008, -1.165, 1.014, 1.132, -0.028, 1.19]]\nD: [[0.219, -1.325, 0.313, 1.01, 0.321, 1.757]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.712, -1.245, 0.91, 1.048, 1.199, 2.013]]\nB: [[0.626, -1.611, 1.221, 1.09, 1.245, 2.069]]\nC: [[1.138, -1.446, 0.77, 0.846, 1.373, 1.96]]\nD: [[0.371, -1.441, 0.499, 0.655, 1.441, 2.321]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_117_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_117_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shower in the scene. The camera pose information includes: the rotation matrix: [[-0.612656, -0.411508, 0.674769], [-0.789543, 0.280105, -0.546043], [0.035694, -0.867296, -0.496511]]; the translation vector: [1.897828, 2.372103, 1.388776], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.712, -1.245, 0.91, 1.048, 1.199, 2.013]]\nB: [[0.626, -1.611, 1.221, 1.09, 1.245, 2.069]]\nC: [[1.138, -1.446, 0.77, 0.846, 1.373, 1.96]]\nD: [[0.371, -1.441, 0.499, 0.655, 1.441, 2.321]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.851, -0.281, 1.012, 0.232, 0.838, 2.123]]\nB: [[-0.647, 0.167, 1.047, -0.111, 0.572, 1.688]]\nC: [[-0.968, -0.496, 1.046, -0.014, 1.192, 1.751]]\nD: [[-0.616, -0.07, 1.075, 0.231, 1.203, 1.991]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_118_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_118_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the doorframe in the scene. The camera pose information includes: the rotation matrix: [[-0.48142, 0.335029, -0.809933], [0.872625, 0.096524, -0.478757], [-0.08222, -0.937251, -0.338823]]; the translation vector: [4.429162, 2.287411, 1.464776], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.851, -0.281, 1.012, 0.232, 0.838, 2.123]]\nB: [[-0.647, 0.167, 1.047, -0.111, 0.572, 1.688]]\nC: [[-0.968, -0.496, 1.046, -0.014, 1.192, 1.751]]\nD: [[-0.616, -0.07, 1.075, 0.231, 1.203, 1.991]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.76, 1.613, 0.501, 0.748, 1.477, 2.283], [-1.256, 0.486, 0.695, 0.261, -0.004, 1.392]]\nB: [[1.66, 0.843, 1.041, 1.024, 1.548, 1.575], [-0.68, 1.177, 0.879, 0.467, 0.635, 2.319]]\nC: [[1.906, 1.059, 1.056, 0.263, 1.047, 1.4], [-0.793, 1.238, 0.654, 0.903, 0.438, 1.901]]\nD: [[1.788, 1.153, 0.954, 0.56, 1.154, 1.881], [-0.939, 0.896, 0.911, 0.636, 0.225, 1.837]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_119_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_119_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[-0.414473, -0.491559, 0.765887], [-0.909569, 0.196057, -0.366396], [0.029948, -0.848488, -0.528367]]; the translation vector: [0.955419, 3.497842, 1.497559], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.76, 1.613, 0.501, 0.748, 1.477, 2.283], [-1.256, 0.486, 0.695, 0.261, -0.004, 1.392]]\nB: [[1.66, 0.843, 1.041, 1.024, 1.548, 1.575], [-0.68, 1.177, 0.879, 0.467, 0.635, 2.319]]\nC: [[1.906, 1.059, 1.056, 0.263, 1.047, 1.4], [-0.793, 1.238, 0.654, 0.903, 0.438, 1.901]]\nD: [[1.788, 1.153, 0.954, 0.56, 1.154, 1.881], [-0.939, 0.896, 0.911, 0.636, 0.225, 1.837]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.054, 1.184, 0.861, 1.846, 0.937, 1.341]]\nB: [[0.486, 0.802, 0.412, 1.751, 1.322, 0.856]]\nC: [[0.138, 0.31, 0.136, 1.361, 1.636, 1.27]]\nD: [[0.461, 1.003, 0.863, 1.591, 0.946, 0.97]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_120_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_120_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the bed in the scene. The camera pose information includes: the rotation matrix: [[-0.778266, 0.076502, -0.623257], [0.626532, 0.028295, -0.778882], [-0.041951, -0.996668, -0.069952]]; the translation vector: [4.354075, 2.27787, 1.510689], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.054, 1.184, 0.861, 1.846, 0.937, 1.341]]\nB: [[0.486, 0.802, 0.412, 1.751, 1.322, 0.856]]\nC: [[0.138, 0.31, 0.136, 1.361, 1.636, 1.27]]\nD: [[0.461, 1.003, 0.863, 1.591, 0.946, 0.97]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.119, 2.382, -1.353, 2.688, 3.135, 0.067], [0.13, -2.518, 2.241, 3.948, 1.36, 0.679], [-0.552, 1.97, 3.469, 0.872, 1.36, 0.237]]\nB: [[1.56, 2.895, -0.877, 2.03, 3.162, 0.481], [-0.012, -2.383, 2.434, 3.863, 1.73, 0.8], [-1.053, 2.303, 3.432, 0.863, 1.12, -0.287]]\nC: [[1.156, 2.743, -1.086, 2.211, 3.278, 0.076], [-0.143, -2.063, 2.035, 4.283, 1.757, 0.379], [-1.038, 2.35, 3.4, 1.326, 1.515, 0.161]]\nD: [[1.559, 2.394, -0.855, 1.997, 3.635, -0.357], [-0.381, -2.532, 1.718, 3.949, 1.906, 0.055], [-0.752, 2.698, 2.911, 0.92, 1.137, -0.299]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_121_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_121_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[0.485844, -0.617081, 0.619005], [-0.873216, -0.311825, 0.374512], [-0.038083, -0.722479, -0.690343]]; the translation vector: [-0.164865, 3.073333, 1.323993], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.119, 2.382, -1.353, 2.688, 3.135, 0.067], [0.13, -2.518, 2.241, 3.948, 1.36, 0.679], [-0.552, 1.97, 3.469, 0.872, 1.36, 0.237]]\nB: [[1.56, 2.895, -0.877, 2.03, 3.162, 0.481], [-0.012, -2.383, 2.434, 3.863, 1.73, 0.8], [-1.053, 2.303, 3.432, 0.863, 1.12, -0.287]]\nC: [[1.156, 2.743, -1.086, 2.211, 3.278, 0.076], [-0.143, -2.063, 2.035, 4.283, 1.757, 0.379], [-1.038, 2.35, 3.4, 1.326, 1.515, 0.161]]\nD: [[1.559, 2.394, -0.855, 1.997, 3.635, -0.357], [-0.381, -2.532, 1.718, 3.949, 1.906, 0.055], [-0.752, 2.698, 2.911, 0.92, 1.137, -0.299]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.806, 0.963, 2.18, -0.15, 0.284, 0.747], [1.781, 1.911, 0.25, 0.639, -0.36, 0.423], [0.587, 2.601, 0.863, 0.066, -0.232, 0.753], [-0.114, 0.242, -0.467, -0.049, 0.569, 0.595], [1.888, 2.229, 0.499, -0.382, 0.046, 0.309], [1.509, 1.846, 0.444, -0.228, 0.075, -0.285]]\nB: [[2.196, 0.809, 1.96, 0.418, -0.322, 0.279], [1.469, 1.583, 0.002, 0.664, -0.01, 0.038], [-0.066, 2.119, 1.533, 0.754, 0.595, 0.72], [0.503, 0.542, -0.003, 0.788, 0.94, -0.045], [2.066, 1.491, 0.9, -0.225, 0.433, 0.456], [1.952, 1.903, 0.373, -0.138, 0.52, 0.69]]\nC: [[2.185, 1.438, 1.612, -0.033, 0.189, 0.171], [2.033, 1.77, 0.709, 0.481, 0.536, -0.285], [-0.228, 2.34, 1.714, 0.595, 0.3, -0.06], [-0.139, 0.192, -0.291, 0.431, 0.48, -0.343], [2.39, 1.84, 0.691, -0.012, 0.252, 0.48], [1.891, 1.81, 0.417, -0.052, -0.296, 0.438]]\nD: [[2.211, 1.285, 1.775, 0.127, 0.174, 0.292], [1.829, 1.683, 0.248, 0.278, 0.134, 0.131], [0.255, 2.241, 1.304, 0.333, 0.221, 0.253], [0.094, 0.321, -0.047, 0.34, 0.473, 0.108], [1.975, 1.944, 0.507, 0.101, 0.048, 0.175], [1.799, 1.959, 0.282, 0.261, 0.114, 0.195]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_122_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_122_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the object in the scene. The camera pose information includes: the rotation matrix: [[-0.877021, 0.121711, -0.464779], [0.46491, 0.459041, -0.75706], [0.12121, -0.880038, -0.459173]]; the translation vector: [3.922419, 3.230202, 1.747047], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.806, 0.963, 2.18, -0.15, 0.284, 0.747], [1.781, 1.911, 0.25, 0.639, -0.36, 0.423], [0.587, 2.601, 0.863, 0.066, -0.232, 0.753], [-0.114, 0.242, -0.467, -0.049, 0.569, 0.595], [1.888, 2.229, 0.499, -0.382, 0.046, 0.309], [1.509, 1.846, 0.444, -0.228, 0.075, -0.285]]\nB: [[2.196, 0.809, 1.96, 0.418, -0.322, 0.279], [1.469, 1.583, 0.002, 0.664, -0.01, 0.038], [-0.066, 2.119, 1.533, 0.754, 0.595, 0.72], [0.503, 0.542, -0.003, 0.788, 0.94, -0.045], [2.066, 1.491, 0.9, -0.225, 0.433, 0.456], [1.952, 1.903, 0.373, -0.138, 0.52, 0.69]]\nC: [[2.185, 1.438, 1.612, -0.033, 0.189, 0.171], [2.033, 1.77, 0.709, 0.481, 0.536, -0.285], [-0.228, 2.34, 1.714, 0.595, 0.3, -0.06], [-0.139, 0.192, -0.291, 0.431, 0.48, -0.343], [2.39, 1.84, 0.691, -0.012, 0.252, 0.48], [1.891, 1.81, 0.417, -0.052, -0.296, 0.438]]\nD: [[2.211, 1.285, 1.775, 0.127, 0.174, 0.292], [1.829, 1.683, 0.248, 0.278, 0.134, 0.131], [0.255, 2.241, 1.304, 0.333, 0.221, 0.253], [0.094, 0.321, -0.047, 0.34, 0.473, 0.108], [1.975, 1.944, 0.507, 0.101, 0.048, 0.175], [1.799, 1.959, 0.282, 0.261, 0.114, 0.195]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.687, 1.332, 0.035, 0.175, 2.444, 0.931], [-0.771, -0.087, 1.874, 0.351, 2.708, 1.076], [1.073, -0.054, 0.17, 0.668, 1.99, 0.53], [0.962, 0.659, 2.221, 0.281, 1.867, 1.287]]\nB: [[-0.793, 1.344, 0.264, -0.144, 2.319, 0.303], [-0.892, -0.228, 1.276, 0.428, 2.505, 1.37], [0.859, 0.126, 0.332, 0.439, 1.529, 0.603], [0.425, 0.012, 2.016, 0.908, 2.008, 0.841]]\nC: [[-1.133, 0.424, 0.86, -0.054, 2.382, 0.943], [-1.282, -0.466, 1.739, 0.288, 2.29, 1.182], [0.76, 0.578, 0.124, 0.797, 1.631, 0.597], [0.974, 0.003, 1.857, 0.274, 1.983, 0.737]]\nD: [[-0.66, 0.92, 0.369, 0.068, 2.758, 0.803], [-0.938, -0.063, 1.743, 0.134, 2.421, 0.981], [0.672, 0.378, 0.36, 0.646, 1.681, 0.828], [0.776, 0.348, 1.743, 0.449, 1.71, 0.968]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_123_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_123_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the kitchen cabinets in the scene. The camera pose information includes: the rotation matrix: [[0.815869, 0.244354, -0.524069], [0.578211, -0.336271, 0.743367], [0.005416, -0.909513, -0.415641]]; the translation vector: [2.358014, 1.230078, 1.369842], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.687, 1.332, 0.035, 0.175, 2.444, 0.931], [-0.771, -0.087, 1.874, 0.351, 2.708, 1.076], [1.073, -0.054, 0.17, 0.668, 1.99, 0.53], [0.962, 0.659, 2.221, 0.281, 1.867, 1.287]]\nB: [[-0.793, 1.344, 0.264, -0.144, 2.319, 0.303], [-0.892, -0.228, 1.276, 0.428, 2.505, 1.37], [0.859, 0.126, 0.332, 0.439, 1.529, 0.603], [0.425, 0.012, 2.016, 0.908, 2.008, 0.841]]\nC: [[-1.133, 0.424, 0.86, -0.054, 2.382, 0.943], [-1.282, -0.466, 1.739, 0.288, 2.29, 1.182], [0.76, 0.578, 0.124, 0.797, 1.631, 0.597], [0.974, 0.003, 1.857, 0.274, 1.983, 0.737]]\nD: [[-0.66, 0.92, 0.369, 0.068, 2.758, 0.803], [-0.938, -0.063, 1.743, 0.134, 2.421, 0.981], [0.672, 0.378, 0.36, 0.646, 1.681, 0.828], [0.776, 0.348, 1.743, 0.449, 1.71, 0.968]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.177, -0.077, 0.773, 0.974, 8.45, 1.181]]\nB: [[1.408, -0.085, 0.96, 1.256, 8.826, 1.391]]\nC: [[1.29, 0.138, 0.989, 1.682, 8.854, 1.495]]\nD: [[1.087, -0.264, 0.505, 1.705, 9.131, 0.904]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_124_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_124_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the blinds in the scene. The camera pose information includes: the rotation matrix: [[0.117057, -0.769276, 0.628102], [-0.987232, -0.021336, 0.157855], [-0.108033, -0.638561, -0.761951]]; the translation vector: [1.032686, 1.226834, 2.186959], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.177, -0.077, 0.773, 0.974, 8.45, 1.181]]\nB: [[1.408, -0.085, 0.96, 1.256, 8.826, 1.391]]\nC: [[1.29, 0.138, 0.989, 1.682, 8.854, 1.495]]\nD: [[1.087, -0.264, 0.505, 1.705, 9.131, 0.904]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.333, 1.449, 0.466, 0.575, 0.885, 0.579], [-0.819, 0.179, 1.256, -0.187, 0.08, 0.261], [-0.881, -0.764, 1.817, 0.04, 0.55, 0.119], [-0.782, -0.821, 1.039, -0.465, -0.079, -0.041]]\nB: [[0.913, 1.406, 0.914, 0.154, 0.729, 0.951], [-0.918, 0.236, 1.614, 0.027, 0.343, 0.415], [-0.932, -0.471, 1.376, 0.043, 0.42, 0.318], [-0.937, -1.266, 1.202, 0.021, 0.397, 0.404]]\nC: [[0.638, 1.511, 1.273, 0.574, 0.958, 0.746], [-1.165, 0.389, 1.897, 0.474, -0.02, 0.527], [-0.474, 0.021, 1.802, 0.289, 0.006, -0.062], [-1.35, -1.672, 1.153, 0.07, 0.246, 0.557]]\nD: [[0.615, 1.775, 1.082, 0.394, 0.94, 1.366], [-0.883, -0.231, 1.634, -0.385, 0.134, 0.914], [-0.757, -0.827, 1.097, 0.253, 0.741, 0.546], [-1.013, -1.459, 1.475, -0.37, 0.862, 0.783]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_125_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_125_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the picture in the scene. The camera pose information includes: the rotation matrix: [[-0.042655, 0.409797, -0.911179], [0.998036, -0.024411, -0.0577], [-0.045888, -0.91185, -0.40795]]; the translation vector: [2.423933, 1.356295, 3.282493], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.333, 1.449, 0.466, 0.575, 0.885, 0.579], [-0.819, 0.179, 1.256, -0.187, 0.08, 0.261], [-0.881, -0.764, 1.817, 0.04, 0.55, 0.119], [-0.782, -0.821, 1.039, -0.465, -0.079, -0.041]]\nB: [[0.913, 1.406, 0.914, 0.154, 0.729, 0.951], [-0.918, 0.236, 1.614, 0.027, 0.343, 0.415], [-0.932, -0.471, 1.376, 0.043, 0.42, 0.318], [-0.937, -1.266, 1.202, 0.021, 0.397, 0.404]]\nC: [[0.638, 1.511, 1.273, 0.574, 0.958, 0.746], [-1.165, 0.389, 1.897, 0.474, -0.02, 0.527], [-0.474, 0.021, 1.802, 0.289, 0.006, -0.062], [-1.35, -1.672, 1.153, 0.07, 0.246, 0.557]]\nD: [[0.615, 1.775, 1.082, 0.394, 0.94, 1.366], [-0.883, -0.231, 1.634, -0.385, 0.134, 0.914], [-0.757, -0.827, 1.097, 0.253, 0.741, 0.546], [-1.013, -1.459, 1.475, -0.37, 0.862, 0.783]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.696, 2.84, 0.243, 0.913, 0.14, 0.965], [-2.524, -1.89, 1.517, 0.263, 1.169, 1.097]]\nB: [[-2.032, 3.081, 0.673, 0.874, 0.207, 1.282], [-2.435, -2.167, 1.207, 0.214, 0.953, 0.8]]\nC: [[-2.083, 2.817, 0.915, 0.407, -0.083, 1.119], [-2.185, -1.778, 0.77, 0.561, 0.888, 0.902]]\nD: [[-1.79, 3.485, 0.577, 0.547, 0.315, 1.286], [-2.516, -2.509, 1.071, 0.577, 1.197, 0.616]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_126_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_126_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the window in the scene. The camera pose information includes: the rotation matrix: [[0.299058, 0.37418, -0.877812], [0.95368, -0.085842, 0.288314], [0.032528, -0.923375, -0.38252]]; the translation vector: [3.908031, 4.993837, 1.41318], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.696, 2.84, 0.243, 0.913, 0.14, 0.965], [-2.524, -1.89, 1.517, 0.263, 1.169, 1.097]]\nB: [[-2.032, 3.081, 0.673, 0.874, 0.207, 1.282], [-2.435, -2.167, 1.207, 0.214, 0.953, 0.8]]\nC: [[-2.083, 2.817, 0.915, 0.407, -0.083, 1.119], [-2.185, -1.778, 0.77, 0.561, 0.888, 0.902]]\nD: [[-1.79, 3.485, 0.577, 0.547, 0.315, 1.286], [-2.516, -2.509, 1.071, 0.577, 1.197, 0.616]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.147, 0.119, 0.251, 0.463, 0.502, 0.493], [1.142, -0.546, 0.997, 0.457, 0.597, 0.473], [1.198, 0.632, 0.925, 0.452, 0.473, 0.432], [1.163, 0.092, 1.069, 0.44, 0.432, 0.506]]\nB: [[0.939, -0.362, 0.676, 0.67, 0.041, 0.58], [0.766, -0.402, 0.786, 0.189, 1.052, 0.915], [1.684, 0.428, 1.283, 0.635, 0.353, 0.864], [1.275, -0.104, 1.385, 0.008, 0.054, 0.956]]\nC: [[1.248, 0.165, 0.549, 0.255, 0.722, 0.454], [1.139, -0.967, 1.065, 0.247, 0.425, 0.531], [0.839, 1.106, 1.224, 0.271, 0.846, 0.671], [0.954, 0.329, 1.422, 0.774, 0.624, 0.313]]\nD: [[1.328, 0.233, 0.409, 0.859, 0.672, 0.071], [1.492, -0.434, 0.743, 0.731, 0.907, 0.382], [1.626, 0.478, 0.601, 0.312, 0.631, 0.904], [1.629, 0.385, 0.684, 0.845, 0.492, 0.801]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_127_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_127_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the printer in the scene. The camera pose information includes: the rotation matrix: [[0.985254, -0.134646, 0.105573], [-0.142287, -0.302097, 0.942599], [-0.095024, -0.94372, -0.3168]]; the translation vector: [1.134605, 1.549487, 1.505245], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.147, 0.119, 0.251, 0.463, 0.502, 0.493], [1.142, -0.546, 0.997, 0.457, 0.597, 0.473], [1.198, 0.632, 0.925, 0.452, 0.473, 0.432], [1.163, 0.092, 1.069, 0.44, 0.432, 0.506]]\nB: [[0.939, -0.362, 0.676, 0.67, 0.041, 0.58], [0.766, -0.402, 0.786, 0.189, 1.052, 0.915], [1.684, 0.428, 1.283, 0.635, 0.353, 0.864], [1.275, -0.104, 1.385, 0.008, 0.054, 0.956]]\nC: [[1.248, 0.165, 0.549, 0.255, 0.722, 0.454], [1.139, -0.967, 1.065, 0.247, 0.425, 0.531], [0.839, 1.106, 1.224, 0.271, 0.846, 0.671], [0.954, 0.329, 1.422, 0.774, 0.624, 0.313]]\nD: [[1.328, 0.233, 0.409, 0.859, 0.672, 0.071], [1.492, -0.434, 0.743, 0.731, 0.907, 0.382], [1.626, 0.478, 0.601, 0.312, 0.631, 0.904], [1.629, 0.385, 0.684, 0.845, 0.492, 0.801]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.978, 2.218, 0.88, 0.413, 0.1, 0.702], [-1.917, 2.1, 1.145, 0.538, 0.288, 0.643]]\nB: [[-1.584, 2.193, 0.205, 0.199, 0.268, 0.839], [-1.535, 2.333, 0.994, 0.342, 0.187, 0.134]]\nC: [[-1.966, 2.066, 0.622, 0.287, 0.189, 0.88], [-1.737, 2.041, 0.848, 0.173, 0.149, 0.382]]\nD: [[-1.998, 2.157, 0.963, 0.629, -0.078, 1.235], [-1.653, 2.214, 0.646, 0.156, 0.285, 0.243]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_128_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_128_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the towel in the scene. The camera pose information includes: the rotation matrix: [[0.686341, -0.358824, 0.632599], [-0.727213, -0.35045, 0.590209], [0.009912, -0.865119, -0.50147]]; the translation vector: [2.486494, 4.601647, 1.455454], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.978, 2.218, 0.88, 0.413, 0.1, 0.702], [-1.917, 2.1, 1.145, 0.538, 0.288, 0.643]]\nB: [[-1.584, 2.193, 0.205, 0.199, 0.268, 0.839], [-1.535, 2.333, 0.994, 0.342, 0.187, 0.134]]\nC: [[-1.966, 2.066, 0.622, 0.287, 0.189, 0.88], [-1.737, 2.041, 0.848, 0.173, 0.149, 0.382]]\nD: [[-1.998, 2.157, 0.963, 0.629, -0.078, 1.235], [-1.653, 2.214, 0.646, 0.156, 0.285, 0.243]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.047, -0.588, -0.114, 0.759, 2.976, 0.961]]\nB: [[-1.511, -0.608, 0.081, 1.102, 2.98, 1.011]]\nC: [[-1.203, -0.385, 0.359, 0.756, 2.647, 0.817]]\nD: [[-1.37, -0.358, 0.323, 0.77, 2.437, 0.409]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_129_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_129_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the desk in the scene. The camera pose information includes: the rotation matrix: [[-0.802837, 0.056561, -0.593509], [0.596192, 0.071654, -0.799638], [-0.002701, -0.995825, -0.091248]]; the translation vector: [2.583219, 4.008804, 1.439254], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.047, -0.588, -0.114, 0.759, 2.976, 0.961]]\nB: [[-1.511, -0.608, 0.081, 1.102, 2.98, 1.011]]\nC: [[-1.203, -0.385, 0.359, 0.756, 2.647, 0.817]]\nD: [[-1.37, -0.358, 0.323, 0.77, 2.437, 0.409]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.904, -0.732, 0.092, 0.243, 0.59, 0.859], [-1.501, -0.76, 0.757, 1.132, 0.498, 0.756]]\nB: [[1.126, -0.366, 0.392, 0.688, 0.942, 0.802], [-1.375, -0.274, 0.471, 1.076, 0.886, 0.947]]\nC: [[0.868, -0.772, 0.151, 0.633, 1.223, 0.791], [-1.775, -0.718, 0.331, 1.093, 0.846, 1.4]]\nD: [[1.114, -0.309, 0.254, 0.953, 0.846, 0.427], [-1.752, 0.101, 0.877, 0.811, 1.045, 0.651]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_130_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_130_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the dresser in the scene. The camera pose information includes: the rotation matrix: [[-0.442667, -0.46733, 0.765277], [-0.896368, 0.253361, -0.363776], [-0.023888, -0.847001, -0.531054]]; the translation vector: [2.453469, 1.905797, 1.451684], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.904, -0.732, 0.092, 0.243, 0.59, 0.859], [-1.501, -0.76, 0.757, 1.132, 0.498, 0.756]]\nB: [[1.126, -0.366, 0.392, 0.688, 0.942, 0.802], [-1.375, -0.274, 0.471, 1.076, 0.886, 0.947]]\nC: [[0.868, -0.772, 0.151, 0.633, 1.223, 0.791], [-1.775, -0.718, 0.331, 1.093, 0.846, 1.4]]\nD: [[1.114, -0.309, 0.254, 0.953, 0.846, 0.427], [-1.752, 0.101, 0.877, 0.811, 1.045, 0.651]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.61, -0.414, 0.408, 4.655, 4.102, -0.244]]\nB: [[0.311, -0.524, 0.039, 4.829, 4.569, 0.162]]\nC: [[0.203, -0.323, 0.325, 5.217, 4.229, 0.65]]\nD: [[0.089, -0.712, 0.114, 5.287, 4.148, 0.629]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_131_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_131_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[0.633294, -0.360819, 0.684652], [-0.773758, -0.312806, 0.550863], [0.015401, -0.878613, -0.477285]]; the translation vector: [3.241882, 3.386626, 1.367882], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.61, -0.414, 0.408, 4.655, 4.102, -0.244]]\nB: [[0.311, -0.524, 0.039, 4.829, 4.569, 0.162]]\nC: [[0.203, -0.323, 0.325, 5.217, 4.229, 0.65]]\nD: [[0.089, -0.712, 0.114, 5.287, 4.148, 0.629]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.917, -0.639, 0.621, 0.336, 6.104, 1.183], [0.361, -2.884, 0.637, 5.668, -0.024, 1.879], [-2.761, -1.433, 0.573, -0.228, 0.551, 1.578], [-3.018, -2.023, 1.21, 0.696, 0.033, 1.511], [-2.944, 0.173, 0.693, 0.375, 4.326, 1.889]]\nB: [[2.88, 0.258, 0.921, 0.635, 5.466, 1.974], [-0.061, -2.646, 0.552, 6.114, -0.006, 1.775], [-3.069, -1.6, 0.804, 0.521, 0.433, 1.489], [-3.084, -1.953, 1.232, 0.742, 0.11, 1.43], [-2.84, 1.121, 0.562, 0.204, 4.902, 1.824]]\nC: [[3.129, -0.248, 1.092, 0.28, 6.006, 1.69], [0.277, -3.248, 1.229, 5.639, 0.457, 1.83], [-2.943, -1.702, 1.206, 0.61, 0.818, 1.511], [-2.632, -1.423, 0.42, 0.373, 0.138, 1.635], [-3.02, 0.349, 0.427, 0.566, 4.15, 1.781]]\nD: [[3.003, -0.173, 0.772, 0.324, 5.743, 1.505], [-0.052, -3.097, 0.827, 6.005, 0.286, 1.553], [-3.164, -1.839, 0.77, 0.192, 0.577, 1.362], [-2.872, -1.562, 0.743, 0.498, 0.153, 1.361], [-2.619, 0.636, 0.832, 0.279, 4.454, 1.688]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_132_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_132_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.924593, 0.219455, -0.311397], [0.371095, 0.334047, -0.86643], [-0.086121, -0.916653, -0.390296]]; the translation vector: [7.650298, 2.745242, 1.444521], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.917, -0.639, 0.621, 0.336, 6.104, 1.183], [0.361, -2.884, 0.637, 5.668, -0.024, 1.879], [-2.761, -1.433, 0.573, -0.228, 0.551, 1.578], [-3.018, -2.023, 1.21, 0.696, 0.033, 1.511], [-2.944, 0.173, 0.693, 0.375, 4.326, 1.889]]\nB: [[2.88, 0.258, 0.921, 0.635, 5.466, 1.974], [-0.061, -2.646, 0.552, 6.114, -0.006, 1.775], [-3.069, -1.6, 0.804, 0.521, 0.433, 1.489], [-3.084, -1.953, 1.232, 0.742, 0.11, 1.43], [-2.84, 1.121, 0.562, 0.204, 4.902, 1.824]]\nC: [[3.129, -0.248, 1.092, 0.28, 6.006, 1.69], [0.277, -3.248, 1.229, 5.639, 0.457, 1.83], [-2.943, -1.702, 1.206, 0.61, 0.818, 1.511], [-2.632, -1.423, 0.42, 0.373, 0.138, 1.635], [-3.02, 0.349, 0.427, 0.566, 4.15, 1.781]]\nD: [[3.003, -0.173, 0.772, 0.324, 5.743, 1.505], [-0.052, -3.097, 0.827, 6.005, 0.286, 1.553], [-3.164, -1.839, 0.77, 0.192, 0.577, 1.362], [-2.872, -1.562, 0.743, 0.498, 0.153, 1.361], [-2.619, 0.636, 0.832, 0.279, 4.454, 1.688]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.133, 0.902, 0.422, 0.039, 0.22, 0.632]]\nB: [[-0.076, 0.973, 0.415, -0.004, 1.174, 1.248]]\nC: [[0.144, 0.321, 0.705, -0.021, 0.284, 1.035]]\nD: [[0.355, 0.535, 0.346, 0.07, 0.677, 0.805]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_133_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_133_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the dishwasher in the scene. The camera pose information includes: the rotation matrix: [[0.975982, 0.033782, -0.215214], [0.215389, -0.297687, 0.930048], [-0.032648, -0.954066, -0.297814]]; the translation vector: [2.838751, 1.414222, 1.664536], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.133, 0.902, 0.422, 0.039, 0.22, 0.632]]\nB: [[-0.076, 0.973, 0.415, -0.004, 1.174, 1.248]]\nC: [[0.144, 0.321, 0.705, -0.021, 0.284, 1.035]]\nD: [[0.355, 0.535, 0.346, 0.07, 0.677, 0.805]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.578, -0.219, 1.033, 0.257, 3.748, 1.935], [2.209, 0.671, 0.788, -0.095, 3.689, 2.508], [0.223, -2.291, 0.584, 0.625, 0.246, 1.596], [0.409, -2.777, 1.049, -0.283, 0.131, 1.81]]\nB: [[-2.04, 0.586, 0.772, 0.304, 3.812, 1.848], [1.619, 0.488, 0.655, 0.555, 3.765, 1.94], [0.355, -2.984, 1.136, -0.107, 0.055, 1.74], [0.752, -2.78, 0.749, 0.33, 0.188, 1.815]]\nC: [[-1.581, 0.188, 1.09, 0.283, 3.526, 2.183], [1.935, 0.185, 1.045, 0.157, 3.57, 2.128], [0.384, -2.556, 0.863, 0.244, 0.135, 1.758], [0.278, -2.37, 1.022, 0.1, 0.539, 2.045]]\nD: [[-1.492, -0.235, 1.434, 0.171, 3.146, 1.819], [2.128, 0.651, 1.233, 0.526, 3.819, 1.664], [0.295, -2.822, 1.218, -0.087, 0.184, 1.532], [0.45, -1.956, 1.009, 0.299, 0.644, 2.242]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_134_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_134_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.037281, 0.595041, -0.80283], [0.998378, -0.012419, -0.055566], [-0.043034, -0.803599, -0.593613]]; the translation vector: [3.95675, 2.244474, 1.442954], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.578, -0.219, 1.033, 0.257, 3.748, 1.935], [2.209, 0.671, 0.788, -0.095, 3.689, 2.508], [0.223, -2.291, 0.584, 0.625, 0.246, 1.596], [0.409, -2.777, 1.049, -0.283, 0.131, 1.81]]\nB: [[-2.04, 0.586, 0.772, 0.304, 3.812, 1.848], [1.619, 0.488, 0.655, 0.555, 3.765, 1.94], [0.355, -2.984, 1.136, -0.107, 0.055, 1.74], [0.752, -2.78, 0.749, 0.33, 0.188, 1.815]]\nC: [[-1.581, 0.188, 1.09, 0.283, 3.526, 2.183], [1.935, 0.185, 1.045, 0.157, 3.57, 2.128], [0.384, -2.556, 0.863, 0.244, 0.135, 1.758], [0.278, -2.37, 1.022, 0.1, 0.539, 2.045]]\nD: [[-1.492, -0.235, 1.434, 0.171, 3.146, 1.819], [2.128, 0.651, 1.233, 0.526, 3.819, 1.664], [0.295, -2.822, 1.218, -0.087, 0.184, 1.532], [0.45, -1.956, 1.009, 0.299, 0.644, 2.242]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.199, 1.125, 0.399, 0.214, 0.05, 0.589]]\nB: [[0.02, 1.322, 0.476, 0.689, 0.454, 0.768]]\nC: [[0.504, 0.831, 0.74, 0.202, 0.254, 0.39]]\nD: [[0.93, 1.224, 1.103, 0.115, -0.143, 0.862]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_135_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_135_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shelf in the scene. The camera pose information includes: the rotation matrix: [[0.994446, -0.078697, 0.06988], [-0.104992, -0.787844, 0.606859], [0.007297, -0.610826, -0.791731]]; the translation vector: [1.305105, 0.510448, 1.183315], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.199, 1.125, 0.399, 0.214, 0.05, 0.589]]\nB: [[0.02, 1.322, 0.476, 0.689, 0.454, 0.768]]\nC: [[0.504, 0.831, 0.74, 0.202, 0.254, 0.39]]\nD: [[0.93, 1.224, 1.103, 0.115, -0.143, 0.862]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.403, 0.709, 0.086, 0.284, 0.032, 0.061]]\nB: [[1.358, 0.357, -0.003, 0.163, 0.142, 0.021]]\nC: [[1.451, 0.553, 0.13, 0.387, 0.236, 0.338]]\nD: [[1.592, 0.722, 0.492, 0.54, 0.067, 0.402]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_136_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_136_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the trash can in the scene. The camera pose information includes: the rotation matrix: [[-0.573389, -0.355745, 0.738018], [-0.818965, 0.223754, -0.528424], [0.02285, -0.907403, -0.419641]]; the translation vector: [2.061407, 3.857203, 1.382209], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.403, 0.709, 0.086, 0.284, 0.032, 0.061]]\nB: [[1.358, 0.357, -0.003, 0.163, 0.142, 0.021]]\nC: [[1.451, 0.553, 0.13, 0.387, 0.236, 0.338]]\nD: [[1.592, 0.722, 0.492, 0.54, 0.067, 0.402]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.019, 1.716, -0.199, 0.133, 0.443, 0.08]]\nB: [[1.539, 1.317, 0.169, 1.021, 0.789, 0.672]]\nC: [[1.691, 1.543, 0.248, 0.524, 0.565, 0.475]]\nD: [[1.676, 1.663, -0.114, 0.76, 0.881, 0.004]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_137_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_137_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the footrest in the scene. The camera pose information includes: the rotation matrix: [[-0.752388, 0.33007, -0.570058], [0.655329, 0.287372, -0.698542], [-0.066749, -0.89915, -0.43252]]; the translation vector: [3.814293, 2.583141, 1.394159], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.019, 1.716, -0.199, 0.133, 0.443, 0.08]]\nB: [[1.539, 1.317, 0.169, 1.021, 0.789, 0.672]]\nC: [[1.691, 1.543, 0.248, 0.524, 0.565, 0.475]]\nD: [[1.676, 1.663, -0.114, 0.76, 0.881, 0.004]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.123, 1.117, 1.321, 0.743, 0.381, 0.635], [2.682, 1.29, 0.828, 0.463, 1.156, 0.705], [2.834, 1.547, 0.399, 0.032, 0.368, 0.16], [3.096, 0.047, 0.87, -0.078, 0.326, -0.125], [2.563, 0.993, 1.356, 0.811, 0.73, 0.229], [2.143, 0.969, 1.037, 0.642, 1.052, 0.055], [2.659, -0.615, 0.836, 0.248, 1.054, 0.022], [3.223, -0.649, 1.168, 0.253, 1.288, 0.671], [2.454, -0.393, 0.682, 0.657, 1.137, 0.691], [-3.644, 1.213, 1.46, 0.605, 1.274, 0.706], [-3.657, -0.185, 0.669, 0.323, 1.16, 0.8], [-3.228, -0.103, 1.329, 0.441, 0.997, 0.754], [-3.305, 0.306, 0.543, 0.056, 1.942, 0.326], [-3.554, -0.503, 0.414, 0.642, 0.665, 0.745], [-2.947, -0.695, 0.368, 0.59, 0.436, 0.372], [-3.593, -0.106, 0.806, 0.216, 0.592, 0.301]]\nB: [[2.989, 1.126, 0.794, -0.179, 0.245, 0.369], [2.962, 1.061, 0.623, -0.057, 0.36, 0.431], [2.845, 1.185, 0.945, 0.308, 0.535, 0.574], [2.424, 0.962, 1.637, -0.272, 0.494, 0.77], [3.085, 0.394, 0.93, 0.245, 0.901, 0.482], [2.87, 0.321, 0.254, 0.308, 0.264, 0.679], [2.834, -0.509, 1.34, 0.641, 0.49, 0.271], [2.993, -0.295, 0.769, -0.075, 1.002, 0.589], [3.132, -0.129, 0.78, 0.069, 1.025, 0.007], [-2.917, 1.638, 1.353, 0.35, 0.736, 0.591], [-2.828, -0.168, 1.186, 0.057, 1.347, 0.51], [-3.297, -0.456, 0.362, 0.307, 0.654, 0.781], [-3.301, 0.612, 0.703, 0.328, 1.414, 0.306], [-2.89, -0.213, 0.298, -0.086, 1.058, 0.488], [-2.855, -0.016, -0.219, -0.168, 0.422, -0.035], [-3.555, 0.252, 0.516, -0.109, 1.029, 0.664]]\nC: [[2.568, 1.418, 1.271, 0.257, 0.709, 0.306], [2.646, 1.448, 0.95, 0.305, 0.76, 0.302], [2.592, 1.461, 0.636, 0.212, 0.718, 0.28], [2.65, 0.514, 1.213, 0.222, 0.814, 0.309], [2.738, 0.497, 0.888, 0.381, 0.863, 0.308], [2.639, 0.563, 0.627, 0.305, 0.736, 0.188], [2.693, -0.392, 1.14, 0.281, 0.891, 0.334], [2.727, -0.372, 0.833, 0.29, 0.926, 0.3], [2.691, -0.383, 0.563, 0.264, 0.854, 0.201], [-3.22, 1.231, 1.017, 0.313, 0.915, 0.346], [-3.273, 0.289, 0.923, 0.23, 1.204, 0.355], [-3.222, -0.487, 0.833, 0.334, 0.747, 0.368], [-3.341, 0.627, 0.626, 0.449, 1.466, 0.437], [-3.265, -0.411, 0.526, 0.337, 0.641, 0.343], [-3.203, -0.328, 0.27, 0.175, 0.592, 0.204], [-3.277, 0.365, 0.338, 0.332, 0.934, 0.242]]\nD: [[2.244, 1.249, 1.196, 0.59, 0.671, 0.591], [3.002, 1.584, 0.459, 0.732, 0.625, -0.064], [2.803, 1.399, 0.195, 0.554, 0.24, -0.185], [2.948, 0.428, 1.564, 0.649, 0.642, 0.076], [2.502, 0.944, 1.279, 0.724, 1.079, 0.788], [3.063, 0.247, 0.912, 0.247, 0.578, 0.126], [2.848, -0.809, 0.778, 0.441, 1.15, 0.263], [2.483, -0.756, 0.605, 0.63, 1.407, 0.292], [2.369, -0.586, 0.732, 0.348, 0.461, 0.12], [-3.238, 0.78, 0.778, 0.212, 1.143, -0.102], [-3.116, 0.426, 0.879, 0.248, 1.646, 0.306], [-2.875, -0.393, 1.087, 0.035, 1.245, 0.038], [-3.308, 0.845, 1.118, 0.472, 1.582, 0.109], [-3.33, -0.848, 0.583, 0.088, 1.108, -0.004], [-3.371, -0.081, 0.236, -0.02, 0.647, 0.543], [-3.267, -0.114, -0.13, -0.134, 1.197, -0.109]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_138_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_138_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the books in the scene. The camera pose information includes: the rotation matrix: [[0.892065, -0.360019, 0.273141], [-0.443019, -0.577417, 0.685801], [-0.089185, -0.732786, -0.674589]]; the translation vector: [2.898737, 2.45906, 1.649541], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.123, 1.117, 1.321, 0.743, 0.381, 0.635], [2.682, 1.29, 0.828, 0.463, 1.156, 0.705], [2.834, 1.547, 0.399, 0.032, 0.368, 0.16], [3.096, 0.047, 0.87, -0.078, 0.326, -0.125], [2.563, 0.993, 1.356, 0.811, 0.73, 0.229], [2.143, 0.969, 1.037, 0.642, 1.052, 0.055], [2.659, -0.615, 0.836, 0.248, 1.054, 0.022], [3.223, -0.649, 1.168, 0.253, 1.288, 0.671], [2.454, -0.393, 0.682, 0.657, 1.137, 0.691], [-3.644, 1.213, 1.46, 0.605, 1.274, 0.706], [-3.657, -0.185, 0.669, 0.323, 1.16, 0.8], [-3.228, -0.103, 1.329, 0.441, 0.997, 0.754], [-3.305, 0.306, 0.543, 0.056, 1.942, 0.326], [-3.554, -0.503, 0.414, 0.642, 0.665, 0.745], [-2.947, -0.695, 0.368, 0.59, 0.436, 0.372], [-3.593, -0.106, 0.806, 0.216, 0.592, 0.301]]\nB: [[2.989, 1.126, 0.794, -0.179, 0.245, 0.369], [2.962, 1.061, 0.623, -0.057, 0.36, 0.431], [2.845, 1.185, 0.945, 0.308, 0.535, 0.574], [2.424, 0.962, 1.637, -0.272, 0.494, 0.77], [3.085, 0.394, 0.93, 0.245, 0.901, 0.482], [2.87, 0.321, 0.254, 0.308, 0.264, 0.679], [2.834, -0.509, 1.34, 0.641, 0.49, 0.271], [2.993, -0.295, 0.769, -0.075, 1.002, 0.589], [3.132, -0.129, 0.78, 0.069, 1.025, 0.007], [-2.917, 1.638, 1.353, 0.35, 0.736, 0.591], [-2.828, -0.168, 1.186, 0.057, 1.347, 0.51], [-3.297, -0.456, 0.362, 0.307, 0.654, 0.781], [-3.301, 0.612, 0.703, 0.328, 1.414, 0.306], [-2.89, -0.213, 0.298, -0.086, 1.058, 0.488], [-2.855, -0.016, -0.219, -0.168, 0.422, -0.035], [-3.555, 0.252, 0.516, -0.109, 1.029, 0.664]]\nC: [[2.568, 1.418, 1.271, 0.257, 0.709, 0.306], [2.646, 1.448, 0.95, 0.305, 0.76, 0.302], [2.592, 1.461, 0.636, 0.212, 0.718, 0.28], [2.65, 0.514, 1.213, 0.222, 0.814, 0.309], [2.738, 0.497, 0.888, 0.381, 0.863, 0.308], [2.639, 0.563, 0.627, 0.305, 0.736, 0.188], [2.693, -0.392, 1.14, 0.281, 0.891, 0.334], [2.727, -0.372, 0.833, 0.29, 0.926, 0.3], [2.691, -0.383, 0.563, 0.264, 0.854, 0.201], [-3.22, 1.231, 1.017, 0.313, 0.915, 0.346], [-3.273, 0.289, 0.923, 0.23, 1.204, 0.355], [-3.222, -0.487, 0.833, 0.334, 0.747, 0.368], [-3.341, 0.627, 0.626, 0.449, 1.466, 0.437], [-3.265, -0.411, 0.526, 0.337, 0.641, 0.343], [-3.203, -0.328, 0.27, 0.175, 0.592, 0.204], [-3.277, 0.365, 0.338, 0.332, 0.934, 0.242]]\nD: [[2.244, 1.249, 1.196, 0.59, 0.671, 0.591], [3.002, 1.584, 0.459, 0.732, 0.625, -0.064], [2.803, 1.399, 0.195, 0.554, 0.24, -0.185], [2.948, 0.428, 1.564, 0.649, 0.642, 0.076], [2.502, 0.944, 1.279, 0.724, 1.079, 0.788], [3.063, 0.247, 0.912, 0.247, 0.578, 0.126], [2.848, -0.809, 0.778, 0.441, 1.15, 0.263], [2.483, -0.756, 0.605, 0.63, 1.407, 0.292], [2.369, -0.586, 0.732, 0.348, 0.461, 0.12], [-3.238, 0.78, 0.778, 0.212, 1.143, -0.102], [-3.116, 0.426, 0.879, 0.248, 1.646, 0.306], [-2.875, -0.393, 1.087, 0.035, 1.245, 0.038], [-3.308, 0.845, 1.118, 0.472, 1.582, 0.109], [-3.33, -0.848, 0.583, 0.088, 1.108, -0.004], [-3.371, -0.081, 0.236, -0.02, 0.647, 0.543], [-3.267, -0.114, -0.13, -0.134, 1.197, -0.109]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.568, 1.594, 2.336, 3.002, 0.215, 1.78], [-1.527, -0.451, 1.258, 0.103, 4.121, 1.756], [-1.386, -2.636, 1.043, -0.207, -0.22, 1.193], [1.834, 0.934, 1.595, 0.321, 1.764, 2.84], [1.018, -0.319, 0.583, 1.01, 0.681, 1.687]]\nB: [[0.072, 1.537, 1.845, 2.689, 0.191, 1.622], [-1.273, -0.316, 0.956, 0.156, 3.767, 1.891], [-1.14, -2.18, 0.679, 0.246, 0.067, 1.34], [1.381, 0.651, 1.354, 0.135, 1.692, 2.602], [0.889, -0.737, 0.87, 1.059, 1.122, 1.773]]\nC: [[0.21, 1.662, 1.825, 2.75, -0.252, 2.024], [-1.56, -0.058, 0.561, 0.054, 3.741, 2.333], [-1.055, -2.665, 0.535, 0.196, 0.05, 1.825], [1.164, 0.58, 1.628, 0.045, 1.482, 2.195], [1.198, -0.291, 1.331, 0.727, 1.34, 1.309]]\nD: [[-0.147, 1.793, 1.85, 3.103, 0.596, 1.69], [-1.538, -0.388, 0.463, 0.445, 3.441, 1.475], [-1.625, -1.946, 0.934, 0.072, -0.182, 1.409], [1.247, 1.123, 0.994, 0.033, 1.379, 2.521], [0.847, -0.38, 0.424, 0.888, 1.469, 2.148]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_139_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_139_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.721847, -0.019511, -0.691778], [0.690918, -0.036893, 0.721991], [-0.039608, -0.999129, -0.013151]]; the translation vector: [1.871862, 0.815296, 1.594356], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.568, 1.594, 2.336, 3.002, 0.215, 1.78], [-1.527, -0.451, 1.258, 0.103, 4.121, 1.756], [-1.386, -2.636, 1.043, -0.207, -0.22, 1.193], [1.834, 0.934, 1.595, 0.321, 1.764, 2.84], [1.018, -0.319, 0.583, 1.01, 0.681, 1.687]]\nB: [[0.072, 1.537, 1.845, 2.689, 0.191, 1.622], [-1.273, -0.316, 0.956, 0.156, 3.767, 1.891], [-1.14, -2.18, 0.679, 0.246, 0.067, 1.34], [1.381, 0.651, 1.354, 0.135, 1.692, 2.602], [0.889, -0.737, 0.87, 1.059, 1.122, 1.773]]\nC: [[0.21, 1.662, 1.825, 2.75, -0.252, 2.024], [-1.56, -0.058, 0.561, 0.054, 3.741, 2.333], [-1.055, -2.665, 0.535, 0.196, 0.05, 1.825], [1.164, 0.58, 1.628, 0.045, 1.482, 2.195], [1.198, -0.291, 1.331, 0.727, 1.34, 1.309]]\nD: [[-0.147, 1.793, 1.85, 3.103, 0.596, 1.69], [-1.538, -0.388, 0.463, 0.445, 3.441, 1.475], [-1.625, -1.946, 0.934, 0.072, -0.182, 1.409], [1.247, 1.123, 0.994, 0.033, 1.379, 2.521], [0.847, -0.38, 0.424, 0.888, 1.469, 2.148]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.013, -1.238, 0.461, 0.493, 0.288, 0.575], [-1.147, 3.348, 0.377, 0.564, 1.212, 0.508], [0.233, -3.522, 0.086, 0.938, 1.233, 0.371], [1.742, -2.531, 0.357, 1.04, 1.008, 0.183], [-0.823, -2.335, 0.1, 1.256, 0.934, 0.241], [-1.592, 0.953, 0.372, 1.131, 0.478, 0.788], [-0.027, 4.368, 1.235, 0.272, -0.107, 0.356], [2.853, 3.344, 0.339, 0.665, 0.157, 0.567], [2.027, 4.048, 1.116, 0.515, 0.345, 0.52], [0.87, 3.839, 0.8, 0.696, -0.287, 0.501], [1.369, 2.326, 0.538, 0.245, 0.786, 0.243], [0.357, 3.043, 0.662, 0.778, 0.111, 0.513], [1.447, 2.656, 0.359, 0.141, 0.33, 0.84]]\nB: [[0.068, -1.042, 0.544, 0.886, 0.779, 0.545], [-1.479, 3.034, 0.552, 0.898, 0.801, 0.497], [-0.06, -3.112, 0.543, 0.84, 0.784, 0.511], [1.274, -2.138, 0.543, 0.738, 0.855, 0.547], [-0.786, -2.2, 0.536, 0.806, 0.879, 0.474], [-1.39, 1.148, 0.549, 0.822, 0.745, 0.54], [0.444, 4.003, 0.791, 0.485, 0.139, 0.082], [2.511, 3.843, 0.762, 0.448, 0.131, 0.083], [1.884, 3.916, 0.775, 0.46, 0.149, 0.083], [1.153, 3.946, 0.791, 0.453, 0.166, 0.098], [1.053, 2.651, 0.606, 0.523, 0.61, 0.485], [0.449, 2.899, 0.606, 0.476, 0.557, 0.467], [1.688, 2.596, 0.605, 0.503, 0.592, 0.451]]\nC: [[0.102, -0.947, 0.484, 1.245, 0.79, 0.775], [-1.118, 3.375, 0.842, 0.401, 1.069, 0.196], [0.407, -2.782, 0.934, 1.07, 0.467, 0.067], [1.541, -2.237, 0.403, 0.888, 1.246, 0.245], [-0.917, -1.889, 0.628, 0.956, 1.204, 0.523], [-1.021, 1.176, 0.814, 0.368, 0.456, 0.678], [0.573, 4.084, 1.228, 0.815, 0.355, 0.385], [2.848, 3.659, 0.488, 0.047, 0.047, 0.092], [1.907, 4.123, 0.733, 0.026, 0.33, -0.009], [1.212, 4.443, 1.139, 0.078, -0.234, 0.21], [0.892, 2.632, 1.105, 0.392, 1.061, 0.435], [0.166, 3.349, 0.352, 0.282, 0.481, 0.755], [1.529, 2.634, 0.397, 0.324, 0.54, 0.072]]\nD: [[-0.194, -1.122, 0.104, 1.378, 1.12, 0.253], [-1.005, 3.518, 0.745, 0.428, 0.792, 0.08], [0.214, -2.901, 0.412, 0.728, 0.43, 0.91], [1.573, -2.219, 0.557, 0.934, 1.13, 0.876], [-0.782, -2.154, 0.858, 0.543, 1.135, 0.108], [-1.448, 1.097, 0.92, 1.197, 0.497, 0.181], [0.045, 3.571, 0.423, 0.736, -0.143, -0.417], [2.244, 4.297, 0.746, 0.101, 0.473, -0.26], [1.879, 3.692, 0.375, 0.596, -0.051, -0.206], [1.372, 4.096, 0.929, 0.827, -0.125, 0.334], [1.326, 2.984, 0.19, 0.493, 0.248, 0.576], [0.74, 2.996, 0.477, 0.655, 0.254, 0.849], [2.003, 3.037, 0.818, 0.844, 0.675, 0.272]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_140_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_140_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[0.853196, -0.330732, 0.403328], [-0.517406, -0.438892, 0.734619], [-0.065945, -0.835458, -0.545584]]; the translation vector: [2.734716, 6.775187, 1.412962], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.013, -1.238, 0.461, 0.493, 0.288, 0.575], [-1.147, 3.348, 0.377, 0.564, 1.212, 0.508], [0.233, -3.522, 0.086, 0.938, 1.233, 0.371], [1.742, -2.531, 0.357, 1.04, 1.008, 0.183], [-0.823, -2.335, 0.1, 1.256, 0.934, 0.241], [-1.592, 0.953, 0.372, 1.131, 0.478, 0.788], [-0.027, 4.368, 1.235, 0.272, -0.107, 0.356], [2.853, 3.344, 0.339, 0.665, 0.157, 0.567], [2.027, 4.048, 1.116, 0.515, 0.345, 0.52], [0.87, 3.839, 0.8, 0.696, -0.287, 0.501], [1.369, 2.326, 0.538, 0.245, 0.786, 0.243], [0.357, 3.043, 0.662, 0.778, 0.111, 0.513], [1.447, 2.656, 0.359, 0.141, 0.33, 0.84]]\nB: [[0.068, -1.042, 0.544, 0.886, 0.779, 0.545], [-1.479, 3.034, 0.552, 0.898, 0.801, 0.497], [-0.06, -3.112, 0.543, 0.84, 0.784, 0.511], [1.274, -2.138, 0.543, 0.738, 0.855, 0.547], [-0.786, -2.2, 0.536, 0.806, 0.879, 0.474], [-1.39, 1.148, 0.549, 0.822, 0.745, 0.54], [0.444, 4.003, 0.791, 0.485, 0.139, 0.082], [2.511, 3.843, 0.762, 0.448, 0.131, 0.083], [1.884, 3.916, 0.775, 0.46, 0.149, 0.083], [1.153, 3.946, 0.791, 0.453, 0.166, 0.098], [1.053, 2.651, 0.606, 0.523, 0.61, 0.485], [0.449, 2.899, 0.606, 0.476, 0.557, 0.467], [1.688, 2.596, 0.605, 0.503, 0.592, 0.451]]\nC: [[0.102, -0.947, 0.484, 1.245, 0.79, 0.775], [-1.118, 3.375, 0.842, 0.401, 1.069, 0.196], [0.407, -2.782, 0.934, 1.07, 0.467, 0.067], [1.541, -2.237, 0.403, 0.888, 1.246, 0.245], [-0.917, -1.889, 0.628, 0.956, 1.204, 0.523], [-1.021, 1.176, 0.814, 0.368, 0.456, 0.678], [0.573, 4.084, 1.228, 0.815, 0.355, 0.385], [2.848, 3.659, 0.488, 0.047, 0.047, 0.092], [1.907, 4.123, 0.733, 0.026, 0.33, -0.009], [1.212, 4.443, 1.139, 0.078, -0.234, 0.21], [0.892, 2.632, 1.105, 0.392, 1.061, 0.435], [0.166, 3.349, 0.352, 0.282, 0.481, 0.755], [1.529, 2.634, 0.397, 0.324, 0.54, 0.072]]\nD: [[-0.194, -1.122, 0.104, 1.378, 1.12, 0.253], [-1.005, 3.518, 0.745, 0.428, 0.792, 0.08], [0.214, -2.901, 0.412, 0.728, 0.43, 0.91], [1.573, -2.219, 0.557, 0.934, 1.13, 0.876], [-0.782, -2.154, 0.858, 0.543, 1.135, 0.108], [-1.448, 1.097, 0.92, 1.197, 0.497, 0.181], [0.045, 3.571, 0.423, 0.736, -0.143, -0.417], [2.244, 4.297, 0.746, 0.101, 0.473, -0.26], [1.879, 3.692, 0.375, 0.596, -0.051, -0.206], [1.372, 4.096, 0.929, 0.827, -0.125, 0.334], [1.326, 2.984, 0.19, 0.493, 0.248, 0.576], [0.74, 2.996, 0.477, 0.655, 0.254, 0.849], [2.003, 3.037, 0.818, 0.844, 0.675, 0.272]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.478, -1.621, 1.471, 0.04, 0.607, 0.77], [-0.622, -0.28, 0.778, 0.596, 0.791, 0.172], [0.68, -2.333, 1.441, 0.527, 0.774, 0.878], [0.47, -3.455, 1.236, 0.145, 0.605, 0.702], [-1.07, -2.607, 1.289, 0.421, 0.201, 0.117], [-0.819, -3.811, 1.501, 0.378, 0.459, 0.305], [-0.289, -1.598, 0.306, 0.193, 0.374, 1.351], [-0.144, -0.28, 1.064, 0.516, 0.462, 1.314], [-0.472, 1.134, 0.95, 0.612, 0.425, 0.242], [0.292, 1.559, -0.0, 1.024, 0.739, 0.637], [1.632, 1.532, 0.377, 0.961, 0.147, 0.54], [1.253, 1.503, 0.223, 0.356, 0.173, 0.917], [2.079, 0.639, 0.524, 0.497, 0.63, 1.101], [1.452, -0.032, 0.35, 1.029, 0.429, 0.469], [1.964, -1.067, 0.351, 1.202, 1.067, 0.649], [1.915, -1.339, 0.962, 0.392, 0.481, -0.02]]\nB: [[-1.05, -1.003, 1.329, 0.32, 0.076, 0.616], [-0.594, -0.426, 0.767, 0.622, 0.307, 0.007], [0.602, -2.305, 1.043, 0.218, 0.243, 0.681], [0.924, -3.409, 0.98, 0.773, 0.471, 1.089], [-0.329, -2.354, 0.789, 0.408, 0.875, 0.623], [-0.349, -3.787, 1.449, 0.31, 0.976, 0.266], [0.652, -1.018, 1.006, 0.796, 0.883, 0.697], [0.628, -0.604, 0.772, 0.114, 0.996, 0.953], [-1.118, 1.128, 0.061, 0.216, 0.338, 0.764], [0.65, 1.585, 0.323, 0.699, 0.859, 0.499], [1.631, 1.493, 0.088, 1.244, 0.636, 1.121], [1.187, 0.927, 0.824, 0.22, 0.275, 0.894], [1.693, 0.178, 0.2, 0.357, 0.96, 0.555], [1.798, -0.426, 0.556, 0.111, 1.016, 0.592], [1.891, -0.692, 0.467, 0.91, 1.42, 0.916], [1.538, -2.029, 0.941, 0.82, 1.037, 0.527]]\nC: [[-0.797, -1.314, 1.076, 0.174, 0.538, 0.297], [-0.804, -0.643, 0.993, 0.18, 0.483, 0.317], [0.265, -2.771, 0.976, 0.564, 0.483, 0.724], [0.443, -3.263, 1.105, 0.404, 0.755, 0.654], [-0.786, -2.701, 1.224, 0.235, 0.623, 0.423], [-0.579, -3.467, 1.386, 0.149, 0.491, 0.284], [0.195, -1.173, 0.617, 0.439, 0.594, 0.981], [0.152, -0.693, 0.576, 0.363, 0.648, 0.901], [-0.836, 1.438, 0.551, 0.438, 0.598, 0.552], [0.258, 1.345, 0.466, 0.561, 0.507, 0.736], [1.246, 1.609, 0.396, 0.752, 0.566, 0.883], [1.646, 1.19, 0.575, 0.611, 0.592, 0.619], [1.73, 0.493, 0.521, 0.445, 0.583, 0.771], [1.766, -0.179, 0.551, 0.536, 0.58, 0.774], [1.864, -0.697, 0.533, 0.816, 1.199, 0.994], [1.74, -1.667, 0.652, 0.516, 0.607, 0.36]]\nD: [[-0.49, -1.289, 0.9, 0.491, 0.951, 0.59], [-1.107, -1.021, 1.479, 0.523, 0.505, 0.09], [-0.233, -2.971, 1.208, 0.309, 0.946, 0.617], [0.587, -2.842, 0.811, 0.828, 0.821, 0.621], [-0.674, -2.976, 1.257, -0.139, 0.206, 0.639], [-0.269, -3.606, 1.299, -0.169, 0.133, 0.486], [0.033, -0.697, 1.063, 0.567, 1.022, 1.265], [0.252, -0.714, 0.426, 0.514, 0.322, 1.359], [-1.161, 1.486, 0.647, 0.683, 0.314, 0.187], [-0.11, 1.173, 0.725, 0.462, 0.264, 1.138], [1.341, 1.682, 0.277, 0.312, 0.356, 0.94], [1.815, 1.188, 0.624, 1.015, 0.174, 0.508], [1.714, 0.423, 0.79, 0.889, 0.659, 0.533], [1.648, -0.367, 0.718, 0.468, 1.049, 0.941], [2.335, -0.44, 0.71, 1.148, 1.407, 0.783], [1.632, -1.945, 0.223, 0.453, 0.239, 0.703]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_141_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_141_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.476704, 0.41796, -0.773345], [0.878176, 0.186897, -0.440314], [-0.039498, -0.889033, -0.456137]]; the translation vector: [2.405627, 4.675593, 1.276166], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.478, -1.621, 1.471, 0.04, 0.607, 0.77], [-0.622, -0.28, 0.778, 0.596, 0.791, 0.172], [0.68, -2.333, 1.441, 0.527, 0.774, 0.878], [0.47, -3.455, 1.236, 0.145, 0.605, 0.702], [-1.07, -2.607, 1.289, 0.421, 0.201, 0.117], [-0.819, -3.811, 1.501, 0.378, 0.459, 0.305], [-0.289, -1.598, 0.306, 0.193, 0.374, 1.351], [-0.144, -0.28, 1.064, 0.516, 0.462, 1.314], [-0.472, 1.134, 0.95, 0.612, 0.425, 0.242], [0.292, 1.559, -0.0, 1.024, 0.739, 0.637], [1.632, 1.532, 0.377, 0.961, 0.147, 0.54], [1.253, 1.503, 0.223, 0.356, 0.173, 0.917], [2.079, 0.639, 0.524, 0.497, 0.63, 1.101], [1.452, -0.032, 0.35, 1.029, 0.429, 0.469], [1.964, -1.067, 0.351, 1.202, 1.067, 0.649], [1.915, -1.339, 0.962, 0.392, 0.481, -0.02]]\nB: [[-1.05, -1.003, 1.329, 0.32, 0.076, 0.616], [-0.594, -0.426, 0.767, 0.622, 0.307, 0.007], [0.602, -2.305, 1.043, 0.218, 0.243, 0.681], [0.924, -3.409, 0.98, 0.773, 0.471, 1.089], [-0.329, -2.354, 0.789, 0.408, 0.875, 0.623], [-0.349, -3.787, 1.449, 0.31, 0.976, 0.266], [0.652, -1.018, 1.006, 0.796, 0.883, 0.697], [0.628, -0.604, 0.772, 0.114, 0.996, 0.953], [-1.118, 1.128, 0.061, 0.216, 0.338, 0.764], [0.65, 1.585, 0.323, 0.699, 0.859, 0.499], [1.631, 1.493, 0.088, 1.244, 0.636, 1.121], [1.187, 0.927, 0.824, 0.22, 0.275, 0.894], [1.693, 0.178, 0.2, 0.357, 0.96, 0.555], [1.798, -0.426, 0.556, 0.111, 1.016, 0.592], [1.891, -0.692, 0.467, 0.91, 1.42, 0.916], [1.538, -2.029, 0.941, 0.82, 1.037, 0.527]]\nC: [[-0.797, -1.314, 1.076, 0.174, 0.538, 0.297], [-0.804, -0.643, 0.993, 0.18, 0.483, 0.317], [0.265, -2.771, 0.976, 0.564, 0.483, 0.724], [0.443, -3.263, 1.105, 0.404, 0.755, 0.654], [-0.786, -2.701, 1.224, 0.235, 0.623, 0.423], [-0.579, -3.467, 1.386, 0.149, 0.491, 0.284], [0.195, -1.173, 0.617, 0.439, 0.594, 0.981], [0.152, -0.693, 0.576, 0.363, 0.648, 0.901], [-0.836, 1.438, 0.551, 0.438, 0.598, 0.552], [0.258, 1.345, 0.466, 0.561, 0.507, 0.736], [1.246, 1.609, 0.396, 0.752, 0.566, 0.883], [1.646, 1.19, 0.575, 0.611, 0.592, 0.619], [1.73, 0.493, 0.521, 0.445, 0.583, 0.771], [1.766, -0.179, 0.551, 0.536, 0.58, 0.774], [1.864, -0.697, 0.533, 0.816, 1.199, 0.994], [1.74, -1.667, 0.652, 0.516, 0.607, 0.36]]\nD: [[-0.49, -1.289, 0.9, 0.491, 0.951, 0.59], [-1.107, -1.021, 1.479, 0.523, 0.505, 0.09], [-0.233, -2.971, 1.208, 0.309, 0.946, 0.617], [0.587, -2.842, 0.811, 0.828, 0.821, 0.621], [-0.674, -2.976, 1.257, -0.139, 0.206, 0.639], [-0.269, -3.606, 1.299, -0.169, 0.133, 0.486], [0.033, -0.697, 1.063, 0.567, 1.022, 1.265], [0.252, -0.714, 0.426, 0.514, 0.322, 1.359], [-1.161, 1.486, 0.647, 0.683, 0.314, 0.187], [-0.11, 1.173, 0.725, 0.462, 0.264, 1.138], [1.341, 1.682, 0.277, 0.312, 0.356, 0.94], [1.815, 1.188, 0.624, 1.015, 0.174, 0.508], [1.714, 0.423, 0.79, 0.889, 0.659, 0.533], [1.648, -0.367, 0.718, 0.468, 1.049, 0.941], [2.335, -0.44, 0.71, 1.148, 1.407, 0.783], [1.632, -1.945, 0.223, 0.453, 0.239, 0.703]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.214, 2.51, 1.365, 3.041, 0.194, 2.765], [-0.05, -2.478, 0.301, 2.986, 0.3, 0.641], [-1.526, -0.161, 1.336, 0.225, 4.64, 2.734]]\nB: [[0.048, 2.151, 1.156, 2.76, 0.655, 2.821], [0.225, -2.827, 0.677, 3.049, -0.043, 0.254], [-1.759, -0.568, 1.729, -0.249, 5.02, 2.969]]\nC: [[0.558, 2.736, 1.619, 3.45, -0.161, 2.854], [-0.367, -2.102, 0.777, 2.594, 0.161, 0.236], [-1.277, -0.254, 1.397, -0.136, 4.615, 2.411]]\nD: [[0.699, 2.21, 1.721, 3.445, -0.097, 2.767], [0.274, -2.743, -0.017, 2.983, 0.564, 0.816], [-1.467, -0.193, 1.628, 0.718, 4.962, 2.711]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_142_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_142_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.207785, -0.462455, 0.861952], [-0.977184, 0.13779, -0.161637], [-0.044019, -0.875871, -0.480534]]; the translation vector: [2.720584, 1.654419, 1.522448], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.214, 2.51, 1.365, 3.041, 0.194, 2.765], [-0.05, -2.478, 0.301, 2.986, 0.3, 0.641], [-1.526, -0.161, 1.336, 0.225, 4.64, 2.734]]\nB: [[0.048, 2.151, 1.156, 2.76, 0.655, 2.821], [0.225, -2.827, 0.677, 3.049, -0.043, 0.254], [-1.759, -0.568, 1.729, -0.249, 5.02, 2.969]]\nC: [[0.558, 2.736, 1.619, 3.45, -0.161, 2.854], [-0.367, -2.102, 0.777, 2.594, 0.161, 0.236], [-1.277, -0.254, 1.397, -0.136, 4.615, 2.411]]\nD: [[0.699, 2.21, 1.721, 3.445, -0.097, 2.767], [0.274, -2.743, -0.017, 2.983, 0.564, 0.816], [-1.467, -0.193, 1.628, 0.718, 4.962, 2.711]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.781, -0.092, 1.437, 0.297, 5.42, 2.838], [-0.225, -2.203, -0.028, 3.04, -0.15, 0.159], [0.563, 2.457, 1.86, 3.628, 0.174, 3.621], [1.294, -0.516, 1.217, 0.088, 5.292, 2.913], [-1.437, -2.905, 0.867, 0.166, -0.17, 0.831]]\nB: [[-1.712, -0.169, 1.937, -0.166, 5.172, 3.249], [0.409, -2.556, 0.634, 3.096, 0.671, 0.11], [-0.287, 2.175, 1.704, 3.703, 0.15, 2.806], [2.158, 0.248, 1.0, 0.669, 5.195, 2.453], [-1.117, -2.267, 1.561, 0.31, -0.422, 1.139]]\nC: [[-1.796, -0.26, 1.071, 0.363, 4.986, 2.747], [-0.333, -2.47, 0.362, 3.532, -0.124, 0.597], [0.208, 2.122, 1.319, 3.656, -0.186, 2.723], [1.521, -0.537, 0.986, 0.704, 5.101, 2.943], [-1.457, -2.856, 0.86, 0.281, 0.313, 0.878]]\nD: [[-1.474, 0.024, 1.526, 0.216, 4.974, 3.09], [0.118, -2.408, 0.332, 3.201, 0.275, 0.54], [0.144, 2.522, 1.535, 3.347, 0.23, 3.137], [1.788, -0.144, 1.382, 0.213, 5.326, 2.779], [-1.437, -2.464, 1.35, 0.243, 0.036, 0.743]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_143_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_143_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.45377, -0.425062, 0.783208], [-0.891046, 0.227634, -0.392708], [-0.01136, -0.876074, -0.482043]]; the translation vector: [2.25004, 3.862298, 1.519108], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.781, -0.092, 1.437, 0.297, 5.42, 2.838], [-0.225, -2.203, -0.028, 3.04, -0.15, 0.159], [0.563, 2.457, 1.86, 3.628, 0.174, 3.621], [1.294, -0.516, 1.217, 0.088, 5.292, 2.913], [-1.437, -2.905, 0.867, 0.166, -0.17, 0.831]]\nB: [[-1.712, -0.169, 1.937, -0.166, 5.172, 3.249], [0.409, -2.556, 0.634, 3.096, 0.671, 0.11], [-0.287, 2.175, 1.704, 3.703, 0.15, 2.806], [2.158, 0.248, 1.0, 0.669, 5.195, 2.453], [-1.117, -2.267, 1.561, 0.31, -0.422, 1.139]]\nC: [[-1.796, -0.26, 1.071, 0.363, 4.986, 2.747], [-0.333, -2.47, 0.362, 3.532, -0.124, 0.597], [0.208, 2.122, 1.319, 3.656, -0.186, 2.723], [1.521, -0.537, 0.986, 0.704, 5.101, 2.943], [-1.457, -2.856, 0.86, 0.281, 0.313, 0.878]]\nD: [[-1.474, 0.024, 1.526, 0.216, 4.974, 3.09], [0.118, -2.408, 0.332, 3.201, 0.275, 0.54], [0.144, 2.522, 1.535, 3.347, 0.23, 3.137], [1.788, -0.144, 1.382, 0.213, 5.326, 2.779], [-1.437, -2.464, 1.35, 0.243, 0.036, 0.743]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.29, -0.316, 1.432, 0.063, 4.92, 2.21], [-1.382, -1.33, 2.175, 0.695, 0.538, 0.2], [-1.806, -2.363, 0.83, 0.352, 1.114, 1.836], [-1.306, -1.788, 0.815, 0.84, 0.3, 1.933], [-2.082, -0.191, 1.078, 0.269, 3.774, 1.876], [-1.331, 2.183, 1.747, 0.179, -0.216, 1.553], [-1.305, 2.378, 1.485, 0.225, 1.117, 1.988], [0.552, 2.732, 1.097, 2.615, 0.213, 2.449]]\nB: [[2.019, 0.108, 1.002, 0.215, 5.208, 2.059], [-1.022, -1.729, 2.165, 0.645, 0.146, 0.247], [-1.383, -1.998, 1.17, 0.215, 0.925, 2.24], [-1.526, -1.582, 1.263, 0.348, 0.152, 2.156], [-1.694, 0.207, 1.164, 0.178, 3.686, 2.357], [-1.644, 1.973, 1.343, 0.176, 0.151, 1.146], [-1.605, 2.692, 1.055, 0.124, 1.358, 1.982], [0.625, 2.804, 0.995, 2.8, 0.361, 2.1]]\nC: [[1.702, 0.103, 1.118, 0.347, 5.169, 2.205], [-0.755, -1.506, 2.319, 1.022, 0.542, -0.064], [-1.248, -1.952, 1.27, 0.08, 1.199, 2.239], [-1.042, -1.657, 1.027, 0.155, -0.197, 2.421], [-1.513, 0.045, 1.167, -0.103, 3.723, 2.465], [-1.23, 1.582, 1.115, -0.014, -0.31, 1.511], [-1.196, 2.213, 1.364, -0.205, 1.046, 1.714], [0.962, 2.867, 0.955, 2.429, 0.313, 2.593]]\nD: [[2.083, 0.385, 1.347, 0.273, 5.186, 1.86], [-0.546, -1.555, 1.851, 0.975, 0.412, 0.638], [-1.077, -1.883, 1.417, -0.014, 0.602, 2.249], [-1.395, -1.99, 1.177, -0.094, -0.079, 2.003], [-1.5, 0.548, 1.221, 0.453, 3.489, 2.126], [-2.081, 1.694, 1.43, -0.163, 0.443, 1.038], [-1.256, 2.343, 0.839, 0.584, 1.506, 1.621], [0.155, 3.04, 0.757, 2.991, 0.014, 2.136]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_144_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_144_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.804414, -0.195207, 0.561082], [-0.593456, -0.306943, 0.74404], [0.026978, -0.931494, -0.362756]]; the translation vector: [4.397897, 1.805397, 1.263968], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.29, -0.316, 1.432, 0.063, 4.92, 2.21], [-1.382, -1.33, 2.175, 0.695, 0.538, 0.2], [-1.806, -2.363, 0.83, 0.352, 1.114, 1.836], [-1.306, -1.788, 0.815, 0.84, 0.3, 1.933], [-2.082, -0.191, 1.078, 0.269, 3.774, 1.876], [-1.331, 2.183, 1.747, 0.179, -0.216, 1.553], [-1.305, 2.378, 1.485, 0.225, 1.117, 1.988], [0.552, 2.732, 1.097, 2.615, 0.213, 2.449]]\nB: [[2.019, 0.108, 1.002, 0.215, 5.208, 2.059], [-1.022, -1.729, 2.165, 0.645, 0.146, 0.247], [-1.383, -1.998, 1.17, 0.215, 0.925, 2.24], [-1.526, -1.582, 1.263, 0.348, 0.152, 2.156], [-1.694, 0.207, 1.164, 0.178, 3.686, 2.357], [-1.644, 1.973, 1.343, 0.176, 0.151, 1.146], [-1.605, 2.692, 1.055, 0.124, 1.358, 1.982], [0.625, 2.804, 0.995, 2.8, 0.361, 2.1]]\nC: [[1.702, 0.103, 1.118, 0.347, 5.169, 2.205], [-0.755, -1.506, 2.319, 1.022, 0.542, -0.064], [-1.248, -1.952, 1.27, 0.08, 1.199, 2.239], [-1.042, -1.657, 1.027, 0.155, -0.197, 2.421], [-1.513, 0.045, 1.167, -0.103, 3.723, 2.465], [-1.23, 1.582, 1.115, -0.014, -0.31, 1.511], [-1.196, 2.213, 1.364, -0.205, 1.046, 1.714], [0.962, 2.867, 0.955, 2.429, 0.313, 2.593]]\nD: [[2.083, 0.385, 1.347, 0.273, 5.186, 1.86], [-0.546, -1.555, 1.851, 0.975, 0.412, 0.638], [-1.077, -1.883, 1.417, -0.014, 0.602, 2.249], [-1.395, -1.99, 1.177, -0.094, -0.079, 2.003], [-1.5, 0.548, 1.221, 0.453, 3.489, 2.126], [-2.081, 1.694, 1.43, -0.163, 0.443, 1.038], [-1.256, 2.343, 0.839, 0.584, 1.506, 1.621], [0.155, 3.04, 0.757, 2.991, 0.014, 2.136]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.219, -0.964, 1.58, 0.354, 3.912, 2.347], [0.255, 0.996, 1.173, 3.495, 1.006, 2.248], [1.513, 0.137, 0.79, 0.63, 3.182, 2.432]]\nB: [[-1.818, -0.647, 1.066, 0.433, 4.095, 1.902], [0.12, 0.784, 1.447, 3.712, 0.386, 2.623], [1.292, -0.011, 0.89, 0.451, 3.017, 2.105]]\nC: [[-1.598, -0.539, 1.125, 0.503, 3.791, 2.392], [-0.019, 1.26, 1.209, 3.332, 0.548, 2.478], [1.708, -0.009, 1.196, 0.447, 2.783, 2.468]]\nD: [[-1.147, -0.143, 1.224, 0.476, 4.202, 2.039], [-0.033, 1.197, 1.039, 3.572, 0.489, 2.65], [1.648, -0.334, 1.403, 0.735, 3.031, 2.541]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_145_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_145_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.218501, -0.721835, 0.656667], [-0.97193, -0.10083, 0.212566], [-0.087226, -0.684681, -0.723605]]; the translation vector: [2.10902, 2.428258, 1.386435], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.219, -0.964, 1.58, 0.354, 3.912, 2.347], [0.255, 0.996, 1.173, 3.495, 1.006, 2.248], [1.513, 0.137, 0.79, 0.63, 3.182, 2.432]]\nB: [[-1.818, -0.647, 1.066, 0.433, 4.095, 1.902], [0.12, 0.784, 1.447, 3.712, 0.386, 2.623], [1.292, -0.011, 0.89, 0.451, 3.017, 2.105]]\nC: [[-1.598, -0.539, 1.125, 0.503, 3.791, 2.392], [-0.019, 1.26, 1.209, 3.332, 0.548, 2.478], [1.708, -0.009, 1.196, 0.447, 2.783, 2.468]]\nD: [[-1.147, -0.143, 1.224, 0.476, 4.202, 2.039], [-0.033, 1.197, 1.039, 3.572, 0.489, 2.65], [1.648, -0.334, 1.403, 0.735, 3.031, 2.541]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.089, 0.93, 0.906, 1.952, 6.847, 0.759], [2.364, 2.183, -0.461, 0.089, -0.064, -0.412], [0.126, -4.445, 0.264, 1.544, 0.593, 0.846], [1.593, -4.523, 0.942, 1.504, 1.198, 0.582]]\nB: [[0.288, 1.03, -0.052, 1.738, 7.022, 0.872], [3.315, 2.231, -0.328, 0.592, 0.336, -0.023], [0.311, -4.176, 1.057, 1.806, 0.812, 1.384], [1.759, -3.771, 0.974, 2.086, 0.713, 1.164]]\nC: [[0.167, 0.689, 0.442, 1.571, 6.663, 0.887], [2.849, 2.011, -0.011, 0.132, 0.183, 0.035], [-0.085, -4.074, 0.615, 1.543, 0.713, 0.958], [1.39, -4.168, 0.506, 1.716, 0.715, 0.966]]\nD: [[0.313, 0.252, 0.284, 1.649, 6.826, 1.244], [2.392, 1.917, -0.34, 0.488, -0.05, 0.218], [0.064, -3.679, 0.658, 2.001, 0.36, 1.007], [1.092, -4.59, 0.839, 1.267, 0.336, 1.034]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_146_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_146_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the table in the scene. The camera pose information includes: the rotation matrix: [[-0.241978, -0.427128, 0.871211], [-0.963615, 0.210861, -0.164264], [-0.113543, -0.879261, -0.462611]]; the translation vector: [2.164319, 10.11033, 1.716674], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.089, 0.93, 0.906, 1.952, 6.847, 0.759], [2.364, 2.183, -0.461, 0.089, -0.064, -0.412], [0.126, -4.445, 0.264, 1.544, 0.593, 0.846], [1.593, -4.523, 0.942, 1.504, 1.198, 0.582]]\nB: [[0.288, 1.03, -0.052, 1.738, 7.022, 0.872], [3.315, 2.231, -0.328, 0.592, 0.336, -0.023], [0.311, -4.176, 1.057, 1.806, 0.812, 1.384], [1.759, -3.771, 0.974, 2.086, 0.713, 1.164]]\nC: [[0.167, 0.689, 0.442, 1.571, 6.663, 0.887], [2.849, 2.011, -0.011, 0.132, 0.183, 0.035], [-0.085, -4.074, 0.615, 1.543, 0.713, 0.958], [1.39, -4.168, 0.506, 1.716, 0.715, 0.966]]\nD: [[0.313, 0.252, 0.284, 1.649, 6.826, 1.244], [2.392, 1.917, -0.34, 0.488, -0.05, 0.218], [0.064, -3.679, 0.658, 2.001, 0.36, 1.007], [1.092, -4.59, 0.839, 1.267, 0.336, 1.034]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.766, -1.392, 1.241, 0.359, 0.114, 0.509]]\nB: [[0.818, -0.933, 0.887, 0.454, 0.574, 0.13]]\nC: [[0.354, -0.503, 0.874, 0.511, 0.736, 0.517]]\nD: [[1.154, -1.214, 1.131, 0.407, 0.409, -0.18]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_147_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_147_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the paper cutter in the scene. The camera pose information includes: the rotation matrix: [[0.624751, -0.31057, 0.716403], [-0.780527, -0.273701, 0.562018], [0.021534, -0.910293, -0.413403]]; the translation vector: [-0.212106, 0.775797, 1.619325], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.766, -1.392, 1.241, 0.359, 0.114, 0.509]]\nB: [[0.818, -0.933, 0.887, 0.454, 0.574, 0.13]]\nC: [[0.354, -0.503, 0.874, 0.511, 0.736, 0.517]]\nD: [[1.154, -1.214, 1.131, 0.407, 0.409, -0.18]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.354, -1.662, 1.012, 0.018, 0.115, 0.103]]\nB: [[-0.792, -1.485, 1.441, 0.393, -0.105, 0.505]]\nC: [[-0.528, -1.745, 1.201, 0.1, 0.492, -0.087]]\nD: [[-0.815, -1.664, 1.36, -0.019, -0.178, -0.353]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_148_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_148_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the light switch in the scene. The camera pose information includes: the rotation matrix: [[-0.677945, 0.409221, -0.610679], [0.735109, 0.38004, -0.561413], [0.00234, -0.829523, -0.558468]]; the translation vector: [3.092599, 2.044437, 1.437429], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.354, -1.662, 1.012, 0.018, 0.115, 0.103]]\nB: [[-0.792, -1.485, 1.441, 0.393, -0.105, 0.505]]\nC: [[-0.528, -1.745, 1.201, 0.1, 0.492, -0.087]]\nD: [[-0.815, -1.664, 1.36, -0.019, -0.178, -0.353]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.747, -2.431, 0.458, 6.984, 5.717, 0.813], [-0.691, 2.68, 0.322, 9.392, 3.093, 0.827]]\nB: [[0.057, -2.429, 0.573, 7.86, 5.95, 0.072], [-0.235, 2.427, 0.641, 8.832, 2.983, 0.523]]\nC: [[0.397, -2.595, 0.294, 7.23, 6.005, 0.625], [-0.735, 2.197, 0.57, 9.4, 2.894, 0.932]]\nD: [[0.26, -2.542, 0.108, 7.4, 6.111, 0.419], [-0.69, 2.286, 0.483, 9.253, 2.675, 0.512]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_149_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_149_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[-0.928108, -0.125197, 0.35063], [-0.371823, 0.3599, -0.855699], [-0.019061, -0.924553, -0.380577]]; the translation vector: [5.296664, 4.137775, 1.856988], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.747, -2.431, 0.458, 6.984, 5.717, 0.813], [-0.691, 2.68, 0.322, 9.392, 3.093, 0.827]]\nB: [[0.057, -2.429, 0.573, 7.86, 5.95, 0.072], [-0.235, 2.427, 0.641, 8.832, 2.983, 0.523]]\nC: [[0.397, -2.595, 0.294, 7.23, 6.005, 0.625], [-0.735, 2.197, 0.57, 9.4, 2.894, 0.932]]\nD: [[0.26, -2.542, 0.108, 7.4, 6.111, 0.419], [-0.69, 2.286, 0.483, 9.253, 2.675, 0.512]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.374, -0.417, 0.072, 5.927, 4.746, 0.404], [1.639, 0.598, 0.366, 1.118, 4.293, 0.368], [2.9, -1.425, 0.632, 1.428, 0.28, 0.254]]\nB: [[0.799, -0.748, 0.531, 6.317, 4.867, 0.446], [1.703, 0.912, 0.069, 1.474, 3.826, 0.769], [3.325, -1.637, 0.524, 1.447, 0.08, -0.103]]\nC: [[0.109, -0.618, 0.303, 6.211, 4.572, 0.089], [2.121, 0.402, 0.274, 1.014, 4.79, 0.298], [2.929, -1.018, 0.279, 1.903, -0.107, 0.311]]\nD: [[-0.053, -0.73, 0.366, 5.716, 4.946, 0.696], [1.94, 0.414, 0.125, 0.997, 4.139, 0.213], [2.533, -1.383, 0.826, 1.711, 0.446, 0.207]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_150_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_150_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[-0.052123, 0.492225, -0.868906], [0.996177, 0.08671, -0.010637], [0.070107, -0.866138, -0.494863]]; the translation vector: [3.27549, 2.071379, 1.287401], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.374, -0.417, 0.072, 5.927, 4.746, 0.404], [1.639, 0.598, 0.366, 1.118, 4.293, 0.368], [2.9, -1.425, 0.632, 1.428, 0.28, 0.254]]\nB: [[0.799, -0.748, 0.531, 6.317, 4.867, 0.446], [1.703, 0.912, 0.069, 1.474, 3.826, 0.769], [3.325, -1.637, 0.524, 1.447, 0.08, -0.103]]\nC: [[0.109, -0.618, 0.303, 6.211, 4.572, 0.089], [2.121, 0.402, 0.274, 1.014, 4.79, 0.298], [2.929, -1.018, 0.279, 1.903, -0.107, 0.311]]\nD: [[-0.053, -0.73, 0.366, 5.716, 4.946, 0.696], [1.94, 0.414, 0.125, 0.997, 4.139, 0.213], [2.533, -1.383, 0.826, 1.711, 0.446, 0.207]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.627, -3.376, 0.855, 6.372, 0.045, 1.93], [-4.559, -0.814, 0.815, 0.23, 4.236, 2.315], [2.457, -1.253, 1.135, 0.502, 4.3, 1.849], [2.763, 1.048, 0.711, 0.538, 0.174, 2.307], [2.637, 1.438, 0.737, -0.26, 0.359, 1.655], [2.838, 1.209, 1.15, -0.136, 0.413, 2.018], [2.547, 3.388, 0.7, 0.133, 3.272, 2.033], [1.842, 5.509, 1.228, 1.639, 0.473, 2.533], [1.462, 4.688, 1.401, -0.073, 1.6, 2.078], [3.735, 2.121, 1.265, 0.57, 0.876, 2.223], [3.212, 2.741, 1.782, 1.502, 0.578, -0.004]]\nB: [[-0.903, -3.755, 0.874, 6.176, 0.003, 1.35], [-4.554, -0.741, 1.026, -0.065, 4.304, 1.597], [2.299, -1.131, 0.865, 0.391, 4.838, 2.495], [2.651, 0.847, 1.18, 0.317, 0.031, 1.522], [1.968, 0.985, 1.174, 0.018, 0.505, 1.538], [2.371, 1.973, 0.767, 0.103, 0.399, 1.514], [2.144, 3.832, 1.306, -0.158, 3.954, 2.582], [1.91, 5.239, 0.926, 1.148, 0.013, 2.526], [1.285, 4.859, 0.706, 0.551, 0.734, 2.356], [3.523, 2.358, 1.085, 0.08, 1.478, 2.236], [3.224, 3.037, 2.38, 1.03, -0.257, 0.188]]\nC: [[-1.266, -3.485, 0.564, 6.835, 0.324, 1.764], [-3.629, -0.962, 1.016, -0.055, 4.598, 2.313], [2.437, -1.124, 1.463, -0.109, 4.49, 2.527], [2.113, 1.183, 0.667, 0.701, -0.094, 1.699], [2.045, 1.707, 0.812, -0.196, 0.311, 1.762], [2.268, 1.659, 0.591, -0.094, 0.192, 1.734], [2.994, 3.302, 1.563, 0.269, 3.696, 2.084], [1.688, 4.818, 0.728, 1.225, 0.665, 2.09], [0.999, 4.388, 1.202, -0.003, 0.99, 2.125], [3.205, 2.357, 1.438, 0.088, 1.176, 2.548], [3.215, 2.712, 1.754, 0.977, -0.046, 0.843]]\nD: [[-0.786, -3.408, 0.812, 6.62, 0.23, 1.627], [-4.064, -0.926, 0.97, 0.26, 4.308, 1.916], [2.527, -1.13, 1.126, 0.243, 4.695, 2.21], [2.347, 1.178, 0.964, 0.317, 0.117, 1.88], [2.227, 1.442, 1.014, 0.145, 0.618, 2.004], [2.348, 1.638, 0.879, 0.351, 0.101, 1.748], [2.53, 3.466, 1.165, 0.288, 3.589, 2.389], [1.982, 5.141, 1.204, 1.32, 0.458, 2.309], [1.319, 4.76, 1.151, 0.233, 1.14, 2.044], [3.664, 2.514, 1.17, 0.338, 1.259, 2.387], [3.264, 3.152, 2.173, 1.061, 0.098, 0.435]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_151_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_151_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.688084, 0.423256, -0.589401], [0.725514, -0.415863, 0.54835], [-0.013017, -0.80493, -0.593227]]; the translation vector: [3.968163, 0.8771, 1.421607], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.627, -3.376, 0.855, 6.372, 0.045, 1.93], [-4.559, -0.814, 0.815, 0.23, 4.236, 2.315], [2.457, -1.253, 1.135, 0.502, 4.3, 1.849], [2.763, 1.048, 0.711, 0.538, 0.174, 2.307], [2.637, 1.438, 0.737, -0.26, 0.359, 1.655], [2.838, 1.209, 1.15, -0.136, 0.413, 2.018], [2.547, 3.388, 0.7, 0.133, 3.272, 2.033], [1.842, 5.509, 1.228, 1.639, 0.473, 2.533], [1.462, 4.688, 1.401, -0.073, 1.6, 2.078], [3.735, 2.121, 1.265, 0.57, 0.876, 2.223], [3.212, 2.741, 1.782, 1.502, 0.578, -0.004]]\nB: [[-0.903, -3.755, 0.874, 6.176, 0.003, 1.35], [-4.554, -0.741, 1.026, -0.065, 4.304, 1.597], [2.299, -1.131, 0.865, 0.391, 4.838, 2.495], [2.651, 0.847, 1.18, 0.317, 0.031, 1.522], [1.968, 0.985, 1.174, 0.018, 0.505, 1.538], [2.371, 1.973, 0.767, 0.103, 0.399, 1.514], [2.144, 3.832, 1.306, -0.158, 3.954, 2.582], [1.91, 5.239, 0.926, 1.148, 0.013, 2.526], [1.285, 4.859, 0.706, 0.551, 0.734, 2.356], [3.523, 2.358, 1.085, 0.08, 1.478, 2.236], [3.224, 3.037, 2.38, 1.03, -0.257, 0.188]]\nC: [[-1.266, -3.485, 0.564, 6.835, 0.324, 1.764], [-3.629, -0.962, 1.016, -0.055, 4.598, 2.313], [2.437, -1.124, 1.463, -0.109, 4.49, 2.527], [2.113, 1.183, 0.667, 0.701, -0.094, 1.699], [2.045, 1.707, 0.812, -0.196, 0.311, 1.762], [2.268, 1.659, 0.591, -0.094, 0.192, 1.734], [2.994, 3.302, 1.563, 0.269, 3.696, 2.084], [1.688, 4.818, 0.728, 1.225, 0.665, 2.09], [0.999, 4.388, 1.202, -0.003, 0.99, 2.125], [3.205, 2.357, 1.438, 0.088, 1.176, 2.548], [3.215, 2.712, 1.754, 0.977, -0.046, 0.843]]\nD: [[-0.786, -3.408, 0.812, 6.62, 0.23, 1.627], [-4.064, -0.926, 0.97, 0.26, 4.308, 1.916], [2.527, -1.13, 1.126, 0.243, 4.695, 2.21], [2.347, 1.178, 0.964, 0.317, 0.117, 1.88], [2.227, 1.442, 1.014, 0.145, 0.618, 2.004], [2.348, 1.638, 0.879, 0.351, 0.101, 1.748], [2.53, 3.466, 1.165, 0.288, 3.589, 2.389], [1.982, 5.141, 1.204, 1.32, 0.458, 2.309], [1.319, 4.76, 1.151, 0.233, 1.14, 2.044], [3.664, 2.514, 1.17, 0.338, 1.259, 2.387], [3.264, 3.152, 2.173, 1.061, 0.098, 0.435]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.24, 0.127, 1.561, 0.17, 6.452, 1.322]]\nB: [[1.808, 0.445, 1.097, -0.163, 6.206, 1.162]]\nC: [[2.076, -0.046, 1.133, 0.371, 6.595, 0.908]]\nD: [[2.152, 0.397, 1.838, 0.141, 6.038, 1.758]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_152_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_152_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the whiteboard in the scene. The camera pose information includes: the rotation matrix: [[-0.176261, -0.039155, 0.983564], [-0.983722, -0.028492, -0.177423], [0.03497, -0.998827, -0.033496]]; the translation vector: [3.054739, 2.437738, 1.503838], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.24, 0.127, 1.561, 0.17, 6.452, 1.322]]\nB: [[1.808, 0.445, 1.097, -0.163, 6.206, 1.162]]\nC: [[2.076, -0.046, 1.133, 0.371, 6.595, 0.908]]\nD: [[2.152, 0.397, 1.838, 0.141, 6.038, 1.758]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.377, 1.75, 0.313, 0.637, 1.084, 1.086], [-0.879, 2.594, 0.422, 0.742, 0.234, 0.85]]\nB: [[-0.13, 2.575, 0.337, 1.18, 0.166, 1.328], [-0.585, 2.455, 0.693, 0.879, 0.311, 1.309]]\nC: [[-0.219, 1.756, 0.406, 1.056, 0.511, 0.967], [-0.363, 1.945, 0.501, 1.166, 0.939, 0.804]]\nD: [[-0.109, 2.202, 0.796, 0.742, 0.65, 0.918], [-0.778, 2.232, 0.83, 0.777, 0.638, 0.953]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_153_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_153_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the sofa chair in the scene. The camera pose information includes: the rotation matrix: [[0.753053, 0.123809, -0.646206], [0.619922, -0.462608, 0.633791], [-0.220471, -0.877875, -0.42512]]; the translation vector: [4.259223, 3.769218, 1.505729], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.377, 1.75, 0.313, 0.637, 1.084, 1.086], [-0.879, 2.594, 0.422, 0.742, 0.234, 0.85]]\nB: [[-0.13, 2.575, 0.337, 1.18, 0.166, 1.328], [-0.585, 2.455, 0.693, 0.879, 0.311, 1.309]]\nC: [[-0.219, 1.756, 0.406, 1.056, 0.511, 0.967], [-0.363, 1.945, 0.501, 1.166, 0.939, 0.804]]\nD: [[-0.109, 2.202, 0.796, 0.742, 0.65, 0.918], [-0.778, 2.232, 0.83, 0.777, 0.638, 0.953]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.251, 0.156, -0.128, -0.051, 0.628, 0.239], [0.767, -0.47, 0.609, 0.494, 0.504, 0.166], [-0.094, 1.499, 1.301, 0.409, 0.637, -0.038], [-0.218, 1.599, 2.043, 0.222, -0.051, -0.084], [0.774, 1.201, 1.813, 0.251, -0.38, 0.063]]\nB: [[-0.408, 0.627, 0.343, 0.188, 0.542, 0.081], [0.501, -0.329, 0.635, 0.154, 0.177, 0.084], [0.343, 1.237, 1.788, 0.265, 0.262, 0.091], [0.216, 1.167, 1.709, 0.275, 0.094, 0.094], [0.467, 1.14, 1.723, 0.259, 0.069, 0.108]]\nC: [[-0.807, 0.141, 0.815, 0.53, 0.607, 0.51], [0.917, -0.099, 0.427, -0.321, -0.28, 0.334], [0.531, 1.399, 2.196, 0.43, 0.03, 0.076], [0.47, 0.735, 1.914, -0.093, 0.374, 0.55], [-0.032, 1.587, 2.029, 0.611, -0.009, -0.144]]\nD: [[-0.225, 0.433, 0.214, 0.523, 1.033, -0.125], [0.497, -0.466, 0.903, 0.572, 0.328, -0.033], [0.249, 0.868, 1.316, 0.58, 0.558, -0.337], [0.688, 0.673, 1.442, -0.064, -0.139, -0.391], [0.045, 1.256, 1.359, -0.021, 0.452, 0.403]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_154_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_154_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the towel in the scene. The camera pose information includes: the rotation matrix: [[0.956223, -0.170898, 0.237554], [-0.292595, -0.544035, 0.786393], [-0.005155, -0.821474, -0.570223]]; the translation vector: [1.275326, 2.834272, 1.3185], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.251, 0.156, -0.128, -0.051, 0.628, 0.239], [0.767, -0.47, 0.609, 0.494, 0.504, 0.166], [-0.094, 1.499, 1.301, 0.409, 0.637, -0.038], [-0.218, 1.599, 2.043, 0.222, -0.051, -0.084], [0.774, 1.201, 1.813, 0.251, -0.38, 0.063]]\nB: [[-0.408, 0.627, 0.343, 0.188, 0.542, 0.081], [0.501, -0.329, 0.635, 0.154, 0.177, 0.084], [0.343, 1.237, 1.788, 0.265, 0.262, 0.091], [0.216, 1.167, 1.709, 0.275, 0.094, 0.094], [0.467, 1.14, 1.723, 0.259, 0.069, 0.108]]\nC: [[-0.807, 0.141, 0.815, 0.53, 0.607, 0.51], [0.917, -0.099, 0.427, -0.321, -0.28, 0.334], [0.531, 1.399, 2.196, 0.43, 0.03, 0.076], [0.47, 0.735, 1.914, -0.093, 0.374, 0.55], [-0.032, 1.587, 2.029, 0.611, -0.009, -0.144]]\nD: [[-0.225, 0.433, 0.214, 0.523, 1.033, -0.125], [0.497, -0.466, 0.903, 0.572, 0.328, -0.033], [0.249, 0.868, 1.316, 0.58, 0.558, -0.337], [0.688, 0.673, 1.442, -0.064, -0.139, -0.391], [0.045, 1.256, 1.359, -0.021, 0.452, 0.403]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.306, -0.014, 1.979, 4.019, 7.414, 0.307]]\nB: [[0.103, 0.292, 1.906, 4.176, 7.558, 0.724]]\nC: [[0.489, 0.437, 1.928, 3.337, 7.327, 0.317]]\nD: [[0.278, 0.096, 1.983, 3.8, 7.07, 0.334]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_155_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_155_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the ceiling in the scene. The camera pose information includes: the rotation matrix: [[-0.443363, -0.325026, 0.835337], [-0.895367, 0.117125, -0.429651], [0.041809, -0.938424, -0.342946]]; the translation vector: [2.190343, 3.392878, 1.594635], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.306, -0.014, 1.979, 4.019, 7.414, 0.307]]\nB: [[0.103, 0.292, 1.906, 4.176, 7.558, 0.724]]\nC: [[0.489, 0.437, 1.928, 3.337, 7.327, 0.317]]\nD: [[0.278, 0.096, 1.983, 3.8, 7.07, 0.334]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.28, 0.304, -0.341, 2.294, 3.044, -0.02], [-1.331, 0.139, -0.157, 1.326, 1.929, 0.397]]\nB: [[0.062, -0.149, 0.038, 2.484, 2.88, 0.127], [-1.587, -0.328, 0.005, 1.461, 1.909, 0.087]]\nC: [[0.029, -0.482, -0.368, 2.94, 2.904, 0.128], [-1.213, -0.173, 0.109, 1.128, 1.645, 0.334]]\nD: [[0.293, -0.453, -0.316, 2.555, 2.944, -0.31], [-1.538, -0.329, -0.099, 1.121, 2.246, -0.19]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_156_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_156_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the floor in the scene. The camera pose information includes: the rotation matrix: [[0.59597, 0.482312, -0.642025], [0.802979, -0.35126, 0.4815], [0.006716, -0.802491, -0.596626]]; the translation vector: [3.449961, 1.112515, 1.412234], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.28, 0.304, -0.341, 2.294, 3.044, -0.02], [-1.331, 0.139, -0.157, 1.326, 1.929, 0.397]]\nB: [[0.062, -0.149, 0.038, 2.484, 2.88, 0.127], [-1.587, -0.328, 0.005, 1.461, 1.909, 0.087]]\nC: [[0.029, -0.482, -0.368, 2.94, 2.904, 0.128], [-1.213, -0.173, 0.109, 1.128, 1.645, 0.334]]\nD: [[0.293, -0.453, -0.316, 2.555, 2.944, -0.31], [-1.538, -0.329, -0.099, 1.121, 2.246, -0.19]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.985, 1.36, 0.323, 0.802, 0.423, -0.124], [2.422, 0.684, 0.754, 0.948, 0.198, 0.546], [-0.687, -2.501, -0.196, 0.73, 0.321, 0.703]]\nB: [[1.365, 1.983, 0.034, 0.504, 0.741, 0.248], [1.92, 1.004, 0.632, 0.401, 0.542, 0.139], [-0.839, -2.927, -0.163, 0.351, 0.752, 0.304]]\nC: [[1.454, 1.792, 0.377, 0.63, 0.637, 0.263], [2.367, 0.546, 0.29, 0.458, 0.434, 0.427], [-1.072, -2.953, 0.222, 0.398, 0.377, 0.406]]\nD: [[1.15, 1.795, -0.026, 0.476, 0.371, 0.563], [2.092, 0.112, 0.084, 0.025, 0.591, 0.3], [-1.071, -3.278, 0.072, 0.031, 0.342, 0.689]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_157_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_157_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the seat in the scene. The camera pose information includes: the rotation matrix: [[0.000188, -0.47362, 0.88073], [-0.997828, 0.057931, 0.031365], [-0.065877, -0.878822, -0.47258]]; the translation vector: [4.366519, 5.511691, 1.307889], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.985, 1.36, 0.323, 0.802, 0.423, -0.124], [2.422, 0.684, 0.754, 0.948, 0.198, 0.546], [-0.687, -2.501, -0.196, 0.73, 0.321, 0.703]]\nB: [[1.365, 1.983, 0.034, 0.504, 0.741, 0.248], [1.92, 1.004, 0.632, 0.401, 0.542, 0.139], [-0.839, -2.927, -0.163, 0.351, 0.752, 0.304]]\nC: [[1.454, 1.792, 0.377, 0.63, 0.637, 0.263], [2.367, 0.546, 0.29, 0.458, 0.434, 0.427], [-1.072, -2.953, 0.222, 0.398, 0.377, 0.406]]\nD: [[1.15, 1.795, -0.026, 0.476, 0.371, 0.563], [2.092, 0.112, 0.084, 0.025, 0.591, 0.3], [-1.071, -3.278, 0.072, 0.031, 0.342, 0.689]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.531, 1.811, 0.438, 0.96, -0.248, 1.075], [-1.41, -1.002, 0.77, 0.47, 1.246, 1.71]]\nB: [[-0.951, 1.545, 0.971, 1.428, 0.629, 1.104], [-2.05, -1.043, 0.695, 0.095, 1.11, 1.626]]\nC: [[-1.029, 1.273, 0.377, 1.0, 0.707, 1.651], [-1.265, -0.353, 1.355, -0.02, 0.917, 2.297]]\nD: [[-1.312, 1.674, 0.691, 1.103, 0.228, 1.421], [-1.753, -0.603, 0.956, 0.349, 1.091, 2.04]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_158_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_158_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[0.927869, -0.125596, 0.351119], [-0.372891, -0.32108, 0.870551], [0.003399, -0.938687, -0.344754]]; the translation vector: [5.442723, 4.031985, 1.348893], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.531, 1.811, 0.438, 0.96, -0.248, 1.075], [-1.41, -1.002, 0.77, 0.47, 1.246, 1.71]]\nB: [[-0.951, 1.545, 0.971, 1.428, 0.629, 1.104], [-2.05, -1.043, 0.695, 0.095, 1.11, 1.626]]\nC: [[-1.029, 1.273, 0.377, 1.0, 0.707, 1.651], [-1.265, -0.353, 1.355, -0.02, 0.917, 2.297]]\nD: [[-1.312, 1.674, 0.691, 1.103, 0.228, 1.421], [-1.753, -0.603, 0.956, 0.349, 1.091, 2.04]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.1, -0.155, 0.488, 0.884, 0.762, 1.139]]\nB: [[-1.609, -0.239, 0.938, 0.096, 1.426, 1.078]]\nC: [[-1.861, -0.273, 0.81, 0.598, 0.82, 0.783]]\nD: [[-1.644, -0.605, 0.583, 0.402, 1.231, 1.185]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_159_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_159_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the shelf in the scene. The camera pose information includes: the rotation matrix: [[-0.070416, -0.411804, 0.908548], [-0.99671, 0.065705, -0.047468], [-0.040148, -0.908901, -0.415075]]; the translation vector: [2.214543, 1.806687, 1.391502], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.1, -0.155, 0.488, 0.884, 0.762, 1.139]]\nB: [[-1.609, -0.239, 0.938, 0.096, 1.426, 1.078]]\nC: [[-1.861, -0.273, 0.81, 0.598, 0.82, 0.783]]\nD: [[-1.644, -0.605, 0.583, 0.402, 1.231, 1.185]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.605, -0.075, 2.599, 6.78, 7.188, 0.753]]\nB: [[-0.136, -0.074, 2.664, 7.091, 7.331, 0.624]]\nC: [[-0.11, -0.067, 2.645, 6.713, 7.047, 0.627]]\nD: [[-0.59, -0.552, 3.048, 6.673, 7.52, 0.884]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_160_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_160_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the ceiling in the scene. The camera pose information includes: the rotation matrix: [[-0.955421, 0.119616, -0.269932], [0.295248, 0.388339, -0.872939], [0.000408, -0.91372, -0.406343]]; the translation vector: [2.65583, 2.981598, 1.368648], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.605, -0.075, 2.599, 6.78, 7.188, 0.753]]\nB: [[-0.136, -0.074, 2.664, 7.091, 7.331, 0.624]]\nC: [[-0.11, -0.067, 2.645, 6.713, 7.047, 0.627]]\nD: [[-0.59, -0.552, 3.048, 6.673, 7.52, 0.884]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.773, 0.999, 2.176, 3.762, 2.16, 0.713]]\nB: [[1.402, 0.542, 2.42, 3.544, 2.145, 0.268]]\nC: [[0.962, 0.894, 1.956, 3.984, 2.23, 0.213]]\nD: [[1.508, 0.544, 2.14, 3.898, 2.009, 0.701]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_161_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_161_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the ceiling in the scene. The camera pose information includes: the rotation matrix: [[-0.454685, 0.144673, -0.878824], [0.890085, 0.109034, -0.442562], [0.031795, -0.983454, -0.178347]]; the translation vector: [3.311996, 2.119304, 1.59409], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.773, 0.999, 2.176, 3.762, 2.16, 0.713]]\nB: [[1.402, 0.542, 2.42, 3.544, 2.145, 0.268]]\nC: [[0.962, 0.894, 1.956, 3.984, 2.23, 0.213]]\nD: [[1.508, 0.544, 2.14, 3.898, 2.009, 0.701]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.445, -1.591, 0.401, 1.208, 0.362, 0.709], [0.85, 1.846, 0.287, 0.495, 1.135, -0.015]]\nB: [[1.318, -1.383, 0.256, 0.782, 0.724, 0.542], [1.339, 2.155, 0.239, 0.765, 0.899, 0.445]]\nC: [[1.731, -1.727, 0.665, 0.715, 0.694, 0.718], [0.941, 2.531, -0.018, 1.235, 0.51, 0.14]]\nD: [[1.454, -1.414, -0.024, 0.443, 0.46, 0.088], [0.989, 2.555, 0.477, 1.003, 1.282, 0.286]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_162_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_162_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the coffee table in the scene. The camera pose information includes: the rotation matrix: [[0.990268, -0.101591, 0.095124], [-0.135934, -0.559426, 0.817658], [-0.029851, -0.822631, -0.567792]]; the translation vector: [6.679901, 2.488796, 1.402653], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.445, -1.591, 0.401, 1.208, 0.362, 0.709], [0.85, 1.846, 0.287, 0.495, 1.135, -0.015]]\nB: [[1.318, -1.383, 0.256, 0.782, 0.724, 0.542], [1.339, 2.155, 0.239, 0.765, 0.899, 0.445]]\nC: [[1.731, -1.727, 0.665, 0.715, 0.694, 0.718], [0.941, 2.531, -0.018, 1.235, 0.51, 0.14]]\nD: [[1.454, -1.414, -0.024, 0.443, 0.46, 0.088], [0.989, 2.555, 0.477, 1.003, 1.282, 0.286]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.849, -1.336, 0.075, 0.117, 0.119, 0.051]]\nB: [[1.779, -1.476, 0.442, 0.485, 0.615, 0.395]]\nC: [[1.904, -1.448, 0.152, 0.474, 0.108, -0.174]]\nD: [[1.427, -1.203, 0.391, 0.059, -0.193, -0.208]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_163_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_163_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the object in the scene. The camera pose information includes: the rotation matrix: [[0.246516, -0.470365, 0.847341], [-0.959136, 0.006886, 0.282862], [-0.138884, -0.882445, -0.449446]]; the translation vector: [3.043058, 2.955299, 1.551102], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.849, -1.336, 0.075, 0.117, 0.119, 0.051]]\nB: [[1.779, -1.476, 0.442, 0.485, 0.615, 0.395]]\nC: [[1.904, -1.448, 0.152, 0.474, 0.108, -0.174]]\nD: [[1.427, -1.203, 0.391, 0.059, -0.193, -0.208]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.487, 1.645, 0.382, 0.302, 0.482, 0.482], [0.998, 1.485, 0.258, 0.64, 0.279, 0.771], [0.516, 1.799, 0.05, 0.639, 0.719, 0.616], [0.027, 2.307, 0.334, 0.459, 0.957, 1.087], [-0.766, 2.114, 0.879, 0.637, 0.926, 1.186], [-1.637, 1.716, 0.195, 0.889, 0.178, 0.887], [-1.878, 1.498, 0.042, 1.014, 0.211, 0.465], [-2.168, 2.812, 0.748, 0.089, 0.133, 0.111], [-2.511, -0.128, 0.278, 0.262, 0.627, 1.122], [-3.252, -1.403, 0.351, 0.543, 0.875, 0.964], [-2.67, -1.518, 0.047, 0.142, 0.368, 0.87], [-0.679, -1.008, 0.86, 0.74, 0.76, 1.005], [-0.054, -1.776, 0.013, 0.602, 0.383, 0.483], [0.442, -1.702, 0.129, 0.294, 0.491, 1.264], [-0.906, -1.527, 0.588, 0.773, 1.129, 1.323], [1.797, -0.711, 0.306, 0.186, 0.995, 1.019], [1.694, -1.236, 0.46, 0.778, 1.151, 1.284], [1.24, -1.901, 0.667, 0.364, 1.023, 0.918], [2.398, -1.858, 0.981, 0.284, 0.726, 0.83], [3.224, -1.673, 0.285, 0.245, 0.491, 0.975], [1.768, 2.317, 0.9, 0.325, 1.027, 1.062], [1.496, 2.661, 0.93, -0.007, 0.819, 0.169]]\nB: [[1.146, 0.948, 0.122, 0.95, 1.005, 0.549], [0.748, 0.973, 0.155, 0.6, 0.484, 0.381], [0.549, 2.17, 0.091, 0.666, 0.717, 0.866], [0.554, 2.811, 0.815, 0.733, 0.125, 0.7], [-0.925, 2.187, 0.558, 0.622, 0.213, 0.874], [-1.744, 1.277, 0.001, 0.646, 0.973, 0.704], [-1.809, 2.353, 0.808, 0.048, 0.797, 0.7], [-1.256, 2.641, 1.036, 0.522, 0.609, 0.158], [-1.879, -0.087, 0.596, 0.81, 0.571, 0.463], [-3.345, -0.961, 0.298, 0.354, 0.59, 1.207], [-2.802, -1.895, 0.135, 0.976, 1.183, 0.764], [-0.847, -1.618, 0.508, 0.783, 0.348, 1.292], [-0.511, -1.056, 0.376, 0.73, 0.392, 1.159], [-0.024, -2.057, 0.759, 0.532, 0.455, 0.817], [-0.251, -2.214, 0.173, 1.127, 0.862, 0.708], [2.174, -0.228, 0.822, 0.364, 0.554, 0.827], [1.928, -1.877, 0.198, 0.653, 1.131, 1.053], [1.218, -2.319, 0.663, 0.163, 0.153, 0.793], [2.951, -1.156, 0.405, 1.011, 0.624, 0.772], [3.153, -1.986, 0.421, 0.263, 0.33, 0.7], [1.367, 2.28, 0.547, 1.058, 0.935, 1.287], [2.006, 2.966, 0.782, 0.332, 0.619, 0.04]]\nC: [[1.346, 1.054, 0.767, 0.951, 0.758, 0.769], [0.659, 1.706, 0.684, 0.913, 0.914, 1.319], [0.805, 2.288, 0.288, 0.155, 0.839, 0.635], [0.287, 2.236, 0.545, 0.587, 0.976, 0.783], [-1.118, 2.319, 0.772, 1.192, 0.851, 0.415], [-1.552, 1.463, 0.231, 0.636, 0.79, 0.457], [-1.992, 2.15, 0.851, 0.919, 1.11, 0.624], [-1.923, 2.253, 1.26, 0.407, 0.257, 0.58], [-2.064, -0.023, 0.196, 0.32, 0.999, 0.859], [-3.224, -1.369, 0.324, 1.046, 0.849, 0.941], [-2.953, -2.153, 0.953, 0.315, 0.426, 0.39], [-0.29, -1.189, 0.464, 0.368, 1.039, 1.28], [-0.098, -2.0, 0.391, 0.817, 0.212, 1.036], [0.365, -1.822, 0.164, 0.214, 0.365, 0.378], [-0.847, -2.191, 0.875, 0.968, 0.479, 0.553], [2.332, -0.438, 0.431, 0.138, 0.956, 1.041], [1.345, -2.004, 0.538, 0.439, 0.287, 0.73], [1.393, -1.64, 0.88, 0.322, 0.297, 1.16], [3.052, -1.259, 0.761, 0.943, 0.828, 0.5], [2.735, -2.476, 0.875, 0.335, 1.087, 0.495], [1.336, 2.088, 0.504, 0.673, 1.053, 0.469], [1.386, 2.116, 0.605, 0.191, 0.61, 0.101]]\nD: [[1.518, 1.271, 0.394, 0.605, 0.594, 0.849], [0.943, 1.353, 0.378, 0.619, 0.666, 0.828], [0.701, 1.955, 0.404, 0.648, 0.696, 0.84], [0.523, 2.479, 0.454, 0.541, 0.563, 0.792], [-1.051, 2.117, 0.448, 0.79, 0.709, 0.804], [-1.341, 1.248, 0.462, 0.57, 0.622, 0.853], [-1.574, 1.994, 0.519, 0.538, 0.675, 0.779], [-1.737, 2.403, 0.858, 0.16, 0.317, 0.168], [-2.078, -0.466, 0.495, 0.568, 0.586, 0.801], [-2.925, -1.082, 0.538, 0.66, 0.66, 0.803], [-3.037, -1.752, 0.519, 0.574, 0.705, 0.845], [-0.539, -1.191, 0.375, 0.64, 0.637, 0.843], [-0.068, -1.536, 0.384, 0.646, 0.641, 0.825], [-0.052, -2.09, 0.408, 0.661, 0.773, 0.824], [-0.669, -1.919, 0.395, 0.676, 0.647, 0.832], [2.151, -0.689, 0.438, 0.636, 0.62, 0.802], [1.695, -1.528, 0.421, 0.589, 0.733, 0.82], [1.703, -2.028, 0.457, 0.561, 0.65, 0.798], [2.65, -1.483, 0.534, 0.701, 0.712, 0.852], [2.844, -2.087, 0.588, 0.548, 0.714, 0.804], [1.775, 1.985, 0.459, 0.664, 0.67, 0.811], [1.768, 2.603, 0.602, 0.329, 0.514, 0.537]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_164_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_164_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[0.424269, -0.366439, 0.828081], [-0.894198, -0.025281, 0.446957], [-0.142848, -0.930098, -0.338395]]; the translation vector: [2.638367, 6.760901, 1.41712], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.487, 1.645, 0.382, 0.302, 0.482, 0.482], [0.998, 1.485, 0.258, 0.64, 0.279, 0.771], [0.516, 1.799, 0.05, 0.639, 0.719, 0.616], [0.027, 2.307, 0.334, 0.459, 0.957, 1.087], [-0.766, 2.114, 0.879, 0.637, 0.926, 1.186], [-1.637, 1.716, 0.195, 0.889, 0.178, 0.887], [-1.878, 1.498, 0.042, 1.014, 0.211, 0.465], [-2.168, 2.812, 0.748, 0.089, 0.133, 0.111], [-2.511, -0.128, 0.278, 0.262, 0.627, 1.122], [-3.252, -1.403, 0.351, 0.543, 0.875, 0.964], [-2.67, -1.518, 0.047, 0.142, 0.368, 0.87], [-0.679, -1.008, 0.86, 0.74, 0.76, 1.005], [-0.054, -1.776, 0.013, 0.602, 0.383, 0.483], [0.442, -1.702, 0.129, 0.294, 0.491, 1.264], [-0.906, -1.527, 0.588, 0.773, 1.129, 1.323], [1.797, -0.711, 0.306, 0.186, 0.995, 1.019], [1.694, -1.236, 0.46, 0.778, 1.151, 1.284], [1.24, -1.901, 0.667, 0.364, 1.023, 0.918], [2.398, -1.858, 0.981, 0.284, 0.726, 0.83], [3.224, -1.673, 0.285, 0.245, 0.491, 0.975], [1.768, 2.317, 0.9, 0.325, 1.027, 1.062], [1.496, 2.661, 0.93, -0.007, 0.819, 0.169]]\nB: [[1.146, 0.948, 0.122, 0.95, 1.005, 0.549], [0.748, 0.973, 0.155, 0.6, 0.484, 0.381], [0.549, 2.17, 0.091, 0.666, 0.717, 0.866], [0.554, 2.811, 0.815, 0.733, 0.125, 0.7], [-0.925, 2.187, 0.558, 0.622, 0.213, 0.874], [-1.744, 1.277, 0.001, 0.646, 0.973, 0.704], [-1.809, 2.353, 0.808, 0.048, 0.797, 0.7], [-1.256, 2.641, 1.036, 0.522, 0.609, 0.158], [-1.879, -0.087, 0.596, 0.81, 0.571, 0.463], [-3.345, -0.961, 0.298, 0.354, 0.59, 1.207], [-2.802, -1.895, 0.135, 0.976, 1.183, 0.764], [-0.847, -1.618, 0.508, 0.783, 0.348, 1.292], [-0.511, -1.056, 0.376, 0.73, 0.392, 1.159], [-0.024, -2.057, 0.759, 0.532, 0.455, 0.817], [-0.251, -2.214, 0.173, 1.127, 0.862, 0.708], [2.174, -0.228, 0.822, 0.364, 0.554, 0.827], [1.928, -1.877, 0.198, 0.653, 1.131, 1.053], [1.218, -2.319, 0.663, 0.163, 0.153, 0.793], [2.951, -1.156, 0.405, 1.011, 0.624, 0.772], [3.153, -1.986, 0.421, 0.263, 0.33, 0.7], [1.367, 2.28, 0.547, 1.058, 0.935, 1.287], [2.006, 2.966, 0.782, 0.332, 0.619, 0.04]]\nC: [[1.346, 1.054, 0.767, 0.951, 0.758, 0.769], [0.659, 1.706, 0.684, 0.913, 0.914, 1.319], [0.805, 2.288, 0.288, 0.155, 0.839, 0.635], [0.287, 2.236, 0.545, 0.587, 0.976, 0.783], [-1.118, 2.319, 0.772, 1.192, 0.851, 0.415], [-1.552, 1.463, 0.231, 0.636, 0.79, 0.457], [-1.992, 2.15, 0.851, 0.919, 1.11, 0.624], [-1.923, 2.253, 1.26, 0.407, 0.257, 0.58], [-2.064, -0.023, 0.196, 0.32, 0.999, 0.859], [-3.224, -1.369, 0.324, 1.046, 0.849, 0.941], [-2.953, -2.153, 0.953, 0.315, 0.426, 0.39], [-0.29, -1.189, 0.464, 0.368, 1.039, 1.28], [-0.098, -2.0, 0.391, 0.817, 0.212, 1.036], [0.365, -1.822, 0.164, 0.214, 0.365, 0.378], [-0.847, -2.191, 0.875, 0.968, 0.479, 0.553], [2.332, -0.438, 0.431, 0.138, 0.956, 1.041], [1.345, -2.004, 0.538, 0.439, 0.287, 0.73], [1.393, -1.64, 0.88, 0.322, 0.297, 1.16], [3.052, -1.259, 0.761, 0.943, 0.828, 0.5], [2.735, -2.476, 0.875, 0.335, 1.087, 0.495], [1.336, 2.088, 0.504, 0.673, 1.053, 0.469], [1.386, 2.116, 0.605, 0.191, 0.61, 0.101]]\nD: [[1.518, 1.271, 0.394, 0.605, 0.594, 0.849], [0.943, 1.353, 0.378, 0.619, 0.666, 0.828], [0.701, 1.955, 0.404, 0.648, 0.696, 0.84], [0.523, 2.479, 0.454, 0.541, 0.563, 0.792], [-1.051, 2.117, 0.448, 0.79, 0.709, 0.804], [-1.341, 1.248, 0.462, 0.57, 0.622, 0.853], [-1.574, 1.994, 0.519, 0.538, 0.675, 0.779], [-1.737, 2.403, 0.858, 0.16, 0.317, 0.168], [-2.078, -0.466, 0.495, 0.568, 0.586, 0.801], [-2.925, -1.082, 0.538, 0.66, 0.66, 0.803], [-3.037, -1.752, 0.519, 0.574, 0.705, 0.845], [-0.539, -1.191, 0.375, 0.64, 0.637, 0.843], [-0.068, -1.536, 0.384, 0.646, 0.641, 0.825], [-0.052, -2.09, 0.408, 0.661, 0.773, 0.824], [-0.669, -1.919, 0.395, 0.676, 0.647, 0.832], [2.151, -0.689, 0.438, 0.636, 0.62, 0.802], [1.695, -1.528, 0.421, 0.589, 0.733, 0.82], [1.703, -2.028, 0.457, 0.561, 0.65, 0.798], [2.65, -1.483, 0.534, 0.701, 0.712, 0.852], [2.844, -2.087, 0.588, 0.548, 0.714, 0.804], [1.775, 1.985, 0.459, 0.664, 0.67, 0.811], [1.768, 2.603, 0.602, 0.329, 0.514, 0.537]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.682, 0.363, 0.619, 1.02, 0.762, 0.226], [0.295, -1.101, 1.206, 0.33, 0.677, 0.052], [-2.902, 0.765, -0.248, 0.755, 0.487, 0.427]]\nB: [[2.599, 0.763, 0.355, 0.291, 0.253, 0.563], [0.364, -0.942, 0.366, 0.823, 0.285, 0.293], [-3.251, -0.039, 0.534, 0.204, 0.315, 0.125]]\nC: [[2.754, 0.716, 0.728, 0.739, 0.536, 0.095], [-0.308, -0.319, 1.147, 0.102, 0.805, 0.177], [-3.102, -0.022, 0.312, 0.658, 0.474, 0.358]]\nD: [[2.461, 0.569, 0.328, 0.546, 0.491, 0.37], [-0.048, -0.818, 0.757, 0.462, 0.439, 0.351], [-2.848, 0.285, 0.131, 0.472, 0.5, 0.37]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_165_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_165_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the box in the scene. The camera pose information includes: the rotation matrix: [[0.764638, 0.028658, -0.643823], [0.64431, -0.055554, 0.762744], [-0.013909, -0.998044, -0.060944]]; the translation vector: [3.061982, 3.98913, 1.495508], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.682, 0.363, 0.619, 1.02, 0.762, 0.226], [0.295, -1.101, 1.206, 0.33, 0.677, 0.052], [-2.902, 0.765, -0.248, 0.755, 0.487, 0.427]]\nB: [[2.599, 0.763, 0.355, 0.291, 0.253, 0.563], [0.364, -0.942, 0.366, 0.823, 0.285, 0.293], [-3.251, -0.039, 0.534, 0.204, 0.315, 0.125]]\nC: [[2.754, 0.716, 0.728, 0.739, 0.536, 0.095], [-0.308, -0.319, 1.147, 0.102, 0.805, 0.177], [-3.102, -0.022, 0.312, 0.658, 0.474, 0.358]]\nD: [[2.461, 0.569, 0.328, 0.546, 0.491, 0.37], [-0.048, -0.818, 0.757, 0.462, 0.439, 0.351], [-2.848, 0.285, 0.131, 0.472, 0.5, 0.37]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.116, -0.769, 0.665, 0.588, 1.197, 1.025], [-0.796, -1.178, 0.171, 0.576, 0.604, 0.431], [-0.822, 1.221, 0.066, 0.696, 1.356, 0.542], [0.425, 0.671, 0.344, 0.824, 1.179, 0.866]]\nB: [[0.608, -0.936, 0.413, 0.854, 0.8, 0.77], [-0.425, -0.856, 0.339, 0.897, 0.767, 0.762], [-0.451, 1.126, 0.358, 0.838, 0.91, 0.764], [0.774, 1.047, 0.416, 0.815, 0.841, 0.775]]\nC: [[0.288, -1.283, 0.655, 0.817, 0.674, 0.566], [-0.233, -0.57, 0.023, 0.569, 0.942, 1.169], [-0.037, 0.77, 0.308, 0.824, 1.383, 0.685], [0.662, 1.515, 0.896, 0.594, 0.416, 0.9]]\nD: [[1.018, -1.113, 0.375, 0.665, 0.803, 1.039], [-0.701, -1.19, 0.042, 0.611, 0.648, 0.566], [-0.236, 1.15, 0.63, 1.12, 1.165, 0.969], [0.285, 0.606, 0.443, 1.268, 0.881, 0.591]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_166_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_166_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.711391, -0.463973, 0.527875], [-0.700286, 0.531398, -0.476672], [-0.059349, -0.708763, -0.702945]]; the translation vector: [2.53321, 4.394931, 1.530427], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.116, -0.769, 0.665, 0.588, 1.197, 1.025], [-0.796, -1.178, 0.171, 0.576, 0.604, 0.431], [-0.822, 1.221, 0.066, 0.696, 1.356, 0.542], [0.425, 0.671, 0.344, 0.824, 1.179, 0.866]]\nB: [[0.608, -0.936, 0.413, 0.854, 0.8, 0.77], [-0.425, -0.856, 0.339, 0.897, 0.767, 0.762], [-0.451, 1.126, 0.358, 0.838, 0.91, 0.764], [0.774, 1.047, 0.416, 0.815, 0.841, 0.775]]\nC: [[0.288, -1.283, 0.655, 0.817, 0.674, 0.566], [-0.233, -0.57, 0.023, 0.569, 0.942, 1.169], [-0.037, 0.77, 0.308, 0.824, 1.383, 0.685], [0.662, 1.515, 0.896, 0.594, 0.416, 0.9]]\nD: [[1.018, -1.113, 0.375, 0.665, 0.803, 1.039], [-0.701, -1.19, 0.042, 0.611, 0.648, 0.566], [-0.236, 1.15, 0.63, 1.12, 1.165, 0.969], [0.285, 0.606, 0.443, 1.268, 0.881, 0.591]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.097, 1.124, 0.669, 0.502, 0.516, 0.549], [-0.719, 0.622, 0.51, 0.696, 0.696, 1.008], [0.747, 0.329, 0.449, 0.568, 0.565, 0.934], [0.72, 0.839, 0.522, 0.626, 0.707, 0.997], [-0.373, -0.636, 0.467, 0.582, 0.551, 0.906]]\nB: [[0.297, 0.852, 0.7, 0.103, 0.44, 0.966], [-0.93, 0.904, 0.062, 0.986, 0.828, 0.767], [0.468, 0.69, 0.657, 0.758, 0.619, 1.108], [0.682, 0.702, 0.346, 0.75, 0.569, 0.847], [-0.423, -0.68, 0.291, 0.082, 0.385, 1.192]]\nC: [[0.512, 0.853, 0.312, 0.021, 0.921, 0.339], [-0.518, 0.57, 0.844, 1.067, 0.275, 1.347], [0.721, 0.423, 0.574, 0.387, 0.991, 1.286], [0.648, 0.46, 0.149, 0.657, 0.835, 0.53], [-0.541, -0.731, 0.203, 0.127, 0.654, 0.996]]\nD: [[0.168, 0.81, 1.159, 0.247, 0.182, 0.73], [-0.91, 0.423, 0.9, 0.946, 0.519, 0.547], [1.221, 0.571, 0.284, 0.571, 0.987, 1.376], [1.146, 0.534, 0.507, 0.778, 0.702, 1.372], [-0.13, -0.402, 0.492, 0.884, 0.774, 1.331]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_167_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_167_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.236277, -0.452541, 0.859872], [-0.970097, 0.160455, -0.182119], [-0.055554, -0.877189, -0.47692]]; the translation vector: [1.575898, 1.961144, 1.314442], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.097, 1.124, 0.669, 0.502, 0.516, 0.549], [-0.719, 0.622, 0.51, 0.696, 0.696, 1.008], [0.747, 0.329, 0.449, 0.568, 0.565, 0.934], [0.72, 0.839, 0.522, 0.626, 0.707, 0.997], [-0.373, -0.636, 0.467, 0.582, 0.551, 0.906]]\nB: [[0.297, 0.852, 0.7, 0.103, 0.44, 0.966], [-0.93, 0.904, 0.062, 0.986, 0.828, 0.767], [0.468, 0.69, 0.657, 0.758, 0.619, 1.108], [0.682, 0.702, 0.346, 0.75, 0.569, 0.847], [-0.423, -0.68, 0.291, 0.082, 0.385, 1.192]]\nC: [[0.512, 0.853, 0.312, 0.021, 0.921, 0.339], [-0.518, 0.57, 0.844, 1.067, 0.275, 1.347], [0.721, 0.423, 0.574, 0.387, 0.991, 1.286], [0.648, 0.46, 0.149, 0.657, 0.835, 0.53], [-0.541, -0.731, 0.203, 0.127, 0.654, 0.996]]\nD: [[0.168, 0.81, 1.159, 0.247, 0.182, 0.73], [-0.91, 0.423, 0.9, 0.946, 0.519, 0.547], [1.221, 0.571, 0.284, 0.571, 0.987, 1.376], [1.146, 0.534, 0.507, 0.778, 0.702, 1.372], [-0.13, -0.402, 0.492, 0.884, 0.774, 1.331]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[2.094, 0.511, 1.124, 0.123, 2.188, 0.539]]\nB: [[2.081, 0.516, 0.947, 0.355, 2.545, 0.592]]\nC: [[1.732, 0.343, 0.947, 0.511, 2.586, 0.308]]\nD: [[1.989, 0.949, 0.649, -0.276, 2.128, 0.539]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_168_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_168_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the whiteboard in the scene. The camera pose information includes: the rotation matrix: [[-0.997074, 0.061747, -0.045056], [0.074474, 0.651998, -0.754554], [-0.017215, -0.755702, -0.654689]]; the translation vector: [1.815792, 5.369752, 1.288561], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[2.094, 0.511, 1.124, 0.123, 2.188, 0.539]]\nB: [[2.081, 0.516, 0.947, 0.355, 2.545, 0.592]]\nC: [[1.732, 0.343, 0.947, 0.511, 2.586, 0.308]]\nD: [[1.989, 0.949, 0.649, -0.276, 2.128, 0.539]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.228, -1.39, 0.131, 0.85, 0.551, 0.683]]\nB: [[-1.305, -1.508, 0.232, 0.822, 0.566, 0.435]]\nC: [[-0.824, -1.786, 0.652, 1.27, 0.727, -0.053]]\nD: [[-0.844, -1.175, 0.453, 0.328, 0.627, 0.359]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_169_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_169_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the piano bench in the scene. The camera pose information includes: the rotation matrix: [[-0.804945, -0.278842, 0.523748], [-0.593014, 0.407765, -0.694307], [-0.019964, -0.869468, -0.493585]]; the translation vector: [4.871809, 2.494869, 1.402737], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.228, -1.39, 0.131, 0.85, 0.551, 0.683]]\nB: [[-1.305, -1.508, 0.232, 0.822, 0.566, 0.435]]\nC: [[-0.824, -1.786, 0.652, 1.27, 0.727, -0.053]]\nD: [[-0.844, -1.175, 0.453, 0.328, 0.627, 0.359]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.141, 1.242, 1.15, 2.629, 0.737, 2.324], [-1.855, -0.469, 1.171, 0.539, 3.779, 2.772], [0.841, 0.114, 0.787, 0.691, 3.588, 2.223], [0.571, -1.772, 1.52, 1.679, 0.887, 2.594]]\nB: [[-0.097, 1.504, 1.106, 3.029, 0.358, 2.218], [-1.545, 0.258, 1.211, 0.132, 3.756, 2.16], [0.905, 0.283, 1.397, -0.093, 3.002, 2.004], [0.394, -1.669, 1.139, 2.247, 0.169, 2.208]]\nC: [[-0.253, 1.653, 1.522, 3.078, 0.478, 2.791], [-1.503, -0.37, 1.376, 0.691, 4.127, 2.703], [1.422, -0.022, 0.986, 0.339, 3.887, 2.497], [-0.134, -1.344, 0.891, 2.344, 0.714, 2.451]]\nD: [[-0.058, 1.533, 1.269, 2.876, 0.624, 2.668], [-1.389, 0.007, 1.251, 0.231, 3.638, 2.637], [1.275, 0.042, 1.086, 0.289, 3.412, 2.272], [0.358, -1.537, 1.122, 1.906, 0.425, 2.129]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_170_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_170_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.032646, 0.194727, -0.980314], [0.998594, -0.034636, -0.040135], [-0.04177, -0.980246, -0.193322]]; the translation vector: [3.506056, 2.493951, 1.706783], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.141, 1.242, 1.15, 2.629, 0.737, 2.324], [-1.855, -0.469, 1.171, 0.539, 3.779, 2.772], [0.841, 0.114, 0.787, 0.691, 3.588, 2.223], [0.571, -1.772, 1.52, 1.679, 0.887, 2.594]]\nB: [[-0.097, 1.504, 1.106, 3.029, 0.358, 2.218], [-1.545, 0.258, 1.211, 0.132, 3.756, 2.16], [0.905, 0.283, 1.397, -0.093, 3.002, 2.004], [0.394, -1.669, 1.139, 2.247, 0.169, 2.208]]\nC: [[-0.253, 1.653, 1.522, 3.078, 0.478, 2.791], [-1.503, -0.37, 1.376, 0.691, 4.127, 2.703], [1.422, -0.022, 0.986, 0.339, 3.887, 2.497], [-0.134, -1.344, 0.891, 2.344, 0.714, 2.451]]\nD: [[-0.058, 1.533, 1.269, 2.876, 0.624, 2.668], [-1.389, 0.007, 1.251, 0.231, 3.638, 2.637], [1.275, 0.042, 1.086, 0.289, 3.412, 2.272], [0.358, -1.537, 1.122, 1.906, 0.425, 2.129]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.22, -0.005, 0.817, 1.09, 2.894, 0.662], [-2.657, -0.552, 1.11, 0.97, 1.155, 0.566]]\nB: [[-1.955, 0.127, 0.536, 1.492, 2.79, 1.348], [-2.155, 0.305, 0.66, 0.159, 1.455, 0.072]]\nC: [[-1.433, 0.186, 1.02, 1.626, 2.332, 1.534], [-2.448, -0.356, 0.853, 0.017, 1.445, 0.618]]\nD: [[-1.798, 0.428, 0.571, 1.201, 2.441, 1.114], [-2.518, -0.083, 1.081, 0.488, 1.535, 0.157]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_171_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_171_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the couch in the scene. The camera pose information includes: the rotation matrix: [[-0.205964, -0.505778, 0.837716], [-0.978495, 0.11627, -0.170378], [-0.011228, -0.854792, -0.518849]]; the translation vector: [2.901534, 4.292832, 1.280844], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.22, -0.005, 0.817, 1.09, 2.894, 0.662], [-2.657, -0.552, 1.11, 0.97, 1.155, 0.566]]\nB: [[-1.955, 0.127, 0.536, 1.492, 2.79, 1.348], [-2.155, 0.305, 0.66, 0.159, 1.455, 0.072]]\nC: [[-1.433, 0.186, 1.02, 1.626, 2.332, 1.534], [-2.448, -0.356, 0.853, 0.017, 1.445, 0.618]]\nD: [[-1.798, 0.428, 0.571, 1.201, 2.441, 1.114], [-2.518, -0.083, 1.081, 0.488, 1.535, 0.157]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.442, -1.133, 0.562, 0.636, 0.657, 0.49], [-0.931, -0.023, 0.592, 0.548, 0.635, 0.449], [1.185, -0.67, 0.523, 0.55, 0.618, 0.447], [-0.778, 1.905, 0.84, 0.606, 0.538, 0.514], [-0.723, -1.153, 0.514, 0.657, 0.632, 0.473], [-1.434, -0.489, 0.591, 0.567, 0.545, 0.458], [-1.479, -1.704, 0.547, 0.555, 0.643, 0.506], [-1.06, 0.579, 0.646, 0.554, 0.57, 0.426], [1.728, -0.095, 0.592, 0.547, 0.596, 0.473], [-1.358, 1.889, 0.774, 0.643, 0.662, 0.446], [2.187, 1.992, 0.739, 0.592, 0.503, 0.463], [-0.349, 1.313, 0.568, 0.481, 0.31, 0.827], [0.659, 1.035, 0.643, 0.561, 0.458, 0.449], [1.351, 1.116, 0.663, 0.567, 0.545, 0.469], [1.67, 0.521, 0.73, 0.179, 0.508, 0.285], [0.482, -0.974, 0.492, 0.592, 0.586, 0.475]]\nB: [[-1.049, -1.529, 1.034, 0.201, 0.822, 0.539], [-0.9, 0.339, 0.327, 0.273, 0.766, 0.553], [0.737, -1.05, 0.211, 0.082, 0.504, 0.933], [-1.047, 2.226, 0.838, 0.996, 0.859, 0.972], [-0.719, -0.678, 0.784, 0.49, 0.145, 0.261], [-1.882, -0.392, 0.818, 0.955, 0.143, 0.713], [-1.551, -2.013, 0.366, 0.53, 0.75, 0.368], [-1.315, 0.463, 0.891, 0.81, 0.604, 0.638], [2.147, -0.334, 0.803, 0.499, 0.844, 0.692], [-1.677, 2.042, 0.864, 0.402, 1.157, 0.639], [1.976, 2.077, 0.904, 0.918, 0.711, 0.254], [-0.187, 1.603, 0.781, 0.267, -0.088, 1.027], [0.26, 0.795, 0.514, 0.847, -0.04, 0.297], [1.756, 1.456, 0.644, 0.597, 0.817, 0.47], [1.242, 0.068, 0.373, 0.448, 0.149, 0.381], [0.319, -0.553, 0.655, 0.691, 0.359, 0.589]]\nC: [[-0.988, -1.282, 0.732, 0.336, 0.483, 0.927], [-0.442, -0.073, 0.808, 0.229, 0.772, 0.639], [1.548, -1.036, 0.108, 0.525, 0.245, 0.035], [-1.266, 1.685, 1.335, 0.956, 0.747, 0.267], [-1.079, -1.607, 1.01, 0.83, 1.062, 0.521], [-1.264, -0.925, 0.343, 1.047, 0.715, 0.269], [-1.458, -1.958, 0.337, 0.66, 0.161, 0.546], [-0.733, 0.312, 0.474, 0.521, 0.178, -0.061], [2.105, 0.263, 0.727, 0.39, 0.976, 0.108], [-1.707, 1.787, 0.496, 0.472, 1.062, 0.821], [2.45, 1.544, 0.321, 1.018, 0.15, 0.075], [-0.837, 1.59, 0.268, 0.538, 0.245, 0.497], [0.297, 1.19, 0.423, 0.185, 0.686, 0.323], [0.857, 1.058, 0.937, 0.887, 0.209, 0.519], [1.802, 0.184, 0.797, 0.22, 0.094, 0.637], [0.094, -0.987, 0.725, 0.553, 1.059, 0.036]]\nD: [[-1.227, -0.819, 0.642, 0.301, 0.736, 0.894], [-1.335, 0.35, 0.132, 0.881, 0.202, 0.441], [1.374, -0.345, 0.698, 0.363, 1.089, 0.667], [-0.963, 1.843, 0.91, 0.493, 0.498, 0.35], [-1.186, -1.506, 0.169, 0.581, 0.638, 0.951], [-1.772, -0.025, 0.967, 0.473, 0.884, -0.032], [-1.614, -1.94, 0.374, 0.725, 0.441, 0.512], [-1.408, 0.285, 1.05, 0.486, 0.297, 0.835], [2.021, -0.535, 0.654, 0.219, 0.759, 0.901], [-1.57, 2.203, 0.527, 0.16, 0.291, 0.718], [1.825, 2.298, 0.457, 1.052, 0.655, 0.73], [-0.153, 1.778, 0.354, 0.514, 0.609, 0.42], [0.512, 1.223, 0.597, 0.407, 0.628, 0.692], [1.022, 1.172, 0.206, 0.702, 0.301, 0.176], [1.64, 0.74, 0.55, 0.197, 0.956, 0.52], [0.38, -0.727, 0.278, 0.877, 0.781, 0.837]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_172_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_172_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.830629, 0.239867, -0.502514], [0.556756, 0.37214, -0.742654], [0.008867, -0.896647, -0.442658]]; the translation vector: [4.849209, 2.614689, 1.447477], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.442, -1.133, 0.562, 0.636, 0.657, 0.49], [-0.931, -0.023, 0.592, 0.548, 0.635, 0.449], [1.185, -0.67, 0.523, 0.55, 0.618, 0.447], [-0.778, 1.905, 0.84, 0.606, 0.538, 0.514], [-0.723, -1.153, 0.514, 0.657, 0.632, 0.473], [-1.434, -0.489, 0.591, 0.567, 0.545, 0.458], [-1.479, -1.704, 0.547, 0.555, 0.643, 0.506], [-1.06, 0.579, 0.646, 0.554, 0.57, 0.426], [1.728, -0.095, 0.592, 0.547, 0.596, 0.473], [-1.358, 1.889, 0.774, 0.643, 0.662, 0.446], [2.187, 1.992, 0.739, 0.592, 0.503, 0.463], [-0.349, 1.313, 0.568, 0.481, 0.31, 0.827], [0.659, 1.035, 0.643, 0.561, 0.458, 0.449], [1.351, 1.116, 0.663, 0.567, 0.545, 0.469], [1.67, 0.521, 0.73, 0.179, 0.508, 0.285], [0.482, -0.974, 0.492, 0.592, 0.586, 0.475]]\nB: [[-1.049, -1.529, 1.034, 0.201, 0.822, 0.539], [-0.9, 0.339, 0.327, 0.273, 0.766, 0.553], [0.737, -1.05, 0.211, 0.082, 0.504, 0.933], [-1.047, 2.226, 0.838, 0.996, 0.859, 0.972], [-0.719, -0.678, 0.784, 0.49, 0.145, 0.261], [-1.882, -0.392, 0.818, 0.955, 0.143, 0.713], [-1.551, -2.013, 0.366, 0.53, 0.75, 0.368], [-1.315, 0.463, 0.891, 0.81, 0.604, 0.638], [2.147, -0.334, 0.803, 0.499, 0.844, 0.692], [-1.677, 2.042, 0.864, 0.402, 1.157, 0.639], [1.976, 2.077, 0.904, 0.918, 0.711, 0.254], [-0.187, 1.603, 0.781, 0.267, -0.088, 1.027], [0.26, 0.795, 0.514, 0.847, -0.04, 0.297], [1.756, 1.456, 0.644, 0.597, 0.817, 0.47], [1.242, 0.068, 0.373, 0.448, 0.149, 0.381], [0.319, -0.553, 0.655, 0.691, 0.359, 0.589]]\nC: [[-0.988, -1.282, 0.732, 0.336, 0.483, 0.927], [-0.442, -0.073, 0.808, 0.229, 0.772, 0.639], [1.548, -1.036, 0.108, 0.525, 0.245, 0.035], [-1.266, 1.685, 1.335, 0.956, 0.747, 0.267], [-1.079, -1.607, 1.01, 0.83, 1.062, 0.521], [-1.264, -0.925, 0.343, 1.047, 0.715, 0.269], [-1.458, -1.958, 0.337, 0.66, 0.161, 0.546], [-0.733, 0.312, 0.474, 0.521, 0.178, -0.061], [2.105, 0.263, 0.727, 0.39, 0.976, 0.108], [-1.707, 1.787, 0.496, 0.472, 1.062, 0.821], [2.45, 1.544, 0.321, 1.018, 0.15, 0.075], [-0.837, 1.59, 0.268, 0.538, 0.245, 0.497], [0.297, 1.19, 0.423, 0.185, 0.686, 0.323], [0.857, 1.058, 0.937, 0.887, 0.209, 0.519], [1.802, 0.184, 0.797, 0.22, 0.094, 0.637], [0.094, -0.987, 0.725, 0.553, 1.059, 0.036]]\nD: [[-1.227, -0.819, 0.642, 0.301, 0.736, 0.894], [-1.335, 0.35, 0.132, 0.881, 0.202, 0.441], [1.374, -0.345, 0.698, 0.363, 1.089, 0.667], [-0.963, 1.843, 0.91, 0.493, 0.498, 0.35], [-1.186, -1.506, 0.169, 0.581, 0.638, 0.951], [-1.772, -0.025, 0.967, 0.473, 0.884, -0.032], [-1.614, -1.94, 0.374, 0.725, 0.441, 0.512], [-1.408, 0.285, 1.05, 0.486, 0.297, 0.835], [2.021, -0.535, 0.654, 0.219, 0.759, 0.901], [-1.57, 2.203, 0.527, 0.16, 0.291, 0.718], [1.825, 2.298, 0.457, 1.052, 0.655, 0.73], [-0.153, 1.778, 0.354, 0.514, 0.609, 0.42], [0.512, 1.223, 0.597, 0.407, 0.628, 0.692], [1.022, 1.172, 0.206, 0.702, 0.301, 0.176], [1.64, 0.74, 0.55, 0.197, 0.956, 0.52], [0.38, -0.727, 0.278, 0.877, 0.781, 0.837]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.285, 2.094, 0.223, 0.8, -0.126, 0.725], [0.89, 2.46, 0.447, -0.137, 0.338, 0.037]]\nB: [[0.378, 1.664, -0.029, 0.245, 0.007, 0.518], [0.551, 2.153, -0.045, 0.073, 0.825, 0.641]]\nC: [[0.842, 1.796, 0.181, 0.339, 0.338, 0.37], [0.768, 2.073, 0.205, 0.294, 0.394, 0.403]]\nD: [[0.562, 2.17, 0.079, 0.501, 0.638, 0.525], [0.42, 1.956, 0.647, 0.731, 0.278, 0.487]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_173_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_173_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the bucket in the scene. The camera pose information includes: the rotation matrix: [[-0.819759, -0.274444, 0.502669], [-0.572709, 0.39303, -0.719397], [-0.00013, -0.877615, -0.479366]]; the translation vector: [2.765326, 1.370172, 1.355227], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.285, 2.094, 0.223, 0.8, -0.126, 0.725], [0.89, 2.46, 0.447, -0.137, 0.338, 0.037]]\nB: [[0.378, 1.664, -0.029, 0.245, 0.007, 0.518], [0.551, 2.153, -0.045, 0.073, 0.825, 0.641]]\nC: [[0.842, 1.796, 0.181, 0.339, 0.338, 0.37], [0.768, 2.073, 0.205, 0.294, 0.394, 0.403]]\nD: [[0.562, 2.17, 0.079, 0.501, 0.638, 0.525], [0.42, 1.956, 0.647, 0.731, 0.278, 0.487]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.351, 1.709, 0.416, 3.301, 3.462, -0.205]]\nB: [[0.748, 1.385, 0.703, 3.676, 3.587, 0.247]]\nC: [[0.285, 1.079, 0.707, 4.151, 3.525, -0.098]]\nD: [[0.437, 1.63, 0.992, 3.864, 3.856, 0.472]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_174_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_174_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the table in the scene. The camera pose information includes: the rotation matrix: [[-0.119369, -0.433868, 0.893034], [-0.990549, 0.113242, -0.077387], [-0.067553, -0.893832, -0.443285]]; the translation vector: [3.407035, 4.679209, 1.397058], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.351, 1.709, 0.416, 3.301, 3.462, -0.205]]\nB: [[0.748, 1.385, 0.703, 3.676, 3.587, 0.247]]\nC: [[0.285, 1.079, 0.707, 4.151, 3.525, -0.098]]\nD: [[0.437, 1.63, 0.992, 3.864, 3.856, 0.472]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.171, -0.049, 0.811, 0.067, 1.74, 1.931], [2.378, 0.912, 0.851, 0.596, 2.524, 1.349], [0.415, -1.4, 0.688, 3.856, 0.243, 1.685], [2.428, -1.301, 0.364, 0.307, 0.039, 1.588], [-1.851, -0.771, 1.131, 0.437, 0.879, 1.981]]\nB: [[-2.124, 0.402, 0.972, 0.336, 1.814, 2.055], [2.714, 0.714, 0.306, -0.022, 2.698, 1.287], [0.218, -0.935, 0.625, 3.775, 0.411, 1.982], [2.74, -0.719, 0.42, -0.08, 0.24, 0.945], [-2.083, -0.772, 1.329, 0.652, 0.37, 2.117]]\nC: [[-2.229, 0.152, 1.164, 0.205, 1.859, 2.109], [2.442, 0.667, 0.678, 0.238, 2.976, 1.311], [0.131, -1.186, 0.807, 4.198, 0.217, 1.596], [2.311, -0.918, 0.648, 0.343, 0.478, 1.211], [-2.036, -0.925, 1.234, 0.571, 0.559, 1.867]]\nD: [[-2.234, 0.572, 0.912, 0.309, 1.595, 2.084], [2.569, 0.682, 1.117, -0.182, 2.613, 1.651], [-0.058, -0.811, 0.697, 4.093, -0.122, 2.058], [2.792, -1.246, 0.764, 0.753, 0.799, 0.76], [-1.948, -0.836, 1.559, 0.97, 0.14, 2.126]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_175_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_175_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.924746, 0.145405, -0.351715], [0.379908, 0.407811, -0.830277], [0.022707, -0.901414, -0.432362]]; the translation vector: [3.891577, 4.106122, 1.335216], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.171, -0.049, 0.811, 0.067, 1.74, 1.931], [2.378, 0.912, 0.851, 0.596, 2.524, 1.349], [0.415, -1.4, 0.688, 3.856, 0.243, 1.685], [2.428, -1.301, 0.364, 0.307, 0.039, 1.588], [-1.851, -0.771, 1.131, 0.437, 0.879, 1.981]]\nB: [[-2.124, 0.402, 0.972, 0.336, 1.814, 2.055], [2.714, 0.714, 0.306, -0.022, 2.698, 1.287], [0.218, -0.935, 0.625, 3.775, 0.411, 1.982], [2.74, -0.719, 0.42, -0.08, 0.24, 0.945], [-2.083, -0.772, 1.329, 0.652, 0.37, 2.117]]\nC: [[-2.229, 0.152, 1.164, 0.205, 1.859, 2.109], [2.442, 0.667, 0.678, 0.238, 2.976, 1.311], [0.131, -1.186, 0.807, 4.198, 0.217, 1.596], [2.311, -0.918, 0.648, 0.343, 0.478, 1.211], [-2.036, -0.925, 1.234, 0.571, 0.559, 1.867]]\nD: [[-2.234, 0.572, 0.912, 0.309, 1.595, 2.084], [2.569, 0.682, 1.117, -0.182, 2.613, 1.651], [-0.058, -0.811, 0.697, 4.093, -0.122, 2.058], [2.792, -1.246, 0.764, 0.753, 0.799, 0.76], [-1.948, -0.836, 1.559, 0.97, 0.14, 2.126]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.059, -1.275, 0.883, 0.298, 1.018, 2.01], [0.236, 1.462, 0.631, 0.873, 0.486, 1.281], [1.1, -1.001, 0.952, 1.349, 0.172, 2.338], [-0.839, 2.079, 0.96, 0.395, 0.842, 1.983], [1.955, -4.485, 1.036, 0.141, 0.98, 2.593], [-0.255, -0.481, 0.905, 0.161, 0.928, 1.969]]\nB: [[-0.954, -1.046, 0.462, 0.658, 0.618, 2.138], [-0.213, 1.899, 0.694, 0.708, 0.841, 1.686], [1.362, -1.133, 1.001, 1.465, -0.084, 2.501], [-0.862, 2.364, 0.854, 0.124, 0.853, 2.159], [2.413, -4.964, 0.774, -0.345, 1.16, 3.015], [0.156, -0.554, 0.434, -0.07, 0.695, 2.392]]\nC: [[-0.732, -1.166, 0.44, 0.739, 0.991, 1.593], [0.338, 1.95, 0.672, 0.941, 0.589, 1.757], [0.743, -0.963, 1.147, 1.448, -0.135, 2.517], [-1.129, 2.483, 1.375, 0.132, 1.054, 2.43], [1.587, -4.551, 0.847, 0.35, 0.965, 2.767], [-0.661, -0.507, 0.612, -0.243, 0.847, 1.515]]\nD: [[-0.907, -1.27, 0.42, -0.059, 1.138, 1.561], [0.626, 1.256, 1.105, 1.202, 0.216, 1.006], [0.877, -0.877, 1.149, 0.987, -0.045, 2.737], [-0.783, 1.691, 0.606, 0.081, 0.643, 2.205], [2.363, -4.049, 1.139, 0.229, 0.955, 2.439], [-0.196, -0.854, 0.721, 0.566, 0.583, 2.254]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_176_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_176_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[-0.996429, -0.081152, -0.023325], [-0.01119, 0.400709, -0.916137], [0.083693, -0.912604, -0.400187]]; the translation vector: [7.365378, 2.610504, 1.343957], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.059, -1.275, 0.883, 0.298, 1.018, 2.01], [0.236, 1.462, 0.631, 0.873, 0.486, 1.281], [1.1, -1.001, 0.952, 1.349, 0.172, 2.338], [-0.839, 2.079, 0.96, 0.395, 0.842, 1.983], [1.955, -4.485, 1.036, 0.141, 0.98, 2.593], [-0.255, -0.481, 0.905, 0.161, 0.928, 1.969]]\nB: [[-0.954, -1.046, 0.462, 0.658, 0.618, 2.138], [-0.213, 1.899, 0.694, 0.708, 0.841, 1.686], [1.362, -1.133, 1.001, 1.465, -0.084, 2.501], [-0.862, 2.364, 0.854, 0.124, 0.853, 2.159], [2.413, -4.964, 0.774, -0.345, 1.16, 3.015], [0.156, -0.554, 0.434, -0.07, 0.695, 2.392]]\nC: [[-0.732, -1.166, 0.44, 0.739, 0.991, 1.593], [0.338, 1.95, 0.672, 0.941, 0.589, 1.757], [0.743, -0.963, 1.147, 1.448, -0.135, 2.517], [-1.129, 2.483, 1.375, 0.132, 1.054, 2.43], [1.587, -4.551, 0.847, 0.35, 0.965, 2.767], [-0.661, -0.507, 0.612, -0.243, 0.847, 1.515]]\nD: [[-0.907, -1.27, 0.42, -0.059, 1.138, 1.561], [0.626, 1.256, 1.105, 1.202, 0.216, 1.006], [0.877, -0.877, 1.149, 0.987, -0.045, 2.737], [-0.783, 1.691, 0.606, 0.081, 0.643, 2.205], [2.363, -4.049, 1.139, 0.229, 0.955, 2.439], [-0.196, -0.854, 0.721, 0.566, 0.583, 2.254]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.662, -1.551, 0.955, 1.174, 1.083, 1.423]]\nB: [[0.488, -1.177, 0.89, 1.089, 0.729, 1.751]]\nC: [[0.483, -0.736, 0.965, 0.958, 0.277, 1.886]]\nD: [[0.649, -1.283, 0.609, 1.143, 1.139, 2.193]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_177_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_177_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the desk in the scene. The camera pose information includes: the rotation matrix: [[0.51864, -0.44867, 0.727811], [-0.853934, -0.229463, 0.467059], [-0.04255, -0.863738, -0.502143]]; the translation vector: [1.002297, 1.98866, 1.344191], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.662, -1.551, 0.955, 1.174, 1.083, 1.423]]\nB: [[0.488, -1.177, 0.89, 1.089, 0.729, 1.751]]\nC: [[0.483, -0.736, 0.965, 0.958, 0.277, 1.886]]\nD: [[0.649, -1.283, 0.609, 1.143, 1.139, 2.193]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.285, -1.171, 0.463, 0.532, 0.611, 0.923], [1.223, 1.763, 0.56, 0.667, 0.631, 0.966], [-0.307, 1.833, 0.5, 0.619, 0.589, 0.922], [-0.406, -0.951, 0.39, 0.539, 0.611, 0.908], [0.655, 1.709, 0.525, 0.699, 0.634, 0.947], [1.269, -2.956, 0.836, 0.629, 0.557, 0.393], [1.192, 0.557, 0.457, 0.623, 0.615, 0.94], [0.416, -2.765, 0.677, 0.614, 0.452, 0.612], [-0.522, 0.635, 0.418, 0.585, 0.574, 0.905], [-0.363, -3.094, 0.495, 0.715, 0.612, 0.912], [0.336, 0.66, 0.438, 0.602, 0.623, 0.904], [-2.007, -0.347, 0.408, 0.521, 0.585, 0.891], [0.411, -1.036, 0.417, 0.682, 0.634, 0.922], [-2.039, -2.805, 0.495, 0.561, 0.631, 0.9], [-1.956, -1.834, 0.436, 0.597, 0.728, 0.922], [-2.754, 1.479, 0.509, 0.58, 0.603, 0.892]]\nB: [[0.94, -0.734, 0.86, 0.847, 0.174, 1.366], [1.047, 1.724, 0.337, 1.114, 0.725, 1.18], [-0.446, 1.839, 0.399, 1.0, 0.211, 0.928], [-0.224, -0.996, 0.671, 0.902, 0.396, 0.957], [0.648, 2.199, 0.865, 0.644, 0.899, 0.978], [1.601, -3.15, 1.071, 0.541, 0.264, 0.224], [0.709, 0.399, 0.396, 0.628, 0.643, 1.257], [0.103, -2.816, 0.184, 1.095, 0.871, 0.909], [-0.735, 1.113, 0.158, 0.968, 0.355, 1.244], [-0.793, -3.536, 0.957, 0.881, 0.306, 1.233], [-0.114, 0.863, 0.498, 0.236, 0.716, 1.116], [-1.845, -0.397, 0.53, 0.528, 0.958, 0.727], [0.156, -0.653, 0.083, 0.658, 1.129, 0.686], [-2.166, -2.74, 0.163, 0.166, 0.842, 0.447], [-2.421, -1.954, 0.206, 0.882, 0.734, 0.761], [-3.119, 1.809, 0.685, 0.543, 0.98, 1.284]]\nC: [[0.87, -1.386, 0.953, 0.148, 0.539, 1.241], [0.822, 1.276, 0.128, 0.239, 0.572, 1.227], [-0.508, 2.214, 0.373, 0.683, 0.2, 1.183], [-0.547, -1.349, -0.07, 0.231, 0.312, 1.389], [0.457, 1.367, 0.965, 0.768, 0.185, 1.088], [1.563, -2.649, 0.498, 0.756, 0.364, 0.362], [1.083, 0.345, 0.921, 0.769, 0.695, 1.386], [0.143, -3.095, 0.202, 0.278, 0.051, 0.502], [-0.474, 0.978, 0.872, 0.559, 0.082, 1.262], [-0.01, -3.401, 0.115, 1.005, 0.452, 1.143], [-0.106, 1.086, 0.284, 0.105, 0.131, 0.844], [-2.44, -0.304, -0.054, 0.667, 0.457, 0.703], [0.747, -1.031, -0.051, 0.551, 0.84, 0.909], [-2.101, -2.554, 0.473, 1.017, 0.994, 1.065], [-1.883, -2.033, 0.423, 0.644, 1.201, 0.726], [-3.109, 1.24, 0.812, 0.728, 1.099, 0.829]]\nD: [[1.585, -0.899, 0.099, 0.724, 0.912, 0.466], [0.799, 2.074, 0.967, 0.764, 0.821, 0.506], [-0.531, 1.393, 0.134, 0.737, 1.022, 1.024], [-0.008, -0.984, -0.095, 0.085, 0.528, 0.524], [0.362, 2.074, 0.189, 0.835, 0.387, 0.74], [1.446, -2.709, 0.927, 0.329, 0.916, 0.373], [1.078, 0.299, 0.482, 0.303, 0.612, 0.521], [0.439, -2.308, 0.3, 0.788, 0.517, 0.416], [-0.314, 0.386, 0.749, 0.588, 0.522, 1.244], [-0.739, -2.845, 0.766, 0.695, 1.017, 0.779], [-0.116, 0.704, 0.487, 0.148, 0.185, 0.776], [-1.607, 0.118, 0.862, 0.934, 0.609, 0.752], [-0.074, -0.593, 0.062, 0.851, 0.522, 0.762], [-2.188, -3.11, 0.134, 0.427, 0.414, 0.637], [-1.911, -1.906, 0.292, 0.873, 0.728, 0.955], [-2.793, 1.335, 0.084, 0.946, 0.494, 0.463]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_178_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_178_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[0.931668, 0.072515, -0.356001], [0.362912, -0.231685, 0.902561], [-0.017031, -0.970084, -0.24217]]; the translation vector: [5.886859, 3.543659, 1.354971], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.285, -1.171, 0.463, 0.532, 0.611, 0.923], [1.223, 1.763, 0.56, 0.667, 0.631, 0.966], [-0.307, 1.833, 0.5, 0.619, 0.589, 0.922], [-0.406, -0.951, 0.39, 0.539, 0.611, 0.908], [0.655, 1.709, 0.525, 0.699, 0.634, 0.947], [1.269, -2.956, 0.836, 0.629, 0.557, 0.393], [1.192, 0.557, 0.457, 0.623, 0.615, 0.94], [0.416, -2.765, 0.677, 0.614, 0.452, 0.612], [-0.522, 0.635, 0.418, 0.585, 0.574, 0.905], [-0.363, -3.094, 0.495, 0.715, 0.612, 0.912], [0.336, 0.66, 0.438, 0.602, 0.623, 0.904], [-2.007, -0.347, 0.408, 0.521, 0.585, 0.891], [0.411, -1.036, 0.417, 0.682, 0.634, 0.922], [-2.039, -2.805, 0.495, 0.561, 0.631, 0.9], [-1.956, -1.834, 0.436, 0.597, 0.728, 0.922], [-2.754, 1.479, 0.509, 0.58, 0.603, 0.892]]\nB: [[0.94, -0.734, 0.86, 0.847, 0.174, 1.366], [1.047, 1.724, 0.337, 1.114, 0.725, 1.18], [-0.446, 1.839, 0.399, 1.0, 0.211, 0.928], [-0.224, -0.996, 0.671, 0.902, 0.396, 0.957], [0.648, 2.199, 0.865, 0.644, 0.899, 0.978], [1.601, -3.15, 1.071, 0.541, 0.264, 0.224], [0.709, 0.399, 0.396, 0.628, 0.643, 1.257], [0.103, -2.816, 0.184, 1.095, 0.871, 0.909], [-0.735, 1.113, 0.158, 0.968, 0.355, 1.244], [-0.793, -3.536, 0.957, 0.881, 0.306, 1.233], [-0.114, 0.863, 0.498, 0.236, 0.716, 1.116], [-1.845, -0.397, 0.53, 0.528, 0.958, 0.727], [0.156, -0.653, 0.083, 0.658, 1.129, 0.686], [-2.166, -2.74, 0.163, 0.166, 0.842, 0.447], [-2.421, -1.954, 0.206, 0.882, 0.734, 0.761], [-3.119, 1.809, 0.685, 0.543, 0.98, 1.284]]\nC: [[0.87, -1.386, 0.953, 0.148, 0.539, 1.241], [0.822, 1.276, 0.128, 0.239, 0.572, 1.227], [-0.508, 2.214, 0.373, 0.683, 0.2, 1.183], [-0.547, -1.349, -0.07, 0.231, 0.312, 1.389], [0.457, 1.367, 0.965, 0.768, 0.185, 1.088], [1.563, -2.649, 0.498, 0.756, 0.364, 0.362], [1.083, 0.345, 0.921, 0.769, 0.695, 1.386], [0.143, -3.095, 0.202, 0.278, 0.051, 0.502], [-0.474, 0.978, 0.872, 0.559, 0.082, 1.262], [-0.01, -3.401, 0.115, 1.005, 0.452, 1.143], [-0.106, 1.086, 0.284, 0.105, 0.131, 0.844], [-2.44, -0.304, -0.054, 0.667, 0.457, 0.703], [0.747, -1.031, -0.051, 0.551, 0.84, 0.909], [-2.101, -2.554, 0.473, 1.017, 0.994, 1.065], [-1.883, -2.033, 0.423, 0.644, 1.201, 0.726], [-3.109, 1.24, 0.812, 0.728, 1.099, 0.829]]\nD: [[1.585, -0.899, 0.099, 0.724, 0.912, 0.466], [0.799, 2.074, 0.967, 0.764, 0.821, 0.506], [-0.531, 1.393, 0.134, 0.737, 1.022, 1.024], [-0.008, -0.984, -0.095, 0.085, 0.528, 0.524], [0.362, 2.074, 0.189, 0.835, 0.387, 0.74], [1.446, -2.709, 0.927, 0.329, 0.916, 0.373], [1.078, 0.299, 0.482, 0.303, 0.612, 0.521], [0.439, -2.308, 0.3, 0.788, 0.517, 0.416], [-0.314, 0.386, 0.749, 0.588, 0.522, 1.244], [-0.739, -2.845, 0.766, 0.695, 1.017, 0.779], [-0.116, 0.704, 0.487, 0.148, 0.185, 0.776], [-1.607, 0.118, 0.862, 0.934, 0.609, 0.752], [-0.074, -0.593, 0.062, 0.851, 0.522, 0.762], [-2.188, -3.11, 0.134, 0.427, 0.414, 0.637], [-1.911, -1.906, 0.292, 0.873, 0.728, 0.955], [-2.793, 1.335, 0.084, 0.946, 0.494, 0.463]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.185, -1.981, 0.71, 1.746, 1.931, 1.092]]\nB: [[0.263, -1.622, 0.402, 1.389, 1.64, 0.804]]\nC: [[0.151, -1.735, 0.574, 1.767, 1.715, 0.631]]\nD: [[0.262, -2.035, 0.645, 1.006, 2.054, 1.049]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_179_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_179_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the table in the scene. The camera pose information includes: the rotation matrix: [[0.987126, 0.106622, -0.119219], [0.159938, -0.652529, 0.740693], [0.00118, -0.750225, -0.661181]]; the translation vector: [4.64166, 4.052867, 1.404314], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.185, -1.981, 0.71, 1.746, 1.931, 1.092]]\nB: [[0.263, -1.622, 0.402, 1.389, 1.64, 0.804]]\nC: [[0.151, -1.735, 0.574, 1.767, 1.715, 0.631]]\nD: [[0.262, -2.035, 0.645, 1.006, 2.054, 1.049]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.648, -0.593, 0.364, 0.758, 0.748, 0.835], [-1.189, -0.998, 0.388, 0.711, 0.664, 0.751], [-0.106, -0.14, 0.366, 0.681, 0.668, 0.806], [-0.467, -1.537, 0.381, 0.682, 0.66, 0.781]]\nB: [[0.715, -1.041, 0.651, 0.471, 0.834, 0.809], [-1.555, -0.972, 0.209, 0.39, 0.675, 1.08], [0.331, 0.337, 0.451, 0.906, 1.083, 1.138], [-0.367, -1.309, -0.086, 0.84, 1.029, 0.958]]\nC: [[0.76, -0.428, 0.328, 0.718, 0.602, 0.917], [-1.301, -1.169, 0.677, 0.824, 0.61, 0.712], [0.1, -0.045, 0.084, 0.878, 0.367, 0.431], [-0.14, -1.88, 0.43, 0.418, 0.474, 0.77]]\nD: [[0.587, -1.036, 0.299, 1.076, 1.171, 0.475], [-1.011, -1.458, 0.499, 0.276, 1.067, 0.759], [-0.487, -0.498, 0.17, 1.114, 0.58, 1.041], [-0.116, -1.819, 0.569, 0.961, 0.364, 1.204]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_180_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_180_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the armchair in the scene. The camera pose information includes: the rotation matrix: [[0.68967, 0.288211, -0.664297], [0.724122, -0.27239, 0.633602], [0.001663, -0.918008, -0.396559]]; the translation vector: [2.530043, 2.005069, 1.437417], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.648, -0.593, 0.364, 0.758, 0.748, 0.835], [-1.189, -0.998, 0.388, 0.711, 0.664, 0.751], [-0.106, -0.14, 0.366, 0.681, 0.668, 0.806], [-0.467, -1.537, 0.381, 0.682, 0.66, 0.781]]\nB: [[0.715, -1.041, 0.651, 0.471, 0.834, 0.809], [-1.555, -0.972, 0.209, 0.39, 0.675, 1.08], [0.331, 0.337, 0.451, 0.906, 1.083, 1.138], [-0.367, -1.309, -0.086, 0.84, 1.029, 0.958]]\nC: [[0.76, -0.428, 0.328, 0.718, 0.602, 0.917], [-1.301, -1.169, 0.677, 0.824, 0.61, 0.712], [0.1, -0.045, 0.084, 0.878, 0.367, 0.431], [-0.14, -1.88, 0.43, 0.418, 0.474, 0.77]]\nD: [[0.587, -1.036, 0.299, 1.076, 1.171, 0.475], [-1.011, -1.458, 0.499, 0.276, 1.067, 0.759], [-0.487, -0.498, 0.17, 1.114, 0.58, 1.041], [-0.116, -1.819, 0.569, 0.961, 0.364, 1.204]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.678, -1.667, 1.218, 1.055, -0.26, 2.418], [-1.464, 1.094, 0.83, 1.262, -0.304, 1.673], [0.614, 2.399, 0.708, 1.662, -0.241, 1.035], [0.965, -0.058, 1.477, 0.773, 4.136, 2.578], [-1.154, 1.558, 1.248, -0.057, 1.091, 2.617], [-1.717, -0.255, 1.603, -0.126, 2.491, 0.5]]\nB: [[-1.352, -1.046, 1.599, 1.44, 0.136, 2.792], [-1.654, 0.816, 0.791, 1.207, 0.132, 1.881], [0.521, 2.259, 1.273, 1.597, -0.332, 0.736], [0.806, 0.159, 1.62, 0.393, 4.568, 2.678], [-1.081, 1.507, 1.378, -0.253, 1.151, 2.742], [-1.495, -0.107, 1.278, 0.325, 1.906, 1.086]]\nC: [[-1.166, -1.418, 1.14, 1.061, 0.184, 2.392], [-1.606, 0.642, 1.143, 0.781, 0.159, 2.172], [0.167, 2.007, 1.118, 1.797, 0.138, 0.569], [0.908, -0.132, 1.206, 0.513, 4.305, 2.258], [-1.326, 1.1, 1.19, 0.242, 0.968, 2.282], [-1.838, -0.447, 1.348, 0.372, 2.121, 0.908]]\nD: [[-0.864, -1.506, 1.526, 0.637, 0.64, 2.228], [-1.745, 0.647, 0.899, 0.933, -0.243, 2.211], [-0.21, 1.507, 1.578, 2.065, 0.587, 0.484], [1.157, 0.294, 0.85, 0.968, 4.125, 2.603], [-1.113, 0.941, 1.165, 0.239, 0.756, 2.423], [-2.32, -0.87, 1.844, 0.517, 2.303, 0.518]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_181_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_181_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.964843, 0.186346, -0.185345], [0.252505, 0.461537, -0.850426], [-0.07293, -0.867329, -0.492364]]; the translation vector: [3.779865, 2.337391, 1.461827], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.678, -1.667, 1.218, 1.055, -0.26, 2.418], [-1.464, 1.094, 0.83, 1.262, -0.304, 1.673], [0.614, 2.399, 0.708, 1.662, -0.241, 1.035], [0.965, -0.058, 1.477, 0.773, 4.136, 2.578], [-1.154, 1.558, 1.248, -0.057, 1.091, 2.617], [-1.717, -0.255, 1.603, -0.126, 2.491, 0.5]]\nB: [[-1.352, -1.046, 1.599, 1.44, 0.136, 2.792], [-1.654, 0.816, 0.791, 1.207, 0.132, 1.881], [0.521, 2.259, 1.273, 1.597, -0.332, 0.736], [0.806, 0.159, 1.62, 0.393, 4.568, 2.678], [-1.081, 1.507, 1.378, -0.253, 1.151, 2.742], [-1.495, -0.107, 1.278, 0.325, 1.906, 1.086]]\nC: [[-1.166, -1.418, 1.14, 1.061, 0.184, 2.392], [-1.606, 0.642, 1.143, 0.781, 0.159, 2.172], [0.167, 2.007, 1.118, 1.797, 0.138, 0.569], [0.908, -0.132, 1.206, 0.513, 4.305, 2.258], [-1.326, 1.1, 1.19, 0.242, 0.968, 2.282], [-1.838, -0.447, 1.348, 0.372, 2.121, 0.908]]\nD: [[-0.864, -1.506, 1.526, 0.637, 0.64, 2.228], [-1.745, 0.647, 0.899, 0.933, -0.243, 2.211], [-0.21, 1.507, 1.578, 2.065, 0.587, 0.484], [1.157, 0.294, 0.85, 0.968, 4.125, 2.603], [-1.113, 0.941, 1.165, 0.239, 0.756, 2.423], [-2.32, -0.87, 1.844, 0.517, 2.303, 0.518]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.143, 1.32, 0.902, 0.946, 0.582, 0.814], [1.314, 2.961, 1.36, 1.125, 0.499, 2.359]]\nB: [[-1.164, 1.549, 1.101, 1.224, -0.166, 1.131], [1.215, 3.687, 1.039, 0.93, -0.333, 2.264]]\nC: [[-1.48, 1.652, 0.846, 0.755, 0.321, 1.167], [1.066, 3.327, 1.091, 1.04, 0.081, 1.998]]\nD: [[-1.81, 1.616, 0.892, 0.552, 0.779, 1.45], [1.382, 3.534, 1.284, 1.152, 0.521, 2.312]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_182_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_182_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the door in the scene. The camera pose information includes: the rotation matrix: [[-0.08083, -0.463089, 0.882618], [-0.994842, 0.091929, -0.042874], [-0.061284, -0.881531, -0.468131]]; the translation vector: [4.543997, 3.147744, 1.235262], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.143, 1.32, 0.902, 0.946, 0.582, 0.814], [1.314, 2.961, 1.36, 1.125, 0.499, 2.359]]\nB: [[-1.164, 1.549, 1.101, 1.224, -0.166, 1.131], [1.215, 3.687, 1.039, 0.93, -0.333, 2.264]]\nC: [[-1.48, 1.652, 0.846, 0.755, 0.321, 1.167], [1.066, 3.327, 1.091, 1.04, 0.081, 1.998]]\nD: [[-1.81, 1.616, 0.892, 0.552, 0.779, 1.45], [1.382, 3.534, 1.284, 1.152, 0.521, 2.312]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.277, 2.29, 0.603, 1.696, 0.522, 1.643], [-1.375, -0.321, 0.794, 0.343, 5.813, 1.671], [1.159, 0.472, 1.342, 0.486, 3.254, 0.972], [0.747, -2.391, 1.046, 0.652, 1.455, 1.87], [1.085, -1.961, 0.723, 1.174, -0.018, 1.576], [0.586, 1.92, 1.575, 0.646, -0.32, 2.439], [0.415, 2.748, 1.312, -0.384, 0.485, 2.051]]\nB: [[-0.351, 2.608, 0.955, 1.541, 0.097, 1.824], [-1.089, -0.096, 0.669, 0.099, 5.464, 1.395], [1.127, 0.133, 1.411, 0.212, 3.643, 0.932], [0.392, -2.442, 0.754, 0.163, 1.609, 1.541], [0.746, -1.664, 0.806, 0.833, 0.085, 1.637], [0.806, 1.971, 1.104, 0.829, 0.129, 2.13], [0.393, 2.271, 1.106, 0.064, 0.665, 2.126]]\nC: [[-0.013, 2.804, 0.64, 1.604, -0.241, 1.907], [-0.99, 0.257, 0.762, -0.167, 5.28, 1.868], [1.095, -0.318, 1.309, 0.698, 4.021, 0.652], [0.346, -2.805, 0.465, -0.167, 2.086, 1.213], [0.527, -1.307, 1.185, 0.733, -0.294, 1.468], [1.191, 1.911, 1.165, 0.69, 0.519, 1.853], [0.342, 2.498, 1.557, -0.047, 0.494, 2.435]]\nD: [[-0.612, 2.451, 1.013, 1.076, 0.146, 2.285], [-0.642, -0.043, 0.498, -0.353, 5.408, 1.585], [1.099, -0.043, 0.972, -0.204, 4.141, 1.05], [0.087, -2.832, 0.317, 0.167, 1.848, 1.113], [0.399, -1.498, 0.656, 1.117, 0.566, 1.989], [0.495, 2.449, 0.82, 0.411, 0.228, 2.522], [0.507, 2.378, 1.381, -0.184, 0.771, 1.769]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_183_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_183_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.286652, 0.220257, -0.932372], [0.958024, -0.061246, 0.28007], [0.004584, -0.973517, -0.228568]]; the translation vector: [3.76659, 1.676076, 1.452194], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.277, 2.29, 0.603, 1.696, 0.522, 1.643], [-1.375, -0.321, 0.794, 0.343, 5.813, 1.671], [1.159, 0.472, 1.342, 0.486, 3.254, 0.972], [0.747, -2.391, 1.046, 0.652, 1.455, 1.87], [1.085, -1.961, 0.723, 1.174, -0.018, 1.576], [0.586, 1.92, 1.575, 0.646, -0.32, 2.439], [0.415, 2.748, 1.312, -0.384, 0.485, 2.051]]\nB: [[-0.351, 2.608, 0.955, 1.541, 0.097, 1.824], [-1.089, -0.096, 0.669, 0.099, 5.464, 1.395], [1.127, 0.133, 1.411, 0.212, 3.643, 0.932], [0.392, -2.442, 0.754, 0.163, 1.609, 1.541], [0.746, -1.664, 0.806, 0.833, 0.085, 1.637], [0.806, 1.971, 1.104, 0.829, 0.129, 2.13], [0.393, 2.271, 1.106, 0.064, 0.665, 2.126]]\nC: [[-0.013, 2.804, 0.64, 1.604, -0.241, 1.907], [-0.99, 0.257, 0.762, -0.167, 5.28, 1.868], [1.095, -0.318, 1.309, 0.698, 4.021, 0.652], [0.346, -2.805, 0.465, -0.167, 2.086, 1.213], [0.527, -1.307, 1.185, 0.733, -0.294, 1.468], [1.191, 1.911, 1.165, 0.69, 0.519, 1.853], [0.342, 2.498, 1.557, -0.047, 0.494, 2.435]]\nD: [[-0.612, 2.451, 1.013, 1.076, 0.146, 2.285], [-0.642, -0.043, 0.498, -0.353, 5.408, 1.585], [1.099, -0.043, 0.972, -0.204, 4.141, 1.05], [0.087, -2.832, 0.317, 0.167, 1.848, 1.113], [0.399, -1.498, 0.656, 1.117, 0.566, 1.989], [0.495, 2.449, 0.82, 0.411, 0.228, 2.522], [0.507, 2.378, 1.381, -0.184, 0.771, 1.769]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.457, -0.683, 0.495, 0.679, 0.597, 0.903], [0.426, 0.843, 0.458, 0.566, 0.562, 0.949], [-0.336, 0.792, 0.461, 0.555, 0.539, 0.932], [0.926, -0.823, 0.62, 0.473, 0.568, 0.621], [-1.992, 0.348, 0.596, 0.635, 0.634, 0.647], [1.1, 0.858, 0.465, 0.529, 0.569, 0.952], [-0.253, -1.834, 0.689, 0.639, 0.561, 0.602], [-0.397, 2.119, 0.473, 0.759, 0.651, 0.94], [-1.254, -0.965, 0.657, 0.558, 0.592, 0.636], [-1.564, -0.168, 0.649, 0.462, 0.615, 0.611], [-0.169, -2.406, 0.711, 0.757, 0.669, 0.597], [1.375, -1.924, 0.508, 0.658, 0.509, 0.96], [0.214, -0.572, 0.651, 0.695, 0.494, 0.584], [-2.356, 2.053, 0.675, 0.673, 0.595, 0.536], [-0.799, -2.241, 0.541, 0.662, 0.673, 0.96], [1.941, -1.89, 0.718, 0.565, 0.56, 0.52], [2.571, -0.575, 0.472, 0.572, 0.595, 0.956], [-0.865, 2.028, 0.487, 0.583, 0.461, 0.128], [0.361, -2.459, 0.78, 0.536, 0.269, 0.489], [1.938, -1.474, 0.649, 0.57, 0.554, 0.6], [0.743, -2.15, 0.817, 0.526, 0.164, 0.344], [2.542, -1.129, 0.644, 0.598, 0.586, 0.612], [1.955, 0.145, 0.785, 0.141, 0.509, 0.304], [-1.685, 2.109, 0.449, 0.566, 0.486, 0.153], [-2.437, -1.918, 0.584, 0.515, 0.45, 0.203]]\nB: [[-0.004, -0.217, 0.058, 0.956, 0.104, 0.992], [0.634, 0.895, 0.282, 0.697, 0.893, 1.438], [-0.387, 0.349, 0.019, 0.239, 0.901, 0.96], [1.064, -1.168, 0.964, 0.374, 1.0, 0.253], [-1.606, 0.8, 0.112, 0.967, 0.862, 0.256], [1.288, 0.417, 0.225, 0.427, 0.112, 1.268], [-0.365, -1.988, 0.842, 0.932, 0.117, 0.21], [0.056, 2.617, 0.694, 0.602, 0.776, 0.848], [-1.173, -1.184, 0.2, 0.567, 0.839, 0.497], [-1.108, -0.485, 0.838, 0.382, 0.723, 1.057], [0.031, -2.18, 0.477, 1.078, 0.774, 0.574], [1.32, -1.614, 0.5, 0.512, 0.791, 1.227], [0.57, -1.002, 0.878, 0.861, 0.739, 0.347], [-2.072, 2.433, 1.143, 0.504, 1.054, 0.551], [-0.569, -2.64, 0.278, 0.616, 1.122, 1.078], [2.089, -1.691, 0.769, 0.97, 0.148, 0.992], [2.274, -0.687, 0.634, 0.56, 0.654, 0.811], [-1.223, 2.009, 0.495, 1.006, -0.028, 0.186], [0.145, -2.547, 0.54, 0.793, 0.387, 0.825], [1.744, -1.228, 0.533, 0.139, 0.886, 1.027], [1.123, -2.589, 1.183, 0.079, 0.187, 0.548], [2.235, -0.842, 0.485, 0.73, 0.575, 0.903], [1.694, -0.054, 1.249, 0.468, 0.557, 0.748], [-1.216, 2.107, 0.803, 0.764, 0.267, 0.274], [-2.623, -2.077, 1.01, 0.838, 0.106, -0.148]]\nC: [[-0.042, -0.277, 0.622, 0.349, 0.954, 1.11], [0.421, 0.801, 0.437, 0.094, 0.078, 1.2], [-0.436, 0.855, 0.625, 0.341, 0.737, 1.353], [0.599, -0.582, 0.28, 0.836, 0.717, 0.357], [-2.398, -0.135, 0.951, 0.429, 1.038, 0.502], [1.554, 0.709, 0.624, 0.144, 0.967, 1.304], [-0.666, -1.374, 0.422, 0.517, 0.122, 0.8], [-0.203, 1.908, 0.093, 1.027, 0.556, 0.76], [-1.532, -0.535, 0.437, 0.57, 0.41, 0.413], [-1.535, -0.307, 0.814, 0.936, 0.544, 1.082], [-0.39, -2.044, 0.309, 0.76, 0.801, 0.62], [1.044, -2.393, 0.932, 1.048, 0.287, 1.261], [0.664, -0.294, 1.14, 0.882, 0.176, 0.207], [-2.135, 2.211, 0.272, 0.963, 0.668, 0.76], [-1.028, -2.103, 1.016, 0.918, 0.609, 1.31], [1.579, -2.37, 0.458, 0.202, 0.159, 0.166], [2.079, -0.505, 0.945, 0.57, 0.86, 0.725], [-0.396, 2.379, 0.489, 0.77, 0.063, 0.52], [0.423, -2.492, 0.598, 0.788, 0.241, 0.406], [2.219, -1.548, 0.415, 0.429, 0.702, 0.329], [1.236, -1.961, 0.849, 0.371, 0.256, -0.039], [2.93, -1.099, 1.108, 0.393, 0.388, 0.187], [1.738, -0.099, 0.354, 0.013, 0.06, 0.667], [-1.711, 2.599, 0.36, 0.548, 0.69, -0.323], [-1.988, -1.796, 0.232, 0.609, 0.912, -0.043]]\nD: [[-0.117, -0.712, 0.165, 0.707, 0.749, 0.416], [0.663, 1.109, 0.92, 0.786, 0.382, 0.761], [-0.485, 1.276, -0.006, 0.122, 0.579, 0.562], [0.651, -1.033, 0.48, 0.012, 0.291, 0.281], [-1.67, 0.137, 0.785, 1.091, 0.142, 0.851], [1.44, 0.455, 0.476, 0.133, 0.572, 0.925], [-0.342, -1.74, 0.35, 0.646, 0.394, 0.443], [-0.793, 2.134, 0.146, 1.105, 0.456, 0.742], [-1.574, -0.65, 0.985, 0.2, 0.168, 1.102], [-1.526, 0.104, 0.427, 0.23, 0.555, 0.818], [-0.21, -2.447, 0.593, 1.166, 1.051, 0.465], [1.11, -2.085, 0.532, 0.952, 0.334, 0.936], [-0.231, -0.532, 0.895, 0.826, 0.523, 0.78], [-2.843, 1.728, 0.764, 0.92, 0.672, 0.101], [-0.845, -1.905, 0.458, 0.184, 0.635, 1.348], [1.844, -1.433, 1.033, 0.147, 0.968, 0.118], [2.166, -0.542, 0.733, 0.117, 0.957, 0.814], [-0.92, 1.743, 0.237, 0.993, 0.477, 0.227], [0.31, -2.458, 0.659, 0.782, 0.696, 0.669], [1.626, -1.353, 0.953, 0.579, 0.517, 0.513], [0.856, -2.414, 0.37, 0.927, 0.46, -0.04], [2.564, -1.512, 0.576, 0.167, 0.528, 0.323], [2.064, -0.007, 0.399, -0.022, 0.444, 0.68], [-1.281, 1.93, 0.706, 0.411, 0.266, -0.223], [-2.823, -2.037, 0.823, 0.649, 0.539, 0.078]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_184_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_184_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the chair in the scene. The camera pose information includes: the rotation matrix: [[-0.895509, 0.17248, -0.410263], [0.444823, 0.375965, -0.812886], [0.014038, -0.91044, -0.413402]]; the translation vector: [2.818061, 5.409916, 1.54775], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.457, -0.683, 0.495, 0.679, 0.597, 0.903], [0.426, 0.843, 0.458, 0.566, 0.562, 0.949], [-0.336, 0.792, 0.461, 0.555, 0.539, 0.932], [0.926, -0.823, 0.62, 0.473, 0.568, 0.621], [-1.992, 0.348, 0.596, 0.635, 0.634, 0.647], [1.1, 0.858, 0.465, 0.529, 0.569, 0.952], [-0.253, -1.834, 0.689, 0.639, 0.561, 0.602], [-0.397, 2.119, 0.473, 0.759, 0.651, 0.94], [-1.254, -0.965, 0.657, 0.558, 0.592, 0.636], [-1.564, -0.168, 0.649, 0.462, 0.615, 0.611], [-0.169, -2.406, 0.711, 0.757, 0.669, 0.597], [1.375, -1.924, 0.508, 0.658, 0.509, 0.96], [0.214, -0.572, 0.651, 0.695, 0.494, 0.584], [-2.356, 2.053, 0.675, 0.673, 0.595, 0.536], [-0.799, -2.241, 0.541, 0.662, 0.673, 0.96], [1.941, -1.89, 0.718, 0.565, 0.56, 0.52], [2.571, -0.575, 0.472, 0.572, 0.595, 0.956], [-0.865, 2.028, 0.487, 0.583, 0.461, 0.128], [0.361, -2.459, 0.78, 0.536, 0.269, 0.489], [1.938, -1.474, 0.649, 0.57, 0.554, 0.6], [0.743, -2.15, 0.817, 0.526, 0.164, 0.344], [2.542, -1.129, 0.644, 0.598, 0.586, 0.612], [1.955, 0.145, 0.785, 0.141, 0.509, 0.304], [-1.685, 2.109, 0.449, 0.566, 0.486, 0.153], [-2.437, -1.918, 0.584, 0.515, 0.45, 0.203]]\nB: [[-0.004, -0.217, 0.058, 0.956, 0.104, 0.992], [0.634, 0.895, 0.282, 0.697, 0.893, 1.438], [-0.387, 0.349, 0.019, 0.239, 0.901, 0.96], [1.064, -1.168, 0.964, 0.374, 1.0, 0.253], [-1.606, 0.8, 0.112, 0.967, 0.862, 0.256], [1.288, 0.417, 0.225, 0.427, 0.112, 1.268], [-0.365, -1.988, 0.842, 0.932, 0.117, 0.21], [0.056, 2.617, 0.694, 0.602, 0.776, 0.848], [-1.173, -1.184, 0.2, 0.567, 0.839, 0.497], [-1.108, -0.485, 0.838, 0.382, 0.723, 1.057], [0.031, -2.18, 0.477, 1.078, 0.774, 0.574], [1.32, -1.614, 0.5, 0.512, 0.791, 1.227], [0.57, -1.002, 0.878, 0.861, 0.739, 0.347], [-2.072, 2.433, 1.143, 0.504, 1.054, 0.551], [-0.569, -2.64, 0.278, 0.616, 1.122, 1.078], [2.089, -1.691, 0.769, 0.97, 0.148, 0.992], [2.274, -0.687, 0.634, 0.56, 0.654, 0.811], [-1.223, 2.009, 0.495, 1.006, -0.028, 0.186], [0.145, -2.547, 0.54, 0.793, 0.387, 0.825], [1.744, -1.228, 0.533, 0.139, 0.886, 1.027], [1.123, -2.589, 1.183, 0.079, 0.187, 0.548], [2.235, -0.842, 0.485, 0.73, 0.575, 0.903], [1.694, -0.054, 1.249, 0.468, 0.557, 0.748], [-1.216, 2.107, 0.803, 0.764, 0.267, 0.274], [-2.623, -2.077, 1.01, 0.838, 0.106, -0.148]]\nC: [[-0.042, -0.277, 0.622, 0.349, 0.954, 1.11], [0.421, 0.801, 0.437, 0.094, 0.078, 1.2], [-0.436, 0.855, 0.625, 0.341, 0.737, 1.353], [0.599, -0.582, 0.28, 0.836, 0.717, 0.357], [-2.398, -0.135, 0.951, 0.429, 1.038, 0.502], [1.554, 0.709, 0.624, 0.144, 0.967, 1.304], [-0.666, -1.374, 0.422, 0.517, 0.122, 0.8], [-0.203, 1.908, 0.093, 1.027, 0.556, 0.76], [-1.532, -0.535, 0.437, 0.57, 0.41, 0.413], [-1.535, -0.307, 0.814, 0.936, 0.544, 1.082], [-0.39, -2.044, 0.309, 0.76, 0.801, 0.62], [1.044, -2.393, 0.932, 1.048, 0.287, 1.261], [0.664, -0.294, 1.14, 0.882, 0.176, 0.207], [-2.135, 2.211, 0.272, 0.963, 0.668, 0.76], [-1.028, -2.103, 1.016, 0.918, 0.609, 1.31], [1.579, -2.37, 0.458, 0.202, 0.159, 0.166], [2.079, -0.505, 0.945, 0.57, 0.86, 0.725], [-0.396, 2.379, 0.489, 0.77, 0.063, 0.52], [0.423, -2.492, 0.598, 0.788, 0.241, 0.406], [2.219, -1.548, 0.415, 0.429, 0.702, 0.329], [1.236, -1.961, 0.849, 0.371, 0.256, -0.039], [2.93, -1.099, 1.108, 0.393, 0.388, 0.187], [1.738, -0.099, 0.354, 0.013, 0.06, 0.667], [-1.711, 2.599, 0.36, 0.548, 0.69, -0.323], [-1.988, -1.796, 0.232, 0.609, 0.912, -0.043]]\nD: [[-0.117, -0.712, 0.165, 0.707, 0.749, 0.416], [0.663, 1.109, 0.92, 0.786, 0.382, 0.761], [-0.485, 1.276, -0.006, 0.122, 0.579, 0.562], [0.651, -1.033, 0.48, 0.012, 0.291, 0.281], [-1.67, 0.137, 0.785, 1.091, 0.142, 0.851], [1.44, 0.455, 0.476, 0.133, 0.572, 0.925], [-0.342, -1.74, 0.35, 0.646, 0.394, 0.443], [-0.793, 2.134, 0.146, 1.105, 0.456, 0.742], [-1.574, -0.65, 0.985, 0.2, 0.168, 1.102], [-1.526, 0.104, 0.427, 0.23, 0.555, 0.818], [-0.21, -2.447, 0.593, 1.166, 1.051, 0.465], [1.11, -2.085, 0.532, 0.952, 0.334, 0.936], [-0.231, -0.532, 0.895, 0.826, 0.523, 0.78], [-2.843, 1.728, 0.764, 0.92, 0.672, 0.101], [-0.845, -1.905, 0.458, 0.184, 0.635, 1.348], [1.844, -1.433, 1.033, 0.147, 0.968, 0.118], [2.166, -0.542, 0.733, 0.117, 0.957, 0.814], [-0.92, 1.743, 0.237, 0.993, 0.477, 0.227], [0.31, -2.458, 0.659, 0.782, 0.696, 0.669], [1.626, -1.353, 0.953, 0.579, 0.517, 0.513], [0.856, -2.414, 0.37, 0.927, 0.46, -0.04], [2.564, -1.512, 0.576, 0.167, 0.528, 0.323], [2.064, -0.007, 0.399, -0.022, 0.444, 0.68], [-1.281, 1.93, 0.706, 0.411, 0.266, -0.223], [-2.823, -2.037, 0.823, 0.649, 0.539, 0.078]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.878, 0.793, 0.525, 0.307, 1.138, 0.24], [-1.741, 1.942, 0.919, 0.374, 0.284, 0.479], [-0.97, -1.167, 0.396, 0.145, 0.451, -0.19], [-1.083, -1.878, 0.816, 0.138, 0.403, 0.089], [-0.905, -1.314, 0.204, 0.196, 0.671, 0.614], [0.896, -0.37, -0.05, 0.065, 0.48, 0.052], [-0.311, 2.442, 0.913, 0.38, 0.489, 1.491]]\nB: [[-0.86, 0.987, 0.429, 0.753, 0.659, 0.935], [-1.179, 1.249, 1.101, 0.219, 0.461, 0.432], [-1.149, -1.648, 0.72, 0.293, 0.482, 0.205], [-0.806, -1.928, 1.378, 0.631, 0.665, 0.667], [-0.762, -1.463, 0.175, -0.154, 0.327, 0.058], [1.471, -0.937, 0.34, 0.631, 0.439, 0.05], [0.043, 1.928, 0.641, 0.637, 0.49, 0.81]]\nC: [[-0.458, 0.879, 0.858, 0.541, 0.86, 0.578], [-1.685, 1.754, 1.421, 0.241, 0.756, -0.241], [-1.527, -1.671, 0.922, 0.635, 0.013, 0.552], [-1.217, -1.15, 1.123, 0.142, 0.166, 0.441], [-1.042, -1.813, 0.54, 0.438, 0.445, 0.211], [1.239, -0.633, 0.179, 0.181, 0.23, 0.74], [-0.011, 2.472, 1.042, 0.129, 0.472, 1.438]]\nD: [[-0.55, 0.944, 0.644, 0.68, 1.046, 0.521], [-1.267, 1.712, 1.229, 0.359, 0.367, 0.165], [-1.24, -1.459, 0.828, 0.501, 0.283, 0.305], [-1.303, -1.553, 1.014, 0.399, 0.228, 0.178], [-0.73, -1.694, 0.629, 0.212, 0.251, 0.137], [1.337, -0.693, 0.283, 0.145, 0.467, 0.539], [0.135, 2.342, 0.574, 0.546, 0.62, 1.068]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_185_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_185_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the object in the scene. The camera pose information includes: the rotation matrix: [[0.330673, -0.328207, 0.884837], [-0.942686, -0.070458, 0.326157], [-0.044703, -0.941975, -0.332694]]; the translation vector: [3.753276, 4.481459, 1.345242], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.878, 0.793, 0.525, 0.307, 1.138, 0.24], [-1.741, 1.942, 0.919, 0.374, 0.284, 0.479], [-0.97, -1.167, 0.396, 0.145, 0.451, -0.19], [-1.083, -1.878, 0.816, 0.138, 0.403, 0.089], [-0.905, -1.314, 0.204, 0.196, 0.671, 0.614], [0.896, -0.37, -0.05, 0.065, 0.48, 0.052], [-0.311, 2.442, 0.913, 0.38, 0.489, 1.491]]\nB: [[-0.86, 0.987, 0.429, 0.753, 0.659, 0.935], [-1.179, 1.249, 1.101, 0.219, 0.461, 0.432], [-1.149, -1.648, 0.72, 0.293, 0.482, 0.205], [-0.806, -1.928, 1.378, 0.631, 0.665, 0.667], [-0.762, -1.463, 0.175, -0.154, 0.327, 0.058], [1.471, -0.937, 0.34, 0.631, 0.439, 0.05], [0.043, 1.928, 0.641, 0.637, 0.49, 0.81]]\nC: [[-0.458, 0.879, 0.858, 0.541, 0.86, 0.578], [-1.685, 1.754, 1.421, 0.241, 0.756, -0.241], [-1.527, -1.671, 0.922, 0.635, 0.013, 0.552], [-1.217, -1.15, 1.123, 0.142, 0.166, 0.441], [-1.042, -1.813, 0.54, 0.438, 0.445, 0.211], [1.239, -0.633, 0.179, 0.181, 0.23, 0.74], [-0.011, 2.472, 1.042, 0.129, 0.472, 1.438]]\nD: [[-0.55, 0.944, 0.644, 0.68, 1.046, 0.521], [-1.267, 1.712, 1.229, 0.359, 0.367, 0.165], [-1.24, -1.459, 0.828, 0.501, 0.283, 0.305], [-1.303, -1.553, 1.014, 0.399, 0.228, 0.178], [-0.73, -1.694, 0.629, 0.212, 0.251, 0.137], [1.337, -0.693, 0.283, 0.145, 0.467, 0.539], [0.135, 2.342, 0.574, 0.546, 0.62, 1.068]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.565, -1.185, 1.353, 0.464, 0.006, 0.118], [0.412, -0.591, 0.819, 0.036, 0.543, 0.322], [0.775, 0.247, 0.843, 0.919, 0.864, 0.389]]\nB: [[-0.648, -1.262, 0.922, 0.446, 0.433, 0.522], [0.437, -0.235, 0.949, 0.366, 0.445, 0.454], [0.764, 0.145, 0.941, 0.483, 0.409, 0.473]]\nC: [[-0.794, -1.422, 1.325, 0.646, -0.011, 0.511], [0.55, -0.124, 0.97, 0.767, 0.276, 0.151], [0.692, 0.134, 0.818, 0.04, 0.142, 0.775]]\nD: [[-0.888, -1.602, 1.373, 0.357, 0.797, 0.596], [0.014, -0.496, 0.808, 0.816, 0.004, 0.14], [0.932, 0.14, 0.871, 0.799, 0.355, 0.358]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_186_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_186_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the monitor in the scene. The camera pose information includes: the rotation matrix: [[0.054781, -0.427281, 0.902458], [-0.998013, -0.051617, 0.036143], [0.031139, -0.902644, -0.429259]]; the translation vector: [1.328526, 0.849821, 1.501181], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.565, -1.185, 1.353, 0.464, 0.006, 0.118], [0.412, -0.591, 0.819, 0.036, 0.543, 0.322], [0.775, 0.247, 0.843, 0.919, 0.864, 0.389]]\nB: [[-0.648, -1.262, 0.922, 0.446, 0.433, 0.522], [0.437, -0.235, 0.949, 0.366, 0.445, 0.454], [0.764, 0.145, 0.941, 0.483, 0.409, 0.473]]\nC: [[-0.794, -1.422, 1.325, 0.646, -0.011, 0.511], [0.55, -0.124, 0.97, 0.767, 0.276, 0.151], [0.692, 0.134, 0.818, 0.04, 0.142, 0.775]]\nD: [[-0.888, -1.602, 1.373, 0.357, 0.797, 0.596], [0.014, -0.496, 0.808, 0.816, 0.004, 0.14], [0.932, 0.14, 0.871, 0.799, 0.355, 0.358]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.353, -1.905, 0.542, 0.198, 0.811, 0.866]]\nB: [[-1.69, -2.015, 0.887, 0.014, 0.72, 0.41]]\nC: [[-1.178, -2.25, 0.868, 0.547, 0.466, 0.935]]\nD: [[-1.26, -1.838, 0.523, -0.212, 0.311, 0.619]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_187_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_187_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the dishwasher in the scene. The camera pose information includes: the rotation matrix: [[0.752445, 0.275595, -0.598225], [0.657828, -0.35994, 0.661593], [-0.032994, -0.891342, -0.452129]]; the translation vector: [2.633805, 2.70906, 1.31733], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.353, -1.905, 0.542, 0.198, 0.811, 0.866]]\nB: [[-1.69, -2.015, 0.887, 0.014, 0.72, 0.41]]\nC: [[-1.178, -2.25, 0.868, 0.547, 0.466, 0.935]]\nD: [[-1.26, -1.838, 0.523, -0.212, 0.311, 0.619]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.575, -1.33, 0.913, 0.422, 0.222, 1.282], [-1.133, -0.569, 0.691, 0.502, 1.733, 0.362], [-1.177, -0.378, 1.14, 0.427, 2.109, 0.616], [-1.073, 1.114, 1.403, 0.582, 1.219, 1.213], [-0.205, 1.471, 1.068, 1.327, 0.559, 1.11], [0.394, 0.971, 1.401, 0.368, 1.532, 0.97], [0.465, 0.918, 0.399, 0.657, 1.295, 0.944], [-1.033, 0.855, 0.494, 0.561, 0.838, 0.96]]\nB: [[0.612, -1.146, 1.029, 0.876, 0.249, 1.097], [-1.186, -0.558, 0.781, 0.832, 1.793, 0.11], [-0.68, -0.259, 1.327, 0.321, 2.085, 0.39], [-0.751, 1.462, 1.244, 1.064, 1.13, 1.071], [-0.245, 1.694, 1.448, 1.271, 0.405, 0.826], [0.501, 1.197, 1.032, 0.635, 1.295, 1.137], [0.575, 1.298, 0.738, 0.961, 1.68, 0.895], [-1.135, 0.586, 0.775, 0.711, 1.079, 0.526]]\nC: [[0.678, -1.206, 0.812, 0.848, -0.174, 1.369], [-1.023, -0.705, 0.492, 0.502, 1.434, -0.09], [-1.388, -0.068, 1.103, 0.59, 1.707, 0.559], [-1.152, 1.027, 1.347, 0.752, 0.971, 1.412], [-0.198, 1.443, 1.383, 1.532, 0.499, 1.267], [0.854, 0.79, 1.691, 0.351, 1.682, 0.641], [0.791, 0.546, 0.687, 0.219, 1.088, 1.252], [-0.634, 1.336, 0.286, 0.814, 1.197, 1.221]]\nD: [[0.82, -1.281, 0.96, -0.009, 0.662, 1.248], [-1.615, -0.505, 0.267, 0.866, 1.991, 0.496], [-0.964, -0.4, 1.176, 0.247, 2.442, 0.894], [-1.088, 1.358, 1.232, 0.782, 1.082, 0.821], [0.043, 1.718, 1.49, 1.508, 0.835, 1.275], [0.739, 1.084, 1.461, 0.376, 1.382, 1.444], [0.407, 0.623, 0.633, 0.434, 1.281, 1.107], [-1.311, 0.453, 0.839, 0.768, 1.093, 0.774]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_188_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_188_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the clothes in the scene. The camera pose information includes: the rotation matrix: [[0.88123, -0.188698, 0.433389], [-0.470321, -0.258404, 0.843816], [-0.047237, -0.947428, -0.316462]]; the translation vector: [1.061636, 1.321782, 1.457525], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.575, -1.33, 0.913, 0.422, 0.222, 1.282], [-1.133, -0.569, 0.691, 0.502, 1.733, 0.362], [-1.177, -0.378, 1.14, 0.427, 2.109, 0.616], [-1.073, 1.114, 1.403, 0.582, 1.219, 1.213], [-0.205, 1.471, 1.068, 1.327, 0.559, 1.11], [0.394, 0.971, 1.401, 0.368, 1.532, 0.97], [0.465, 0.918, 0.399, 0.657, 1.295, 0.944], [-1.033, 0.855, 0.494, 0.561, 0.838, 0.96]]\nB: [[0.612, -1.146, 1.029, 0.876, 0.249, 1.097], [-1.186, -0.558, 0.781, 0.832, 1.793, 0.11], [-0.68, -0.259, 1.327, 0.321, 2.085, 0.39], [-0.751, 1.462, 1.244, 1.064, 1.13, 1.071], [-0.245, 1.694, 1.448, 1.271, 0.405, 0.826], [0.501, 1.197, 1.032, 0.635, 1.295, 1.137], [0.575, 1.298, 0.738, 0.961, 1.68, 0.895], [-1.135, 0.586, 0.775, 0.711, 1.079, 0.526]]\nC: [[0.678, -1.206, 0.812, 0.848, -0.174, 1.369], [-1.023, -0.705, 0.492, 0.502, 1.434, -0.09], [-1.388, -0.068, 1.103, 0.59, 1.707, 0.559], [-1.152, 1.027, 1.347, 0.752, 0.971, 1.412], [-0.198, 1.443, 1.383, 1.532, 0.499, 1.267], [0.854, 0.79, 1.691, 0.351, 1.682, 0.641], [0.791, 0.546, 0.687, 0.219, 1.088, 1.252], [-0.634, 1.336, 0.286, 0.814, 1.197, 1.221]]\nD: [[0.82, -1.281, 0.96, -0.009, 0.662, 1.248], [-1.615, -0.505, 0.267, 0.866, 1.991, 0.496], [-0.964, -0.4, 1.176, 0.247, 2.442, 0.894], [-1.088, 1.358, 1.232, 0.782, 1.082, 0.821], [0.043, 1.718, 1.49, 1.508, 0.835, 1.275], [0.739, 1.084, 1.461, 0.376, 1.382, 1.444], [0.407, 0.623, 0.633, 0.434, 1.281, 1.107], [-1.311, 0.453, 0.839, 0.768, 1.093, 0.774]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.405, 0.601, 0.764, -0.233, 0.102, -0.396]]\nB: [[-1.074, 0.387, 1.003, 0.303, 0.098, -0.079]]\nC: [[-1.416, 1.278, 0.402, -0.026, 0.472, 0.541]]\nD: [[-1.238, 0.875, 0.853, 0.207, 0.18, 0.059]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_189_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_189_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the washcloth in the scene. The camera pose information includes: the rotation matrix: [[-0.922168, 0.178823, -0.342969], [0.38661, 0.453076, -0.803278], [0.011746, -0.873352, -0.486947]]; the translation vector: [3.207336, 1.959871, 1.267555], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.405, 0.601, 0.764, -0.233, 0.102, -0.396]]\nB: [[-1.074, 0.387, 1.003, 0.303, 0.098, -0.079]]\nC: [[-1.416, 1.278, 0.402, -0.026, 0.472, 0.541]]\nD: [[-1.238, 0.875, 0.853, 0.207, 0.18, 0.059]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.762, -1.555, 0.827, 2.184, 0.001, 1.66], [-1.543, -2.246, 0.687, 2.166, 0.538, 1.705], [-0.782, 1.874, 0.612, 0.36, -0.393, 2.288], [-2.462, 0.204, 1.234, 0.129, 3.712, 1.462], [-0.383, -1.635, 0.309, 0.432, -0.146, 1.237], [2.291, 0.002, 1.16, 0.467, 3.329, 1.816], [1.638, 1.427, 0.73, 2.104, 0.102, 1.563], [-1.67, 1.91, 0.562, 0.741, 0.507, 1.714]]\nB: [[1.374, -1.714, 0.725, 2.507, 0.169, 1.413], [-1.211, -1.757, 0.826, 2.443, 0.176, 1.694], [-0.519, 1.79, 0.908, 0.294, 0.099, 1.833], [-2.419, 0.035, 0.987, 0.337, 3.555, 1.874], [0.072, -1.69, 0.634, 0.2, 0.284, 1.225], [2.688, 0.023, 0.867, 0.191, 3.55, 1.732], [1.91, 1.763, 0.852, 1.655, 0.149, 1.762], [-2.022, 1.78, 0.984, 1.051, 0.126, 1.927]]\nC: [[0.908, -1.987, 1.173, 2.894, 0.341, 1.45], [-1.176, -1.625, 1.254, 2.938, 0.258, 1.218], [-0.734, 1.406, 1.146, 0.597, 0.342, 1.626], [-2.253, 0.34, 1.308, 0.063, 3.579, 1.568], [-0.079, -1.858, 0.689, 0.18, 0.741, 0.85], [2.904, 0.375, 0.691, 0.079, 3.103, 2.186], [1.824, 1.499, 0.728, 1.255, 0.079, 1.787], [-2.124, 1.899, 1.164, 1.019, 0.481, 1.863]]\nD: [[1.462, -1.527, 0.599, 2.871, 0.537, 1.876], [-0.801, -1.454, 1.05, 2.817, -0.258, 1.392], [-0.063, 2.202, 0.566, 0.693, 0.023, 1.708], [-2.869, -0.081, 1.48, 0.816, 3.209, 2.127], [0.07, -1.284, 0.825, -0.242, 0.304, 1.406], [2.798, 0.497, 1.202, 0.386, 3.591, 2.066], [1.458, 2.138, 0.37, 1.504, 0.6, 1.542], [-2.145, 1.824, 0.999, 1.206, 0.504, 1.867]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_190_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_190_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.984594, -0.069457, 0.160469], [-0.174127, -0.305795, 0.936039], [-0.015944, -0.949561, -0.313178]]; the translation vector: [3.941113, 2.817773, 1.559826], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.762, -1.555, 0.827, 2.184, 0.001, 1.66], [-1.543, -2.246, 0.687, 2.166, 0.538, 1.705], [-0.782, 1.874, 0.612, 0.36, -0.393, 2.288], [-2.462, 0.204, 1.234, 0.129, 3.712, 1.462], [-0.383, -1.635, 0.309, 0.432, -0.146, 1.237], [2.291, 0.002, 1.16, 0.467, 3.329, 1.816], [1.638, 1.427, 0.73, 2.104, 0.102, 1.563], [-1.67, 1.91, 0.562, 0.741, 0.507, 1.714]]\nB: [[1.374, -1.714, 0.725, 2.507, 0.169, 1.413], [-1.211, -1.757, 0.826, 2.443, 0.176, 1.694], [-0.519, 1.79, 0.908, 0.294, 0.099, 1.833], [-2.419, 0.035, 0.987, 0.337, 3.555, 1.874], [0.072, -1.69, 0.634, 0.2, 0.284, 1.225], [2.688, 0.023, 0.867, 0.191, 3.55, 1.732], [1.91, 1.763, 0.852, 1.655, 0.149, 1.762], [-2.022, 1.78, 0.984, 1.051, 0.126, 1.927]]\nC: [[0.908, -1.987, 1.173, 2.894, 0.341, 1.45], [-1.176, -1.625, 1.254, 2.938, 0.258, 1.218], [-0.734, 1.406, 1.146, 0.597, 0.342, 1.626], [-2.253, 0.34, 1.308, 0.063, 3.579, 1.568], [-0.079, -1.858, 0.689, 0.18, 0.741, 0.85], [2.904, 0.375, 0.691, 0.079, 3.103, 2.186], [1.824, 1.499, 0.728, 1.255, 0.079, 1.787], [-2.124, 1.899, 1.164, 1.019, 0.481, 1.863]]\nD: [[1.462, -1.527, 0.599, 2.871, 0.537, 1.876], [-0.801, -1.454, 1.05, 2.817, -0.258, 1.392], [-0.063, 2.202, 0.566, 0.693, 0.023, 1.708], [-2.869, -0.081, 1.48, 0.816, 3.209, 2.127], [0.07, -1.284, 0.825, -0.242, 0.304, 1.406], [2.798, 0.497, 1.202, 0.386, 3.591, 2.066], [1.458, 2.138, 0.37, 1.504, 0.6, 1.542], [-2.145, 1.824, 0.999, 1.206, 0.504, 1.867]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.548, -0.723, 1.702, 0.029, 0.56, 0.548], [0.99, -0.353, 1.798, 0.254, 0.406, 1.307], [0.157, -0.505, 0.811, 2.866, -0.08, 2.257], [-1.059, 0.22, 1.187, 0.204, 1.96, 1.609], [-1.515, 1.374, 1.432, 0.39, 0.462, 2.145], [-1.181, 2.329, 0.518, 0.161, 0.641, 1.327], [1.31, 1.368, 1.072, 1.078, 3.27, 1.707]]\nB: [[-0.408, -0.944, 1.285, 0.383, 0.855, 1.329], [1.037, -1.048, 1.113, 0.346, 0.92, 1.337], [0.631, -0.546, 1.683, 2.674, 0.421, 2.371], [-1.406, 0.284, 0.493, 0.426, 1.745, 1.616], [-0.913, 1.243, 0.625, 0.944, -0.159, 1.495], [-0.749, 1.827, 0.664, -0.223, 0.933, 1.793], [1.27, 1.253, 1.221, 0.403, 2.724, 2.094]]\nC: [[-0.454, -0.86, 2.026, 0.451, 0.358, 1.257], [1.65, -0.511, 2.057, 0.183, 0.13, 0.645], [0.357, -0.781, 1.143, 3.085, -0.312, 2.705], [-1.785, 0.873, 0.92, 0.414, 1.805, 1.915], [-0.907, 0.946, 0.648, 1.086, 0.063, 2.046], [-0.884, 1.711, 1.057, -0.048, 0.722, 0.964], [1.337, 0.641, 0.462, 0.296, 3.312, 2.01]]\nD: [[-0.751, -0.786, 1.574, 0.095, 0.444, 1.034], [1.18, -0.773, 1.574, 0.094, 0.433, 1.033], [0.142, -0.562, 1.184, 3.142, 0.116, 2.394], [-1.437, 0.419, 0.848, 0.139, 1.974, 1.688], [-1.083, 1.379, 1.042, 0.807, 0.163, 1.776], [-0.694, 1.837, 0.766, 0.107, 0.954, 1.459], [1.355, 0.889, 0.903, 0.788, 2.903, 1.82]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_191_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_191_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[-0.355681, -0.20797, 0.911175], [-0.934036, 0.113197, -0.338769], [-0.032689, -0.971563, -0.234514]]; the translation vector: [0.539195, 4.841905, 1.636959], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.548, -0.723, 1.702, 0.029, 0.56, 0.548], [0.99, -0.353, 1.798, 0.254, 0.406, 1.307], [0.157, -0.505, 0.811, 2.866, -0.08, 2.257], [-1.059, 0.22, 1.187, 0.204, 1.96, 1.609], [-1.515, 1.374, 1.432, 0.39, 0.462, 2.145], [-1.181, 2.329, 0.518, 0.161, 0.641, 1.327], [1.31, 1.368, 1.072, 1.078, 3.27, 1.707]]\nB: [[-0.408, -0.944, 1.285, 0.383, 0.855, 1.329], [1.037, -1.048, 1.113, 0.346, 0.92, 1.337], [0.631, -0.546, 1.683, 2.674, 0.421, 2.371], [-1.406, 0.284, 0.493, 0.426, 1.745, 1.616], [-0.913, 1.243, 0.625, 0.944, -0.159, 1.495], [-0.749, 1.827, 0.664, -0.223, 0.933, 1.793], [1.27, 1.253, 1.221, 0.403, 2.724, 2.094]]\nC: [[-0.454, -0.86, 2.026, 0.451, 0.358, 1.257], [1.65, -0.511, 2.057, 0.183, 0.13, 0.645], [0.357, -0.781, 1.143, 3.085, -0.312, 2.705], [-1.785, 0.873, 0.92, 0.414, 1.805, 1.915], [-0.907, 0.946, 0.648, 1.086, 0.063, 2.046], [-0.884, 1.711, 1.057, -0.048, 0.722, 0.964], [1.337, 0.641, 0.462, 0.296, 3.312, 2.01]]\nD: [[-0.751, -0.786, 1.574, 0.095, 0.444, 1.034], [1.18, -0.773, 1.574, 0.094, 0.433, 1.033], [0.142, -0.562, 1.184, 3.142, 0.116, 2.394], [-1.437, 0.419, 0.848, 0.139, 1.974, 1.688], [-1.083, 1.379, 1.042, 0.807, 0.163, 1.776], [-0.694, 1.837, 0.766, 0.107, 0.954, 1.459], [1.355, 0.889, 0.903, 0.788, 2.903, 1.82]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.515, -3.241, 1.128, 2.444, 0.863, 2.298], [1.577, 0.871, 1.235, 2.218, 0.709, 2.09], [1.099, 3.677, 1.424, 1.498, 0.813, 2.316], [1.686, -0.521, 1.08, 2.449, 0.774, 1.941], [1.48, 2.234, 1.312, 2.224, 0.696, 2.161], [0.71, 4.953, 0.833, 0.669, 0.644, 1.154], [1.678, -1.888, 1.095, 2.523, 0.759, 2.102]]\nB: [[1.269, -3.321, 1.076, 2.021, 0.959, 2.397], [1.664, 1.284, 1.204, 2.329, 1.065, 2.182], [1.189, 3.832, 1.394, 1.94, 1.033, 1.829], [2.066, -0.941, 0.589, 2.315, 1.169, 1.455], [1.915, 2.253, 1.321, 2.418, 0.57, 2.378], [0.213, 5.41, 0.898, 0.409, 1.093, 1.517], [1.55, -2.082, 1.024, 2.82, 0.884, 2.344]]\nC: [[1.118, -3.575, 0.993, 1.946, 0.682, 2.318], [1.412, 0.928, 1.006, 2.495, 0.73, 2.187], [0.774, 3.36, 0.968, 1.482, 0.922, 2.574], [1.295, -0.734, 1.167, 2.189, 0.383, 1.587], [1.325, 2.548, 0.999, 2.413, 1.015, 2.532], [0.98, 5.017, 0.875, 0.448, 0.455, 0.917], [2.018, -1.5, 1.046, 2.717, 0.819, 2.55]]\nD: [[1.259, -3.521, 1.143, 2.894, 0.867, 2.663], [1.362, 1.016, 1.431, 2.314, 0.878, 2.2], [0.748, 3.481, 1.025, 1.495, 1.271, 2.75], [1.85, -0.752, 1.348, 2.468, 0.657, 1.566], [1.513, 2.006, 1.345, 1.751, 0.827, 2.159], [0.635, 4.802, 1.263, 0.202, 1.111, 1.501], [1.353, -2.331, 1.563, 2.89, 1.228, 2.108]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_192_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_192_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the bookshelf in the scene. The camera pose information includes: the rotation matrix: [[-0.941243, -0.209403, 0.264975], [-0.336113, 0.504116, -0.795548], [0.033012, -0.837865, -0.544878]]; the translation vector: [4.828751, 9.008894, 1.463441], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.515, -3.241, 1.128, 2.444, 0.863, 2.298], [1.577, 0.871, 1.235, 2.218, 0.709, 2.09], [1.099, 3.677, 1.424, 1.498, 0.813, 2.316], [1.686, -0.521, 1.08, 2.449, 0.774, 1.941], [1.48, 2.234, 1.312, 2.224, 0.696, 2.161], [0.71, 4.953, 0.833, 0.669, 0.644, 1.154], [1.678, -1.888, 1.095, 2.523, 0.759, 2.102]]\nB: [[1.269, -3.321, 1.076, 2.021, 0.959, 2.397], [1.664, 1.284, 1.204, 2.329, 1.065, 2.182], [1.189, 3.832, 1.394, 1.94, 1.033, 1.829], [2.066, -0.941, 0.589, 2.315, 1.169, 1.455], [1.915, 2.253, 1.321, 2.418, 0.57, 2.378], [0.213, 5.41, 0.898, 0.409, 1.093, 1.517], [1.55, -2.082, 1.024, 2.82, 0.884, 2.344]]\nC: [[1.118, -3.575, 0.993, 1.946, 0.682, 2.318], [1.412, 0.928, 1.006, 2.495, 0.73, 2.187], [0.774, 3.36, 0.968, 1.482, 0.922, 2.574], [1.295, -0.734, 1.167, 2.189, 0.383, 1.587], [1.325, 2.548, 0.999, 2.413, 1.015, 2.532], [0.98, 5.017, 0.875, 0.448, 0.455, 0.917], [2.018, -1.5, 1.046, 2.717, 0.819, 2.55]]\nD: [[1.259, -3.521, 1.143, 2.894, 0.867, 2.663], [1.362, 1.016, 1.431, 2.314, 0.878, 2.2], [0.748, 3.481, 1.025, 1.495, 1.271, 2.75], [1.85, -0.752, 1.348, 2.468, 0.657, 1.566], [1.513, 2.006, 1.345, 1.751, 0.827, 2.159], [0.635, 4.802, 1.263, 0.202, 1.111, 1.501], [1.353, -2.331, 1.563, 2.89, 1.228, 2.108]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.126, -0.51, 1.73, -0.359, 0.479, 0.324]]\nB: [[-1.548, -0.135, 1.59, 0.021, 0.457, 0.386]]\nC: [[-1.508, -0.035, 1.589, -0.436, 0.071, 0.171]]\nD: [[-1.888, -0.563, 1.28, -0.393, 0.688, 0.046]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_193_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_193_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the picture in the scene. The camera pose information includes: the rotation matrix: [[0.623567, 0.536294, -0.568817], [0.781209, -0.455034, 0.427384], [-0.029628, -0.710867, -0.702702]]; the translation vector: [1.790477, 1.816361, 1.229059], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.126, -0.51, 1.73, -0.359, 0.479, 0.324]]\nB: [[-1.548, -0.135, 1.59, 0.021, 0.457, 0.386]]\nC: [[-1.508, -0.035, 1.589, -0.436, 0.071, 0.171]]\nD: [[-1.888, -0.563, 1.28, -0.393, 0.688, 0.046]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-2.08, 0.154, 1.005, 0.283, 1.414, 1.731]]\nB: [[-1.974, 0.286, 1.416, 0.341, 1.457, 1.235]]\nC: [[-1.941, 0.29, 1.24, -0.098, 1.307, 1.381]]\nD: [[-1.581, 0.374, 0.521, 0.311, 1.136, 1.526]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_194_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_194_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the whiteboard in the scene. The camera pose information includes: the rotation matrix: [[-0.341382, 0.594812, -0.727775], [0.932196, 0.11517, -0.343142], [-0.120287, -0.795572, -0.593798]]; the translation vector: [7.151203, 3.587152, 1.581923], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-2.08, 0.154, 1.005, 0.283, 1.414, 1.731]]\nB: [[-1.974, 0.286, 1.416, 0.341, 1.457, 1.235]]\nC: [[-1.941, 0.29, 1.24, -0.098, 1.307, 1.381]]\nD: [[-1.581, 0.374, 0.521, 0.311, 1.136, 1.526]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-0.65, 1.626, 0.952, 1.426, 0.125, 1.867]]\nB: [[-0.34, 1.647, 1.105, 1.036, 0.294, 2.092]]\nC: [[-0.202, 1.219, 1.248, 1.308, -0.28, 1.829]]\nD: [[-1.114, 1.711, 0.518, 0.996, 0.291, 2.172]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_195_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_195_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the doorframe in the scene. The camera pose information includes: the rotation matrix: [[-0.40936, -0.486807, 0.77165], [-0.912164, 0.236459, -0.334729], [-0.019515, -0.840896, -0.540844]]; the translation vector: [1.412713, 1.214489, 1.390939], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-0.65, 1.626, 0.952, 1.426, 0.125, 1.867]]\nB: [[-0.34, 1.647, 1.105, 1.036, 0.294, 2.092]]\nC: [[-0.202, 1.219, 1.248, 1.308, -0.28, 1.829]]\nD: [[-1.114, 1.711, 0.518, 0.996, 0.291, 2.172]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[-1.253, 0.185, 0.949, 0.229, 3.97, 1.922], [-0.174, 1.794, 1.044, 2.121, 0.218, 2.116], [0.873, -0.38, 1.281, 0.158, 4.352, 2.497], [0.476, -2.537, 0.593, 0.677, 0.042, 1.129], [0.122, -2.616, 0.312, 0.063, 0.188, 0.596]]\nB: [[-1.703, -0.184, 0.581, -0.118, 3.494, 2.053], [0.248, 1.316, 1.102, 2.022, 0.319, 1.655], [1.35, -0.101, 1.108, 0.315, 4.473, 2.489], [0.441, -2.72, 0.688, 0.321, 0.469, 1.1], [-0.308, -2.248, -0.131, 0.362, 0.498, 0.335]]\nC: [[-1.001, 0.387, 0.855, 0.13, 4.223, 1.808], [-0.121, 2.25, 1.058, 2.216, 0.377, 2.185], [0.489, 0.025, 0.85, -0.341, 3.971, 2.77], [0.668, -2.895, 0.381, 0.972, 0.18, 1.122], [0.223, -2.648, 0.118, -0.29, 0.288, 0.814]]\nD: [[-1.615, 0.237, 0.631, 0.113, 3.734, 2.164], [-0.111, 1.6, 1.257, 2.2, 0.658, 1.704], [0.468, -0.376, 0.97, -0.134, 3.943, 2.668], [0.083, -2.476, 0.49, 0.836, 0.329, 1.629], [-0.101, -2.949, 0.022, 0.48, 0.426, 0.711]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_196_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_196_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the wall in the scene. The camera pose information includes: the rotation matrix: [[0.977514, -0.102294, 0.184398], [-0.210796, -0.497303, 0.841578], [0.005613, -0.861525, -0.507684]]; the translation vector: [3.555602, 1.207732, 1.356493], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[-1.253, 0.185, 0.949, 0.229, 3.97, 1.922], [-0.174, 1.794, 1.044, 2.121, 0.218, 2.116], [0.873, -0.38, 1.281, 0.158, 4.352, 2.497], [0.476, -2.537, 0.593, 0.677, 0.042, 1.129], [0.122, -2.616, 0.312, 0.063, 0.188, 0.596]]\nB: [[-1.703, -0.184, 0.581, -0.118, 3.494, 2.053], [0.248, 1.316, 1.102, 2.022, 0.319, 1.655], [1.35, -0.101, 1.108, 0.315, 4.473, 2.489], [0.441, -2.72, 0.688, 0.321, 0.469, 1.1], [-0.308, -2.248, -0.131, 0.362, 0.498, 0.335]]\nC: [[-1.001, 0.387, 0.855, 0.13, 4.223, 1.808], [-0.121, 2.25, 1.058, 2.216, 0.377, 2.185], [0.489, 0.025, 0.85, -0.341, 3.971, 2.77], [0.668, -2.895, 0.381, 0.972, 0.18, 1.122], [0.223, -2.648, 0.118, -0.29, 0.288, 0.814]]\nD: [[-1.615, 0.237, 0.631, 0.113, 3.734, 2.164], [-0.111, 1.6, 1.257, 2.2, 0.658, 1.704], [0.468, -0.376, 0.97, -0.134, 3.943, 2.668], [0.083, -2.476, 0.49, 0.836, 0.329, 1.629], [-0.101, -2.949, 0.022, 0.48, 0.426, 0.711]]"}, "output": {"output_text": "A"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[1.561, -0.516, 0.43, 0.03, 0.264, 0.023]]\nB: [[1.307, -0.077, 0.927, 0.18, 0.373, 0.438]]\nC: [[1.232, 0.339, 1.368, -0.266, 0.794, 0.386]]\nD: [[1.366, 0.134, 0.662, 0.477, 0.375, 0.57]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_197_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_197_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the toilet paper holder in the scene. The camera pose information includes: the rotation matrix: [[-0.566304, -0.590941, 0.574533], [-0.823945, 0.423135, -0.376925], [-0.020365, -0.686838, -0.726526]]; the translation vector: [2.143516, 1.760119, 1.343188], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[1.561, -0.516, 0.43, 0.03, 0.264, 0.023]]\nB: [[1.307, -0.077, 0.927, 0.18, 0.373, 0.438]]\nC: [[1.232, 0.339, 1.368, -0.266, 0.794, 0.386]]\nD: [[1.366, 0.134, 0.662, 0.477, 0.375, 0.57]]"}, "output": {"output_text": "B"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.989, -2.867, 2.62, 3.827, 5.879, 0.215]]\nB: [[0.557, -2.629, 2.447, 3.868, 5.161, -0.064]]\nC: [[0.767, -2.57, 3.32, 4.124, 4.999, -0.179]]\nD: [[0.538, -2.391, 2.899, 4.263, 5.407, 0.187]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_198_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_198_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the ceiling in the scene. The camera pose information includes: the rotation matrix: [[-0.999494, 0.005595, 0.031322], [-0.029883, 0.172936, -0.98448], [-0.010925, -0.984917, -0.172681]]; the translation vector: [6.687301, 5.436423, 1.742894], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.989, -2.867, 2.62, 3.827, 5.879, 0.215]]\nB: [[0.557, -2.629, 2.447, 3.868, 5.161, -0.064]]\nC: [[0.767, -2.57, 3.32, 4.124, 4.999, -0.179]]\nD: [[0.538, -2.391, 2.899, 4.263, 5.407, 0.187]]"}, "output": {"output_text": "D"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_bbox_detection", "options": "A: [[0.728, -0.216, 1.391, -0.233, 0.319, 0.888]]\nB: [[1.382, -0.434, 1.41, 0.62, 0.036, 0.847]]\nC: [[1.017, -0.314, 0.963, 0.261, 0.326, 0.441]]\nD: [[1.373, -0.033, 0.749, 0.246, 0.609, 0.097]]", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Object_Detection/threeD_Object_Detection_199_0.jpg", "3D-spatial/threeD_Object_Detection/threeD_Object_Detection_199_1.png"], "question": "Given a RGB image and a depth image, please detect the 3D bounding box of the paper towel dispenser in the scene. The camera pose information includes: the rotation matrix: [[0.207705, 0.494542, -0.843971], [0.97739, -0.069996, 0.199524], [0.039599, -0.866331, -0.497898]]; the translation vector: [4.53083, 2.291093, 1.52739], representing the transformation from the camera coordinate system to the world coordinate system. For each detected object, provide the output in this format, i.e., [x, y, z, x_size, y_size, z_size]. Here, [x, y, z] represents the gravity center of the 3D bounding boxes in the world coordinate system, [x_size, y_size, z_size] represents the width, height, and length of the 3D bounding box.", "context": "Your task is to detect objects in 3D space using a scan of RGB-Depth image pair. \nSelect from the following choices.\nA: [[0.728, -0.216, 1.391, -0.233, 0.319, 0.888]]\nB: [[1.382, -0.434, 1.41, 0.62, 0.036, 0.847]]\nC: [[1.017, -0.314, 0.963, 0.261, 0.326, 0.441]]\nD: [[1.373, -0.033, 0.749, 0.246, 0.609, 0.097]]"}, "output": {"output_text": "C"}, "task": "threeD_Object_Detection"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999860986788529, -0.0029206102888043426, 0.004512150995140729], [0.0029125077264400548, 0.9999942525374551, 0.0017791916930172049], [-0.004515929331115238, -0.0017660484162836249, 0.9999884136529374]], 'translation vector': [0.00035121537956284143, -0.00211204147587285, 0.0015269971399166637]}\nB: {'rotation matrix': [[0.992252, 0.033516, -0.119639], [0.120006, -0.507929, 0.852999], [-0.032179, -0.860747, -0.508015]], 'translation vector': [2.483829, 1.386735, 1.351847]}\nC: {'rotation matrix': [[0.992393, 0.03365, -0.118424], [0.118928, -0.510671, 0.851511], [-0.031822, -0.859118, -0.510788]], 'translation vector': [2.483625, 1.389348, 1.348027]}\nD: {'rotation matrix': [[0.992358, 0.033913, -0.118638], [0.11923, -0.511103, 0.85121], [-0.031769, -0.85885, -0.511241]], 'translation vector': [2.484339, 1.38954, 1.351903]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_0_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_0_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_0_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_0_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999860986788529, -0.0029206102888043426, 0.004512150995140729], [0.0029125077264400548, 0.9999942525374551, 0.0017791916930172049], [-0.004515929331115238, -0.0017660484162836249, 0.9999884136529374]], 'translation vector': [0.00035121537956284143, -0.00211204147587285, 0.0015269971399166637]}\nB: {'rotation matrix': [[0.992252, 0.033516, -0.119639], [0.120006, -0.507929, 0.852999], [-0.032179, -0.860747, -0.508015]], 'translation vector': [2.483829, 1.386735, 1.351847]}\nC: {'rotation matrix': [[0.992393, 0.03365, -0.118424], [0.118928, -0.510671, 0.851511], [-0.031822, -0.859118, -0.510788]], 'translation vector': [2.483625, 1.389348, 1.348027]}\nD: {'rotation matrix': [[0.992358, 0.033913, -0.118638], [0.11923, -0.511103, 0.85121], [-0.031769, -0.85885, -0.511241]], 'translation vector': [2.484339, 1.38954, 1.351903]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.947387, 0.126025, -0.294239], [0.319939, 0.401221, -0.858289], [0.009889, -0.90727, -0.420431]], 'translation vector': [2.649368, 2.97856, 1.365403]}\nB: {'rotation matrix': [[0.9999009689053274, -0.0005834477726320058, -0.014081260252404066], [0.0005185811040898338, 0.9999892566550749, -0.004650674323837413], [0.014082812752834694, 0.004642499383147887, 0.9998897887510193]], 'translation vector': [-0.0001697198246333187, -0.006057464737030116, -0.0030857621071840313]}\nC: {'rotation matrix': [[-0.946914, 0.131611, -0.293313], [0.321456, 0.400409, -0.858102], [0.004509, -0.906836, -0.42146]], 'translation vector': [2.644349, 2.98006, 1.361572]}\nD: {'rotation matrix': [[-0.946851, 0.128282, -0.294988], [0.321573, 0.400396, -0.858064], [0.008037, -0.907318, -0.420367]], 'translation vector': [2.647634, 2.978188, 1.36466]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_1_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_1_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_1_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_1_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.947387, 0.126025, -0.294239], [0.319939, 0.401221, -0.858289], [0.009889, -0.90727, -0.420431]], 'translation vector': [2.649368, 2.97856, 1.365403]}\nB: {'rotation matrix': [[0.9999009689053274, -0.0005834477726320058, -0.014081260252404066], [0.0005185811040898338, 0.9999892566550749, -0.004650674323837413], [0.014082812752834694, 0.004642499383147887, 0.9998897887510193]], 'translation vector': [-0.0001697198246333187, -0.006057464737030116, -0.0030857621071840313]}\nC: {'rotation matrix': [[-0.946914, 0.131611, -0.293313], [0.321456, 0.400409, -0.858102], [0.004509, -0.906836, -0.42146]], 'translation vector': [2.644349, 2.98006, 1.361572]}\nD: {'rotation matrix': [[-0.946851, 0.128282, -0.294988], [0.321573, 0.400396, -0.858064], [0.008037, -0.907318, -0.420367]], 'translation vector': [2.647634, 2.978188, 1.36466]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.932535, 0.164547, -0.321407], [0.359784, -0.498771, 0.788533], [-0.030558, -0.850971, -0.524323]], 'translation vector': [4.48804, -0.229774, 1.538571]}\nB: {'rotation matrix': [[0.999974189934778, 0.00023654440835110308, 0.007205305592304459], [-0.00021900350048315587, 0.9999974076812913, -0.002394096534537237], [-0.00720576870446999, 0.0023924811533001236, 0.9999711271279352]], 'translation vector': [-0.011672688463027825, -0.012243066982587869, 0.0020668703552249035]}\nC: {'rotation matrix': [[0.930699, 0.167887, -0.324983], [0.364431, -0.502007, 0.784334], [-0.031464, -0.848412, -0.5284]], 'translation vector': [4.497419, -0.228559, 1.538943]}\nD: {'rotation matrix': [[0.928253, 0.171766, -0.329913], [0.370592, -0.502789, 0.780939], [-0.031738, -0.847172, -0.53037]], 'translation vector': [4.506209, -0.230888, 1.537021]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_2_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_2_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_2_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_2_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.932535, 0.164547, -0.321407], [0.359784, -0.498771, 0.788533], [-0.030558, -0.850971, -0.524323]], 'translation vector': [4.48804, -0.229774, 1.538571]}\nB: {'rotation matrix': [[0.999974189934778, 0.00023654440835110308, 0.007205305592304459], [-0.00021900350048315587, 0.9999974076812913, -0.002394096534537237], [-0.00720576870446999, 0.0023924811533001236, 0.9999711271279352]], 'translation vector': [-0.011672688463027825, -0.012243066982587869, 0.0020668703552249035]}\nC: {'rotation matrix': [[0.930699, 0.167887, -0.324983], [0.364431, -0.502007, 0.784334], [-0.031464, -0.848412, -0.5284]], 'translation vector': [4.497419, -0.228559, 1.538943]}\nD: {'rotation matrix': [[0.928253, 0.171766, -0.329913], [0.370592, -0.502789, 0.780939], [-0.031738, -0.847172, -0.53037]], 'translation vector': [4.506209, -0.230888, 1.537021]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999922186975153, -0.0004929414354074684, -0.003990843308659959], [0.0004944153894448021, 1.0000000788246688, 0.0002887878542439764], [0.0039912978026401735, -0.00029052347125931846, 0.9999922538344134]], 'translation vector': [0.0015230291799757656, -0.0023232322897525567, 0.004482115182110835]}\nB: {'rotation matrix': [[-0.597501, 0.375338, -0.7086], [0.801649, 0.25893, -0.538808], [-0.018758, -0.889987, -0.4556]], 'translation vector': [2.357092, 1.421442, 1.358509]}\nC: {'rotation matrix': [[-0.595396, 0.37569, -0.710183], [0.803242, 0.259116, -0.536341], [-0.017478, -0.889784, -0.456047]], 'translation vector': [2.35612, 1.420569, 1.361782]}\nD: {'rotation matrix': [[-0.600812, 0.375021, -0.705963], [0.799114, 0.258529, -0.542752], [-0.021031, -0.890237, -0.455012]], 'translation vector': [2.356618, 1.42274, 1.357666]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_3_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_3_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_3_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_3_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999922186975153, -0.0004929414354074684, -0.003990843308659959], [0.0004944153894448021, 1.0000000788246688, 0.0002887878542439764], [0.0039912978026401735, -0.00029052347125931846, 0.9999922538344134]], 'translation vector': [0.0015230291799757656, -0.0023232322897525567, 0.004482115182110835]}\nB: {'rotation matrix': [[-0.597501, 0.375338, -0.7086], [0.801649, 0.25893, -0.538808], [-0.018758, -0.889987, -0.4556]], 'translation vector': [2.357092, 1.421442, 1.358509]}\nC: {'rotation matrix': [[-0.595396, 0.37569, -0.710183], [0.803242, 0.259116, -0.536341], [-0.017478, -0.889784, -0.456047]], 'translation vector': [2.35612, 1.420569, 1.361782]}\nD: {'rotation matrix': [[-0.600812, 0.375021, -0.705963], [0.799114, 0.258529, -0.542752], [-0.021031, -0.890237, -0.455012]], 'translation vector': [2.356618, 1.42274, 1.357666]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.690346, 0.288159, -0.663616], [0.723477, -0.272947, 0.634098], [0.001589, -0.917858, -0.396905]], 'translation vector': [2.536332, 2.010734, 1.438743]}\nB: {'rotation matrix': [[0.691208, 0.288183, -0.662708], [0.722652, -0.27257, 0.635201], [0.00242, -0.917963, -0.396658]], 'translation vector': [2.535653, 2.009964, 1.439474]}\nC: {'rotation matrix': [[0.9999995587474457, 0.00024022036499647738, 0.0007957711644081506], [-0.00023933007530568237, 0.9999998633783928, -0.0007108069241564525], [-0.0007964539455043593, 0.0007109455644655081, 1.000000560983507]], 'translation vector': [-0.004770728985455719, 0.002959587174171885, 0.0013885111462622612]}\nD: {'rotation matrix': [[0.690426, 0.287793, -0.663692], [0.723401, -0.272862, 0.634222], [0.001429, -0.917999, -0.396581]], 'translation vector': [2.53477, 2.009069, 1.43814]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_4_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_4_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_4_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_4_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.690346, 0.288159, -0.663616], [0.723477, -0.272947, 0.634098], [0.001589, -0.917858, -0.396905]], 'translation vector': [2.536332, 2.010734, 1.438743]}\nB: {'rotation matrix': [[0.691208, 0.288183, -0.662708], [0.722652, -0.27257, 0.635201], [0.00242, -0.917963, -0.396658]], 'translation vector': [2.535653, 2.009964, 1.439474]}\nC: {'rotation matrix': [[0.9999995587474457, 0.00024022036499647738, 0.0007957711644081506], [-0.00023933007530568237, 0.9999998633783928, -0.0007108069241564525], [-0.0007964539455043593, 0.0007109455644655081, 1.000000560983507]], 'translation vector': [-0.004770728985455719, 0.002959587174171885, 0.0013885111462622612]}\nD: {'rotation matrix': [[0.690426, 0.287793, -0.663692], [0.723401, -0.272862, 0.634222], [0.001429, -0.917999, -0.396581]], 'translation vector': [2.53477, 2.009069, 1.43814]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999658530200795, -0.008162385048605073, -0.000989578606081076], [0.008166400906506959, 0.9999603758313507, 0.0035456278045154508], [0.000960099553273771, -0.0035536229934212678, 0.9999930725472598]], 'translation vector': [0.0005115289741049189, -0.00032414464705918244, 0.0017902118924140176]}\nB: {'rotation matrix': [[-0.221487, 0.417059, -0.881479], [0.974313, 0.13239, -0.182174], [0.040721, -0.899186, -0.435668]], 'translation vector': [3.156802, 0.483491, 1.355875]}\nC: {'rotation matrix': [[-0.223193, 0.415497, -0.881786], [0.973999, 0.131126, -0.184746], [0.038864, -0.900094, -0.43396]], 'translation vector': [3.157208, 0.483314, 1.355186]}\nD: {'rotation matrix': [[-0.22378, 0.416079, -0.881363], [0.973939, 0.129755, -0.18603], [0.036958, -0.900023, -0.434273]], 'translation vector': [3.157156, 0.483591, 1.355072]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_5_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_5_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_5_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_5_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999658530200795, -0.008162385048605073, -0.000989578606081076], [0.008166400906506959, 0.9999603758313507, 0.0035456278045154508], [0.000960099553273771, -0.0035536229934212678, 0.9999930725472598]], 'translation vector': [0.0005115289741049189, -0.00032414464705918244, 0.0017902118924140176]}\nB: {'rotation matrix': [[-0.221487, 0.417059, -0.881479], [0.974313, 0.13239, -0.182174], [0.040721, -0.899186, -0.435668]], 'translation vector': [3.156802, 0.483491, 1.355875]}\nC: {'rotation matrix': [[-0.223193, 0.415497, -0.881786], [0.973999, 0.131126, -0.184746], [0.038864, -0.900094, -0.43396]], 'translation vector': [3.157208, 0.483314, 1.355186]}\nD: {'rotation matrix': [[-0.22378, 0.416079, -0.881363], [0.973939, 0.129755, -0.18603], [0.036958, -0.900023, -0.434273]], 'translation vector': [3.157156, 0.483591, 1.355072]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.294979, -0.395497, 0.869811], [-0.955406, 0.135138, -0.26256], [-0.013703, -0.908472, -0.417722]], 'translation vector': [4.231627, 1.757554, 1.314948]}\nB: {'rotation matrix': [[0.9999859365705687, 3.1558862291102445e-05, 0.005361241460385626], [2.1367515200134227e-05, 0.9999509201368397, -0.009913096398898577], [-0.0053616455101250845, 0.009912181817668633, 0.9999368747200875]], 'translation vector': [-0.00185453480108011, 0.004425119632380792, 0.004740673653586214]}\nC: {'rotation matrix': [[-0.295231, -0.385219, 0.874325], [-0.955253, 0.136423, -0.262452], [-0.018176, -0.912686, -0.408258]], 'translation vector': [4.225714, 1.76129, 1.315325]}\nD: {'rotation matrix': [[-0.297898, -0.402478, 0.865603], [-0.954572, 0.132313, -0.266996], [-0.007071, -0.905817, -0.42361]], 'translation vector': [4.239912, 1.761582, 1.310375]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_6_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_6_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_6_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_6_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.294979, -0.395497, 0.869811], [-0.955406, 0.135138, -0.26256], [-0.013703, -0.908472, -0.417722]], 'translation vector': [4.231627, 1.757554, 1.314948]}\nB: {'rotation matrix': [[0.9999859365705687, 3.1558862291102445e-05, 0.005361241460385626], [2.1367515200134227e-05, 0.9999509201368397, -0.009913096398898577], [-0.0053616455101250845, 0.009912181817668633, 0.9999368747200875]], 'translation vector': [-0.00185453480108011, 0.004425119632380792, 0.004740673653586214]}\nC: {'rotation matrix': [[-0.295231, -0.385219, 0.874325], [-0.955253, 0.136423, -0.262452], [-0.018176, -0.912686, -0.408258]], 'translation vector': [4.225714, 1.76129, 1.315325]}\nD: {'rotation matrix': [[-0.297898, -0.402478, 0.865603], [-0.954572, 0.132313, -0.266996], [-0.007071, -0.905817, -0.42361]], 'translation vector': [4.239912, 1.761582, 1.310375]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.43634, -0.426945, 0.792039], [-0.899692, 0.219464, -0.377346], [-0.012718, -0.877242, -0.47988]], 'translation vector': [1.991026, 3.721216, 1.553809]}\nB: {'rotation matrix': [[0.9999994163271791, 0.0007055386419518741, -5.183687097418444e-05], [-0.0007048159396394524, 1.0000001700794185, 0.00017964949547958028], [5.299300853873026e-05, -0.00017925701598013347, 1.000000048079148]], 'translation vector': [-0.0002435455336966541, -0.00047987538941862695, 0.0009530826592798469]}\nC: {'rotation matrix': [[-0.436198, -0.427205, 0.791977], [-0.899763, 0.219364, -0.377235], [-0.012574, -0.877141, -0.480069]], 'translation vector': [1.990491, 3.720783, 1.55354]}\nD: {'rotation matrix': [[-0.436159, -0.427335, 0.791928], [-0.899792, 0.218686, -0.377559], [-0.011839, -0.877246, -0.479894]], 'translation vector': [1.98993, 3.720837, 1.552023]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_7_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_7_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_7_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_7_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.43634, -0.426945, 0.792039], [-0.899692, 0.219464, -0.377346], [-0.012718, -0.877242, -0.47988]], 'translation vector': [1.991026, 3.721216, 1.553809]}\nB: {'rotation matrix': [[0.9999994163271791, 0.0007055386419518741, -5.183687097418444e-05], [-0.0007048159396394524, 1.0000001700794185, 0.00017964949547958028], [5.299300853873026e-05, -0.00017925701598013347, 1.000000048079148]], 'translation vector': [-0.0002435455336966541, -0.00047987538941862695, 0.0009530826592798469]}\nC: {'rotation matrix': [[-0.436198, -0.427205, 0.791977], [-0.899763, 0.219364, -0.377235], [-0.012574, -0.877141, -0.480069]], 'translation vector': [1.990491, 3.720783, 1.55354]}\nD: {'rotation matrix': [[-0.436159, -0.427335, 0.791928], [-0.899792, 0.218686, -0.377559], [-0.011839, -0.877246, -0.479894]], 'translation vector': [1.98993, 3.720837, 1.552023]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999987261495074, 0.0004975354064711766, -0.0014612465343166485], [-0.0004956714882147159, 0.9999976114101617, 0.002168145916521647], [0.0014629580715452147, -0.002166812264256858, 0.9999963843096157]], 'translation vector': [-0.0006911004437073487, 0.0010681685362672333, 0.00045340774962232544]}\nB: {'rotation matrix': [[0.254029, -0.222698, 0.941209], [-0.965413, 0.000689, 0.260725], [-0.058712, -0.974887, -0.21482]], 'translation vector': [0.927676, 4.785758, 1.499229]}\nC: {'rotation matrix': [[0.261058, -0.219751, 0.939978], [-0.963311, 0.003543, 0.268366], [-0.062304, -0.97555, -0.210763]], 'translation vector': [0.925951, 4.784105, 1.497862]}\nD: {'rotation matrix': [[0.253006, -0.222602, 0.941507], [-0.965684, 0.00092, 0.259721], [-0.058681, -0.974909, -0.21473]], 'translation vector': [0.928139, 4.78494, 1.499076]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_8_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_8_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_8_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_8_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999987261495074, 0.0004975354064711766, -0.0014612465343166485], [-0.0004956714882147159, 0.9999976114101617, 0.002168145916521647], [0.0014629580715452147, -0.002166812264256858, 0.9999963843096157]], 'translation vector': [-0.0006911004437073487, 0.0010681685362672333, 0.00045340774962232544]}\nB: {'rotation matrix': [[0.254029, -0.222698, 0.941209], [-0.965413, 0.000689, 0.260725], [-0.058712, -0.974887, -0.21482]], 'translation vector': [0.927676, 4.785758, 1.499229]}\nC: {'rotation matrix': [[0.261058, -0.219751, 0.939978], [-0.963311, 0.003543, 0.268366], [-0.062304, -0.97555, -0.210763]], 'translation vector': [0.925951, 4.784105, 1.497862]}\nD: {'rotation matrix': [[0.253006, -0.222602, 0.941507], [-0.965684, 0.00092, 0.259721], [-0.058681, -0.974909, -0.21473]], 'translation vector': [0.928139, 4.78494, 1.499076]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.298773, 0.351612, -0.887189], [0.953749, -0.077747, 0.290375], [0.033123, -0.932912, -0.358578]], 'translation vector': [3.912279, 4.982921, 1.420651]}\nB: {'rotation matrix': [[0.29932, 0.353357, -0.88631], [0.953697, -0.082092, 0.289349], [0.029485, -0.93188, -0.361567]], 'translation vector': [3.9112, 4.98563, 1.419169]}\nC: {'rotation matrix': [[0.999988451223679, 0.004467367701975065, -0.0013038134525021694], [-0.00445692043483639, 0.9999572940857223, 0.008125240965736606], [0.0013398420675200973, -0.008118916964061989, 0.9999668442244075]], 'translation vector': [0.0032416245875248606, 0.010404768814489485, 0.0002686970709979697]}\nD: {'rotation matrix': [[0.298213, 0.352721, -0.886937], [0.953989, -0.07977, 0.289034], [0.031197, -0.932323, -0.36028]], 'translation vector': [3.912466, 4.985029, 1.419803]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_9_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_9_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_9_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_9_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.298773, 0.351612, -0.887189], [0.953749, -0.077747, 0.290375], [0.033123, -0.932912, -0.358578]], 'translation vector': [3.912279, 4.982921, 1.420651]}\nB: {'rotation matrix': [[0.29932, 0.353357, -0.88631], [0.953697, -0.082092, 0.289349], [0.029485, -0.93188, -0.361567]], 'translation vector': [3.9112, 4.98563, 1.419169]}\nC: {'rotation matrix': [[0.999988451223679, 0.004467367701975065, -0.0013038134525021694], [-0.00445692043483639, 0.9999572940857223, 0.008125240965736606], [0.0013398420675200973, -0.008118916964061989, 0.9999668442244075]], 'translation vector': [0.0032416245875248606, 0.010404768814489485, 0.0002686970709979697]}\nD: {'rotation matrix': [[0.298213, 0.352721, -0.886937], [0.953989, -0.07977, 0.289034], [0.031197, -0.932323, -0.36028]], 'translation vector': [3.912466, 4.985029, 1.419803]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999651962705811, 0.003066448737415877, 0.007690547235694275], [-0.003089438182273661, 0.9999915450229314, 0.002959839315488226], [-0.007681501308992471, -0.0029840343790842917, 0.999966014383214]], 'translation vector': [-0.009845168086086709, -0.005623772397939042, 0.0006148134083248102]}\nB: {'rotation matrix': [[-0.998744, -0.022866, -0.044595], [0.034706, 0.326335, -0.944617], [0.036152, -0.944977, -0.325132]], 'translation vector': [2.332638, 2.988529, 1.390534]}\nC: {'rotation matrix': [[-0.998733, -0.022769, -0.044885], [0.035006, 0.326505, -0.944547], [0.036161, -0.944921, -0.325294]], 'translation vector': [2.335994, 2.987912, 1.391848]}\nD: {'rotation matrix': [[-0.998702, -0.02238, -0.045764], [0.035975, 0.326219, -0.94461], [0.03607, -0.945029, -0.32499]], 'translation vector': [2.340556, 2.987934, 1.391904]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_10_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_10_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_10_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_10_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999651962705811, 0.003066448737415877, 0.007690547235694275], [-0.003089438182273661, 0.9999915450229314, 0.002959839315488226], [-0.007681501308992471, -0.0029840343790842917, 0.999966014383214]], 'translation vector': [-0.009845168086086709, -0.005623772397939042, 0.0006148134083248102]}\nB: {'rotation matrix': [[-0.998744, -0.022866, -0.044595], [0.034706, 0.326335, -0.944617], [0.036152, -0.944977, -0.325132]], 'translation vector': [2.332638, 2.988529, 1.390534]}\nC: {'rotation matrix': [[-0.998733, -0.022769, -0.044885], [0.035006, 0.326505, -0.944547], [0.036161, -0.944921, -0.325294]], 'translation vector': [2.335994, 2.987912, 1.391848]}\nD: {'rotation matrix': [[-0.998702, -0.02238, -0.045764], [0.035975, 0.326219, -0.94461], [0.03607, -0.945029, -0.32499]], 'translation vector': [2.340556, 2.987934, 1.391904]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.088289, -0.769037, 0.633078], [-0.992448, -0.013575, 0.121917], [-0.085165, -0.63906, -0.764427]], 'translation vector': [1.06143, 1.251586, 2.183495]}\nB: {'rotation matrix': [[0.095281, -0.770575, 0.630187], [-0.991458, -0.016816, 0.129342], [-0.08907, -0.637128, -0.765594]], 'translation vector': [1.056131, 1.246655, 2.184574]}\nC: {'rotation matrix': [[0.101903, -0.771131, 0.628469], [-0.990357, -0.019031, 0.13723], [-0.093862, -0.636392, -0.765634]], 'translation vector': [1.04909, 1.241123, 2.18482]}\nD: {'rotation matrix': [[0.9999704077161121, 0.0009465902067394159, -0.007647096819155797], [-0.0009526969531251086, 0.9999995333260298, -0.0008216172460913287], [0.00764682863028495, 0.0008292870802229541, 0.9999701657998578]], 'translation vector': [0.005049491112872229, 0.003519427946364395, -0.004842133831311157]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_11_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_11_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_11_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_11_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.088289, -0.769037, 0.633078], [-0.992448, -0.013575, 0.121917], [-0.085165, -0.63906, -0.764427]], 'translation vector': [1.06143, 1.251586, 2.183495]}\nB: {'rotation matrix': [[0.095281, -0.770575, 0.630187], [-0.991458, -0.016816, 0.129342], [-0.08907, -0.637128, -0.765594]], 'translation vector': [1.056131, 1.246655, 2.184574]}\nC: {'rotation matrix': [[0.101903, -0.771131, 0.628469], [-0.990357, -0.019031, 0.13723], [-0.093862, -0.636392, -0.765634]], 'translation vector': [1.04909, 1.241123, 2.18482]}\nD: {'rotation matrix': [[0.9999704077161121, 0.0009465902067394159, -0.007647096819155797], [-0.0009526969531251086, 0.9999995333260298, -0.0008216172460913287], [0.00764682863028495, 0.0008292870802229541, 0.9999701657998578]], 'translation vector': [0.005049491112872229, 0.003519427946364395, -0.004842133831311157]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.236859, -0.585227, 0.775504], [-0.967029, -0.065142, 0.246196], [-0.093563, -0.808248, -0.581361]], 'translation vector': [0.85633, 3.124968, 1.418476]}\nB: {'rotation matrix': [[0.9999986227118945, -0.0014386707321173084, -0.0013720086731618905], [0.0014396405048904127, 0.9999985343757846, 0.0002480174905685849], [0.0013722163701430706, -0.00024858511187529484, 0.9999990039875143]], 'translation vector': [0.0014743108378150183, 9.881450519233503e-05, 0.00010772367212419365]}\nC: {'rotation matrix': [[0.234228, -0.586349, 0.775456], [-0.967526, -0.06262, 0.244894], [-0.095034, -0.807635, -0.581975]], 'translation vector': [0.858687, 3.12069, 1.418757]}\nD: {'rotation matrix': [[0.234642, -0.58546, 0.776002], [-0.967537, -0.063552, 0.24461], [-0.093893, -0.808206, -0.581366]], 'translation vector': [0.856906, 3.122666, 1.417663]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_12_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_12_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_12_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_12_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.236859, -0.585227, 0.775504], [-0.967029, -0.065142, 0.246196], [-0.093563, -0.808248, -0.581361]], 'translation vector': [0.85633, 3.124968, 1.418476]}\nB: {'rotation matrix': [[0.9999986227118945, -0.0014386707321173084, -0.0013720086731618905], [0.0014396405048904127, 0.9999985343757846, 0.0002480174905685849], [0.0013722163701430706, -0.00024858511187529484, 0.9999990039875143]], 'translation vector': [0.0014743108378150183, 9.881450519233503e-05, 0.00010772367212419365]}\nC: {'rotation matrix': [[0.234228, -0.586349, 0.775456], [-0.967526, -0.06262, 0.244894], [-0.095034, -0.807635, -0.581975]], 'translation vector': [0.858687, 3.12069, 1.418757]}\nD: {'rotation matrix': [[0.234642, -0.58546, 0.776002], [-0.967537, -0.063552, 0.24461], [-0.093893, -0.808206, -0.581366]], 'translation vector': [0.856906, 3.122666, 1.417663]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.582104, 0.470868, -0.662901], [0.81311, -0.339656, 0.472743], [-0.002559, -0.814197, -0.580583]], 'translation vector': [4.229822, 1.596572, 1.425168]}\nB: {'rotation matrix': [[0.9999560146697009, 0.003230475520535795, -0.00886892770497088], [-0.0032175833964298243, 0.9999942676924473, 0.0014734023416888033], [0.008873986014087454, -0.001444906579376565, 0.9999590747869875]], 'translation vector': [0.0012149381321875374, 0.0024560981455157282, -0.00010287317760537817]}\nC: {'rotation matrix': [[0.582444, 0.471641, -0.662053], [0.812867, -0.340629, 0.472461], [-0.002682, -0.813343, -0.581779]], 'translation vector': [4.230144, 1.598887, 1.426125]}\nD: {'rotation matrix': [[0.583525, 0.471082, -0.661499], [0.812092, -0.340805, 0.473665], [-0.002307, -0.813593, -0.58143]], 'translation vector': [4.230429, 1.59898, 1.426046]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_13_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_13_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_13_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_13_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.582104, 0.470868, -0.662901], [0.81311, -0.339656, 0.472743], [-0.002559, -0.814197, -0.580583]], 'translation vector': [4.229822, 1.596572, 1.425168]}\nB: {'rotation matrix': [[0.9999560146697009, 0.003230475520535795, -0.00886892770497088], [-0.0032175833964298243, 0.9999942676924473, 0.0014734023416888033], [0.008873986014087454, -0.001444906579376565, 0.9999590747869875]], 'translation vector': [0.0012149381321875374, 0.0024560981455157282, -0.00010287317760537817]}\nC: {'rotation matrix': [[0.582444, 0.471641, -0.662053], [0.812867, -0.340629, 0.472461], [-0.002682, -0.813343, -0.581779]], 'translation vector': [4.230144, 1.598887, 1.426125]}\nD: {'rotation matrix': [[0.583525, 0.471082, -0.661499], [0.812092, -0.340805, 0.473665], [-0.002307, -0.813593, -0.58143]], 'translation vector': [4.230429, 1.59898, 1.426046]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.990893, 0.057008, -0.121987], [0.134304, -0.353431, 0.92577], [0.009662, -0.933722, -0.357869]], 'translation vector': [2.186028, 2.144782, 1.462596]}\nB: {'rotation matrix': [[0.991569, 0.053062, -0.11822], [0.129357, -0.351493, 0.927211], [0.007646, -0.934686, -0.355393]], 'translation vector': [2.183204, 2.143093, 1.462234]}\nC: {'rotation matrix': [[0.9999874902969159, 0.004379197439594465, -0.002403723493325247], [-0.004372650351021444, 0.9999870005118716, 0.0026700250405810233], [0.0024158589744238553, -0.0026609890925621414, 0.9999939599581709]], 'translation vector': [-0.003672033476809222, -0.0017027412429904132, -0.0003357999980959647]}\nD: {'rotation matrix': [[0.991257, 0.055775, -0.119575], [0.131602, -0.352888, 0.926364], [0.009471, -0.934002, -0.357143]], 'translation vector': [2.184101, 2.143995, 1.46179]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_14_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_14_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_14_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_14_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.990893, 0.057008, -0.121987], [0.134304, -0.353431, 0.92577], [0.009662, -0.933722, -0.357869]], 'translation vector': [2.186028, 2.144782, 1.462596]}\nB: {'rotation matrix': [[0.991569, 0.053062, -0.11822], [0.129357, -0.351493, 0.927211], [0.007646, -0.934686, -0.355393]], 'translation vector': [2.183204, 2.143093, 1.462234]}\nC: {'rotation matrix': [[0.9999874902969159, 0.004379197439594465, -0.002403723493325247], [-0.004372650351021444, 0.9999870005118716, 0.0026700250405810233], [0.0024158589744238553, -0.0026609890925621414, 0.9999939599581709]], 'translation vector': [-0.003672033476809222, -0.0017027412429904132, -0.0003357999980959647]}\nD: {'rotation matrix': [[0.991257, 0.055775, -0.119575], [0.131602, -0.352888, 0.926364], [0.009471, -0.934002, -0.357143]], 'translation vector': [2.184101, 2.143995, 1.46179]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999986691328494, -0.001073261840895818, -0.0008802624854347336], [0.001080335550453663, 0.9999723432097648, 0.00736012496129083], [0.0008730903166935182, -0.007359768837914202, 0.9999729147693108]], 'translation vector': [0.0010486658094048806, -0.004009939681768326, 0.0017973568614269853]}\nB: {'rotation matrix': [[-0.386299, -0.298688, 0.872673], [-0.920393, 0.186791, -0.343491], [-0.060411, -0.935893, -0.347067]], 'translation vector': [2.08048, 4.009937, 1.840847]}\nC: {'rotation matrix': [[-0.383122, -0.307436, 0.871034], [-0.921947, 0.185316, -0.340108], [-0.056855, -0.933349, -0.354438]], 'translation vector': [2.080896, 4.009106, 1.847586]}\nD: {'rotation matrix': [[-0.384424, -0.301178, 0.872645], [-0.921297, 0.185141, -0.341959], [-0.058572, -0.935422, -0.348647]], 'translation vector': [2.077995, 4.010322, 1.837904]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_15_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_15_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_15_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_15_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999986691328494, -0.001073261840895818, -0.0008802624854347336], [0.001080335550453663, 0.9999723432097648, 0.00736012496129083], [0.0008730903166935182, -0.007359768837914202, 0.9999729147693108]], 'translation vector': [0.0010486658094048806, -0.004009939681768326, 0.0017973568614269853]}\nB: {'rotation matrix': [[-0.386299, -0.298688, 0.872673], [-0.920393, 0.186791, -0.343491], [-0.060411, -0.935893, -0.347067]], 'translation vector': [2.08048, 4.009937, 1.840847]}\nC: {'rotation matrix': [[-0.383122, -0.307436, 0.871034], [-0.921947, 0.185316, -0.340108], [-0.056855, -0.933349, -0.354438]], 'translation vector': [2.080896, 4.009106, 1.847586]}\nD: {'rotation matrix': [[-0.384424, -0.301178, 0.872645], [-0.921297, 0.185141, -0.341959], [-0.058572, -0.935422, -0.348647]], 'translation vector': [2.077995, 4.010322, 1.837904]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999787823940977, 0.006347499783107699, -0.0013436878206843045], [-0.006341718624184496, 0.9999688828294876, 0.004625015892328648], [0.0013735202687803576, -0.004616292980016383, 0.9999878757602079]], 'translation vector': [-0.006298849286503927, 0.01405890593176995, 0.0007444533799123576]}\nB: {'rotation matrix': [[0.999733, -0.006694, 0.022129], [-0.023039, -0.368118, 0.929494], [0.001924, -0.929755, -0.368173]], 'translation vector': [3.317142, 3.173762, 1.523565]}\nC: {'rotation matrix': [[0.999731, -0.010083, 0.02088], [-0.023127, -0.369367, 0.928996], [-0.001654, -0.929229, -0.3695]], 'translation vector': [3.314788, 3.169853, 1.521514]}\nD: {'rotation matrix': [[0.999712, -0.007131, 0.022924], [-0.023946, -0.364324, 0.930964], [0.001713, -0.931245, -0.36439]], 'translation vector': [3.320507, 3.174599, 1.524876]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_16_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_16_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_16_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_16_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999787823940977, 0.006347499783107699, -0.0013436878206843045], [-0.006341718624184496, 0.9999688828294876, 0.004625015892328648], [0.0013735202687803576, -0.004616292980016383, 0.9999878757602079]], 'translation vector': [-0.006298849286503927, 0.01405890593176995, 0.0007444533799123576]}\nB: {'rotation matrix': [[0.999733, -0.006694, 0.022129], [-0.023039, -0.368118, 0.929494], [0.001924, -0.929755, -0.368173]], 'translation vector': [3.317142, 3.173762, 1.523565]}\nC: {'rotation matrix': [[0.999731, -0.010083, 0.02088], [-0.023127, -0.369367, 0.928996], [-0.001654, -0.929229, -0.3695]], 'translation vector': [3.314788, 3.169853, 1.521514]}\nD: {'rotation matrix': [[0.999712, -0.007131, 0.022924], [-0.023946, -0.364324, 0.930964], [0.001713, -0.931245, -0.36439]], 'translation vector': [3.320507, 3.174599, 1.524876]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.998283, -0.004041, -0.058434], [0.054839, 0.286044, -0.956646], [0.020581, -0.958208, -0.285332]], 'translation vector': [1.688122, 4.435732, 1.572228]}\nB: {'rotation matrix': [[0.9999952600112642, 0.0029895442027471574, 0.0009673920050112699], [-0.0029953662693526914, 0.9999719574782809, 0.006845684012961962], [-0.000945954294544353, -0.006847856485920328, 0.9999767653082445]], 'translation vector': [-0.00043220364465224037, 0.0023057137872921907, 0.0026271806076847426]}\nC: {'rotation matrix': [[-0.998336, -0.002848, -0.057597], [0.054423, 0.283794, -0.95734], [0.019072, -0.958881, -0.283167]], 'translation vector': [1.687961, 4.436946, 1.571062]}\nD: {'rotation matrix': [[-0.998358, -0.001309, -0.057275], [0.054546, 0.284027, -0.957264], [0.017521, -0.958815, -0.283489]], 'translation vector': [1.688286, 4.43679, 1.571851]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_17_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_17_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_17_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_17_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.998283, -0.004041, -0.058434], [0.054839, 0.286044, -0.956646], [0.020581, -0.958208, -0.285332]], 'translation vector': [1.688122, 4.435732, 1.572228]}\nB: {'rotation matrix': [[0.9999952600112642, 0.0029895442027471574, 0.0009673920050112699], [-0.0029953662693526914, 0.9999719574782809, 0.006845684012961962], [-0.000945954294544353, -0.006847856485920328, 0.9999767653082445]], 'translation vector': [-0.00043220364465224037, 0.0023057137872921907, 0.0026271806076847426]}\nC: {'rotation matrix': [[-0.998336, -0.002848, -0.057597], [0.054423, 0.283794, -0.95734], [0.019072, -0.958881, -0.283167]], 'translation vector': [1.687961, 4.436946, 1.571062]}\nD: {'rotation matrix': [[-0.998358, -0.001309, -0.057275], [0.054546, 0.284027, -0.957264], [0.017521, -0.958815, -0.283489]], 'translation vector': [1.688286, 4.43679, 1.571851]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.083515, 0.422666, -0.902429], [0.995888, 0.067297, -0.060645], [0.035099, -0.903783, -0.426549]], 'translation vector': [4.26049, 5.866284, 1.66918]}\nB: {'rotation matrix': [[-0.080848, 0.422553, -0.902725], [0.996028, 0.068154, -0.057302], [0.037311, -0.903772, -0.426385]], 'translation vector': [4.26043, 5.866841, 1.668667]}\nC: {'rotation matrix': [[-0.081468, 0.422714, -0.902594], [0.995995, 0.068006, -0.058049], [0.036844, -0.903708, -0.426561]], 'translation vector': [4.260486, 5.864969, 1.669529]}\nD: {'rotation matrix': [[0.9999996666026792, 0.0007024902365725143, 0.00045309895052521656], [-0.0007041385629428506, 0.9999945142012453, 0.003060691886503368], [-0.00045146230549075186, -0.0030616184704849174, 0.9999957671575359]], 'translation vector': [-0.008238592634347341, 0.0026712907326988944, 0.0010534554726602252]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_18_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_18_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_18_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_18_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.083515, 0.422666, -0.902429], [0.995888, 0.067297, -0.060645], [0.035099, -0.903783, -0.426549]], 'translation vector': [4.26049, 5.866284, 1.66918]}\nB: {'rotation matrix': [[-0.080848, 0.422553, -0.902725], [0.996028, 0.068154, -0.057302], [0.037311, -0.903772, -0.426385]], 'translation vector': [4.26043, 5.866841, 1.668667]}\nC: {'rotation matrix': [[-0.081468, 0.422714, -0.902594], [0.995995, 0.068006, -0.058049], [0.036844, -0.903708, -0.426561]], 'translation vector': [4.260486, 5.864969, 1.669529]}\nD: {'rotation matrix': [[0.9999996666026792, 0.0007024902365725143, 0.00045309895052521656], [-0.0007041385629428506, 0.9999945142012453, 0.003060691886503368], [-0.00045146230549075186, -0.0030616184704849174, 0.9999957671575359]], 'translation vector': [-0.008238592634347341, 0.0026712907326988944, 0.0010534554726602252]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.192624, -0.379717, 0.904826], [-0.981148, -0.059825, 0.183766], [-0.015648, -0.923166, -0.384082]], 'translation vector': [4.984646, 4.164808, 1.32267]}\nB: {'rotation matrix': [[0.9993897560101446, -0.009672092115768357, 0.033569058676964116], [0.00944513640094131, 0.9999317047122397, 0.006930507743012298], [-0.033634032991460915, -0.006610261888877057, 0.9994127816547865]], 'translation vector': [-0.03424616147212767, -0.0027538632482175807, 0.008124405533084023]}\nC: {'rotation matrix': [[0.180272, -0.384554, 0.905329], [-0.983511, -0.056947, 0.171651], [-0.014453, -0.921344, -0.388479]], 'translation vector': [4.987018, 4.177592, 1.323464]}\nD: {'rotation matrix': [[0.205405, -0.377617, 0.902892], [-0.978531, -0.0633, 0.196139], [-0.016912, -0.923796, -0.382512]], 'translation vector': [4.985321, 4.152791, 1.324267]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_19_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_19_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_19_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_19_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.192624, -0.379717, 0.904826], [-0.981148, -0.059825, 0.183766], [-0.015648, -0.923166, -0.384082]], 'translation vector': [4.984646, 4.164808, 1.32267]}\nB: {'rotation matrix': [[0.9993897560101446, -0.009672092115768357, 0.033569058676964116], [0.00944513640094131, 0.9999317047122397, 0.006930507743012298], [-0.033634032991460915, -0.006610261888877057, 0.9994127816547865]], 'translation vector': [-0.03424616147212767, -0.0027538632482175807, 0.008124405533084023]}\nC: {'rotation matrix': [[0.180272, -0.384554, 0.905329], [-0.983511, -0.056947, 0.171651], [-0.014453, -0.921344, -0.388479]], 'translation vector': [4.987018, 4.177592, 1.323464]}\nD: {'rotation matrix': [[0.205405, -0.377617, 0.902892], [-0.978531, -0.0633, 0.196139], [-0.016912, -0.923796, -0.382512]], 'translation vector': [4.985321, 4.152791, 1.324267]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.084118, -0.329466, 0.940413], [-0.993483, 0.100574, -0.05363], [-0.076912, -0.938795, -0.335779]], 'translation vector': [4.338453, 2.933071, 1.462896]}\nB: {'rotation matrix': [[0.9999984493090167, 0.0015295352178733802, -0.0012886089849350254], [-0.0015301527829556035, 0.9999993364831051, -0.0007407082576259022], [0.0012866367449453698, 0.0007417730730382566, 0.99999926268211]], 'translation vector': [-0.001971799758651027, -0.003988184523042726, -0.0019001345524003455]}\nC: {'rotation matrix': [[-0.084181, -0.324678, 0.942071], [-0.993543, 0.09952, -0.054482], [-0.076066, -0.940574, -0.330959]], 'translation vector': [4.337488, 2.935505, 1.461639]}\nD: {'rotation matrix': [[-0.083371, -0.331462, 0.939778], [-0.993645, 0.099215, -0.053156], [-0.075621, -0.938238, -0.337627]], 'translation vector': [4.338066, 2.933557, 1.453891]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_20_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_20_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_20_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_20_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.084118, -0.329466, 0.940413], [-0.993483, 0.100574, -0.05363], [-0.076912, -0.938795, -0.335779]], 'translation vector': [4.338453, 2.933071, 1.462896]}\nB: {'rotation matrix': [[0.9999984493090167, 0.0015295352178733802, -0.0012886089849350254], [-0.0015301527829556035, 0.9999993364831051, -0.0007407082576259022], [0.0012866367449453698, 0.0007417730730382566, 0.99999926268211]], 'translation vector': [-0.001971799758651027, -0.003988184523042726, -0.0019001345524003455]}\nC: {'rotation matrix': [[-0.084181, -0.324678, 0.942071], [-0.993543, 0.09952, -0.054482], [-0.076066, -0.940574, -0.330959]], 'translation vector': [4.337488, 2.935505, 1.461639]}\nD: {'rotation matrix': [[-0.083371, -0.331462, 0.939778], [-0.993645, 0.099215, -0.053156], [-0.075621, -0.938238, -0.337627]], 'translation vector': [4.338066, 2.933557, 1.453891]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.486704, 0.327719, -0.809765], [0.869935, 0.097394, -0.483454], [-0.079571, -0.939742, -0.332496]], 'translation vector': [4.437128, 2.283443, 1.465507]}\nB: {'rotation matrix': [[0.9999935589617881, -0.0023123151478069903, 0.002638162918517237], [0.0023036775758451737, 0.9999925417720922, 0.0032437850296420778], [-0.0026449296307575294, -0.0032387699437697024, 0.9999912244700261]], 'translation vector': [0.0032199017210038927, 0.0001093759992842891, 0.0024338106134420556]}\nC: {'rotation matrix': [[-0.494127, 0.32769, -0.805269], [0.866163, 0.105829, -0.488427], [-0.074832, -0.938839, -0.336126]], 'translation vector': [4.441189, 2.279036, 1.469096]}\nD: {'rotation matrix': [[-0.489836, 0.32797, -0.807773], [0.868301, 0.100425, -0.485767], [-0.078196, -0.939335, -0.333968]], 'translation vector': [4.439312, 2.280933, 1.467607]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_21_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_21_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_21_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_21_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.486704, 0.327719, -0.809765], [0.869935, 0.097394, -0.483454], [-0.079571, -0.939742, -0.332496]], 'translation vector': [4.437128, 2.283443, 1.465507]}\nB: {'rotation matrix': [[0.9999935589617881, -0.0023123151478069903, 0.002638162918517237], [0.0023036775758451737, 0.9999925417720922, 0.0032437850296420778], [-0.0026449296307575294, -0.0032387699437697024, 0.9999912244700261]], 'translation vector': [0.0032199017210038927, 0.0001093759992842891, 0.0024338106134420556]}\nC: {'rotation matrix': [[-0.494127, 0.32769, -0.805269], [0.866163, 0.105829, -0.488427], [-0.074832, -0.938839, -0.336126]], 'translation vector': [4.441189, 2.279036, 1.469096]}\nD: {'rotation matrix': [[-0.489836, 0.32797, -0.807773], [0.868301, 0.100425, -0.485767], [-0.078196, -0.939335, -0.333968]], 'translation vector': [4.439312, 2.280933, 1.467607]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.591474, -0.360427, 0.721284], [-0.806244, -0.251769, 0.535335], [-0.011352, -0.898167, -0.439507]], 'translation vector': [2.523668, 2.4613, 1.342936]}\nB: {'rotation matrix': [[0.9999823901895873, 0.005315913984669213, -0.0024241384709724934], [-0.005325670093989841, 0.9999780230897936, -0.003916850861463004], [0.002402806763812986, 0.0039304838949891525, 0.9999899783862463]], 'translation vector': [-0.0021162233882717763, -0.0011065369325464758, -0.0015805869003076012]}\nC: {'rotation matrix': [[0.588358, -0.362651, 0.722717], [-0.808515, -0.250803, 0.532355], [-0.0118, -0.897542, -0.440771]], 'translation vector': [2.523157, 2.461525, 1.343416]}\nD: {'rotation matrix': [[0.586933, -0.361149, 0.724625], [-0.809565, -0.249931, 0.531168], [-0.010725, -0.898391, -0.439067]], 'translation vector': [2.521696, 2.461699, 1.342706]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_22_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_22_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_22_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_22_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.591474, -0.360427, 0.721284], [-0.806244, -0.251769, 0.535335], [-0.011352, -0.898167, -0.439507]], 'translation vector': [2.523668, 2.4613, 1.342936]}\nB: {'rotation matrix': [[0.9999823901895873, 0.005315913984669213, -0.0024241384709724934], [-0.005325670093989841, 0.9999780230897936, -0.003916850861463004], [0.002402806763812986, 0.0039304838949891525, 0.9999899783862463]], 'translation vector': [-0.0021162233882717763, -0.0011065369325464758, -0.0015805869003076012]}\nC: {'rotation matrix': [[0.588358, -0.362651, 0.722717], [-0.808515, -0.250803, 0.532355], [-0.0118, -0.897542, -0.440771]], 'translation vector': [2.523157, 2.461525, 1.343416]}\nD: {'rotation matrix': [[0.586933, -0.361149, 0.724625], [-0.809565, -0.249931, 0.531168], [-0.010725, -0.898391, -0.439067]], 'translation vector': [2.521696, 2.461699, 1.342706]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.519941, -0.4438, 0.729866], [-0.853216, -0.228773, 0.468706], [-0.041038, -0.866432, -0.497605]], 'translation vector': [1.000289, 1.985685, 1.347635]}\nB: {'rotation matrix': [[0.520738, -0.4401, 0.731535], [-0.852723, -0.226811, 0.470553], [-0.041171, -0.868832, -0.493393]], 'translation vector': [0.998782, 1.983781, 1.347411]}\nC: {'rotation matrix': [[0.9999982219593319, -0.002079266464163152, 4.9620397493296405e-05], [0.0020793800784517404, 0.99998989374647, 0.004115140408478336], [-5.7016409592928054e-05, -0.004114590657101431, 0.9999914687195216]], 'translation vector': [-0.0013942399199289301, -0.0008989415392872679, 0.0029889416631920795]}\nD: {'rotation matrix': [[0.521192, -0.438092, 0.732417], [-0.852373, -0.224319, 0.472378], [-0.04265, -0.870492, -0.490332]], 'translation vector': [0.999181, 1.981126, 1.348386]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_23_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_23_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_23_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_23_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.519941, -0.4438, 0.729866], [-0.853216, -0.228773, 0.468706], [-0.041038, -0.866432, -0.497605]], 'translation vector': [1.000289, 1.985685, 1.347635]}\nB: {'rotation matrix': [[0.520738, -0.4401, 0.731535], [-0.852723, -0.226811, 0.470553], [-0.041171, -0.868832, -0.493393]], 'translation vector': [0.998782, 1.983781, 1.347411]}\nC: {'rotation matrix': [[0.9999982219593319, -0.002079266464163152, 4.9620397493296405e-05], [0.0020793800784517404, 0.99998989374647, 0.004115140408478336], [-5.7016409592928054e-05, -0.004114590657101431, 0.9999914687195216]], 'translation vector': [-0.0013942399199289301, -0.0008989415392872679, 0.0029889416631920795]}\nD: {'rotation matrix': [[0.521192, -0.438092, 0.732417], [-0.852373, -0.224319, 0.472378], [-0.04265, -0.870492, -0.490332]], 'translation vector': [0.999181, 1.981126, 1.348386]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.961526, 0.04991, -0.270143], [0.263115, -0.450039, 0.853367], [-0.078983, -0.891613, -0.445856]], 'translation vector': [2.643601, 1.008587, 1.47483]}\nB: {'rotation matrix': [[0.9999611816262259, 0.006037262376624097, -0.006357733639992428], [-0.0060929360747264, 0.9999425771984013, -0.008789441445278661], [0.006305603526763411, 0.00882761527567942, 0.9999410260999323]], 'translation vector': [0.006025344459442472, -0.004704561758730241, -0.003336645842906716]}\nC: {'rotation matrix': [[0.958799, 0.0516, -0.27936], [0.272826, -0.441359, 0.85485], [-0.079187, -0.895846, -0.437252]], 'translation vector': [2.65219, 1.005876, 1.472401]}\nD: {'rotation matrix': [[0.963523, 0.050371, -0.262843], [0.256662, -0.452159, 0.854211], [-0.075819, -0.890514, -0.448594]], 'translation vector': [2.637859, 1.00927, 1.478429]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_24_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_24_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_24_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_24_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.961526, 0.04991, -0.270143], [0.263115, -0.450039, 0.853367], [-0.078983, -0.891613, -0.445856]], 'translation vector': [2.643601, 1.008587, 1.47483]}\nB: {'rotation matrix': [[0.9999611816262259, 0.006037262376624097, -0.006357733639992428], [-0.0060929360747264, 0.9999425771984013, -0.008789441445278661], [0.006305603526763411, 0.00882761527567942, 0.9999410260999323]], 'translation vector': [0.006025344459442472, -0.004704561758730241, -0.003336645842906716]}\nC: {'rotation matrix': [[0.958799, 0.0516, -0.27936], [0.272826, -0.441359, 0.85485], [-0.079187, -0.895846, -0.437252]], 'translation vector': [2.65219, 1.005876, 1.472401]}\nD: {'rotation matrix': [[0.963523, 0.050371, -0.262843], [0.256662, -0.452159, 0.854211], [-0.075819, -0.890514, -0.448594]], 'translation vector': [2.637859, 1.00927, 1.478429]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999982465629441, -0.0013465983865406563, -0.0009053084488285325], [0.0013501875456354963, 0.999982864157121, 0.005743205099670796], [0.0008980715136351007, -0.0057437521444019864, 0.9999827649954101]], 'translation vector': [0.006929822597295576, -0.003954296383870348, -0.0008962942027399556]}\nB: {'rotation matrix': [[0.678055, 0.431256, -0.595198], [0.734977, -0.40565, 0.543375], [-0.007108, -0.805894, -0.592017]], 'translation vector': [3.965842, 0.866337, 1.41271]}\nC: {'rotation matrix': [[0.680551, 0.428937, -0.594024], [0.732652, -0.407746, 0.544944], [-0.008465, -0.806075, -0.591754]], 'translation vector': [3.965306, 0.868392, 1.416605]}\nD: {'rotation matrix': [[0.681867, 0.427349, -0.593659], [0.731402, -0.409894, 0.545013], [-0.010426, -0.805829, -0.592057]], 'translation vector': [3.966104, 0.870012, 1.418402]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_25_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_25_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_25_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_25_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999982465629441, -0.0013465983865406563, -0.0009053084488285325], [0.0013501875456354963, 0.999982864157121, 0.005743205099670796], [0.0008980715136351007, -0.0057437521444019864, 0.9999827649954101]], 'translation vector': [0.006929822597295576, -0.003954296383870348, -0.0008962942027399556]}\nB: {'rotation matrix': [[0.678055, 0.431256, -0.595198], [0.734977, -0.40565, 0.543375], [-0.007108, -0.805894, -0.592017]], 'translation vector': [3.965842, 0.866337, 1.41271]}\nC: {'rotation matrix': [[0.680551, 0.428937, -0.594024], [0.732652, -0.407746, 0.544944], [-0.008465, -0.806075, -0.591754]], 'translation vector': [3.965306, 0.868392, 1.416605]}\nD: {'rotation matrix': [[0.681867, 0.427349, -0.593659], [0.731402, -0.409894, 0.545013], [-0.010426, -0.805829, -0.592057]], 'translation vector': [3.966104, 0.870012, 1.418402]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.999985554212974, -0.0031855347936376403, 0.004407056654662203], [0.003212953146819932, 0.9999747868858251, -0.006356723963664241], [-0.004386364338204691, 0.006371176808258376, 0.999970327730571]], 'translation vector': [-0.010337331339885125, -0.003078319020026643, -0.0057934529197571916]}\nB: {'rotation matrix': [[-0.816952, -0.193331, 0.543335], [-0.575587, 0.331994, -0.747315], [-0.035905, -0.923257, -0.382502]], 'translation vector': [4.389139, 4.029859, 1.398995]}\nC: {'rotation matrix': [[-0.817965, -0.190324, 0.542871], [-0.574258, 0.326013, -0.750961], [-0.034057, -0.926009, -0.375963]], 'translation vector': [4.389857, 4.037429, 1.401592]}\nD: {'rotation matrix': [[-0.817754, -0.196252, 0.541077], [-0.574392, 0.338327, -0.745392], [-0.036776, -0.920337, -0.389394]], 'translation vector': [4.391615, 4.02441, 1.397694]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_26_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_26_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_26_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_26_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.999985554212974, -0.0031855347936376403, 0.004407056654662203], [0.003212953146819932, 0.9999747868858251, -0.006356723963664241], [-0.004386364338204691, 0.006371176808258376, 0.999970327730571]], 'translation vector': [-0.010337331339885125, -0.003078319020026643, -0.0057934529197571916]}\nB: {'rotation matrix': [[-0.816952, -0.193331, 0.543335], [-0.575587, 0.331994, -0.747315], [-0.035905, -0.923257, -0.382502]], 'translation vector': [4.389139, 4.029859, 1.398995]}\nC: {'rotation matrix': [[-0.817965, -0.190324, 0.542871], [-0.574258, 0.326013, -0.750961], [-0.034057, -0.926009, -0.375963]], 'translation vector': [4.389857, 4.037429, 1.401592]}\nD: {'rotation matrix': [[-0.817754, -0.196252, 0.541077], [-0.574392, 0.338327, -0.745392], [-0.036776, -0.920337, -0.389394]], 'translation vector': [4.391615, 4.02441, 1.397694]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.989636, -0.107174, 0.095574], [-0.139302, -0.554918, 0.820159], [-0.034864, -0.824973, -0.564096]], 'translation vector': [6.683643, 2.494903, 1.406773]}\nB: {'rotation matrix': [[0.989623, -0.107386, 0.095468], [-0.139407, -0.55662, 0.818988], [-0.034808, -0.823798, -0.565814]], 'translation vector': [6.681599, 2.49535, 1.408922]}\nC: {'rotation matrix': [[0.989755, -0.10674, 0.094822], [-0.138471, -0.555821, 0.819688], [-0.034789, -0.824421, -0.564907]], 'translation vector': [6.681521, 2.493315, 1.407658]}\nD: {'rotation matrix': [[0.9999901303029469, 0.004176228929177566, 0.0011903596205295832], [-0.00418098989555596, 0.9999858303738082, 0.003280298331639201], [-0.0011769495287118517, -0.003284936107684076, 0.9999936907012513]], 'translation vector': [-0.000365649748907515, 0.007725567810238587, -0.0007277349140568656]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_27_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_27_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_27_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_27_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.989636, -0.107174, 0.095574], [-0.139302, -0.554918, 0.820159], [-0.034864, -0.824973, -0.564096]], 'translation vector': [6.683643, 2.494903, 1.406773]}\nB: {'rotation matrix': [[0.989623, -0.107386, 0.095468], [-0.139407, -0.55662, 0.818988], [-0.034808, -0.823798, -0.565814]], 'translation vector': [6.681599, 2.49535, 1.408922]}\nC: {'rotation matrix': [[0.989755, -0.10674, 0.094822], [-0.138471, -0.555821, 0.819688], [-0.034789, -0.824421, -0.564907]], 'translation vector': [6.681521, 2.493315, 1.407658]}\nD: {'rotation matrix': [[0.9999901303029469, 0.004176228929177566, 0.0011903596205295832], [-0.00418098989555596, 0.9999858303738082, 0.003280298331639201], [-0.0011769495287118517, -0.003284936107684076, 0.9999936907012513]], 'translation vector': [-0.000365649748907515, 0.007725567810238587, -0.0007277349140568656]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999990866555979, 0.0004093298689293289, 0.0008391460833273652], [-0.00041250820264666677, 0.9999930945433543, 0.0037605519712407454], [-0.0008378351814566854, -0.0037610473264969298, 0.9999924864591837]], 'translation vector': [-0.002544400937407598, -0.0017921618995201394, 0.0018599883369136982]}\nB: {'rotation matrix': [[0.155491, 0.600889, -0.784063], [0.987779, -0.103232, 0.116776], [-0.010771, -0.792638, -0.609597]], 'translation vector': [3.280226, 1.958162, 1.281368]}\nC: {'rotation matrix': [[0.159827, 0.598569, -0.784966], [0.987096, -0.104834, 0.121042], [-0.009839, -0.794182, -0.6076]], 'translation vector': [3.27763, 1.954194, 1.282551]}\nD: {'rotation matrix': [[0.164916, 0.595071, -0.786571], [0.986276, -0.105924, 0.126651], [-0.007951, -0.796662, -0.604372]], 'translation vector': [3.274219, 1.949482, 1.285722]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_28_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_28_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_28_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_28_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999990866555979, 0.0004093298689293289, 0.0008391460833273652], [-0.00041250820264666677, 0.9999930945433543, 0.0037605519712407454], [-0.0008378351814566854, -0.0037610473264969298, 0.9999924864591837]], 'translation vector': [-0.002544400937407598, -0.0017921618995201394, 0.0018599883369136982]}\nB: {'rotation matrix': [[0.155491, 0.600889, -0.784063], [0.987779, -0.103232, 0.116776], [-0.010771, -0.792638, -0.609597]], 'translation vector': [3.280226, 1.958162, 1.281368]}\nC: {'rotation matrix': [[0.159827, 0.598569, -0.784966], [0.987096, -0.104834, 0.121042], [-0.009839, -0.794182, -0.6076]], 'translation vector': [3.27763, 1.954194, 1.282551]}\nD: {'rotation matrix': [[0.164916, 0.595071, -0.786571], [0.986276, -0.105924, 0.126651], [-0.007951, -0.796662, -0.604372]], 'translation vector': [3.274219, 1.949482, 1.285722]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999748122492941, 0.0038384551465654687, -0.005906369455983883], [-0.0037983225864595374, 0.9999703256171942, 0.006729367156156027], [0.005932617650240982, -0.006707131114408277, 0.9999600560348555]], 'translation vector': [-0.00423530822573337, 0.003061759875670811, -0.009950114450137715]}\nB: {'rotation matrix': [[-0.937821, -0.115212, 0.32744], [-0.346749, 0.354456, -0.868405], [-0.016013, -0.927948, -0.372366]], 'translation vector': [5.30238, 4.116027, 1.850731]}\nC: {'rotation matrix': [[-0.932005, -0.116649, 0.343162], [-0.36182, 0.355063, -0.861984], [-0.021294, -0.927536, -0.373127]], 'translation vector': [5.291139, 4.11983, 1.856331]}\nD: {'rotation matrix': [[-0.934388, -0.115649, 0.336964], [-0.355745, 0.353624, -0.865099], [-0.01911, -0.928211, -0.371563]], 'translation vector': [5.294776, 4.11946, 1.854234]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_29_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_29_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_29_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_29_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999748122492941, 0.0038384551465654687, -0.005906369455983883], [-0.0037983225864595374, 0.9999703256171942, 0.006729367156156027], [0.005932617650240982, -0.006707131114408277, 0.9999600560348555]], 'translation vector': [-0.00423530822573337, 0.003061759875670811, -0.009950114450137715]}\nB: {'rotation matrix': [[-0.937821, -0.115212, 0.32744], [-0.346749, 0.354456, -0.868405], [-0.016013, -0.927948, -0.372366]], 'translation vector': [5.30238, 4.116027, 1.850731]}\nC: {'rotation matrix': [[-0.932005, -0.116649, 0.343162], [-0.36182, 0.355063, -0.861984], [-0.021294, -0.927536, -0.373127]], 'translation vector': [5.291139, 4.11983, 1.856331]}\nD: {'rotation matrix': [[-0.934388, -0.115649, 0.336964], [-0.355745, 0.353624, -0.865099], [-0.01911, -0.928211, -0.371563]], 'translation vector': [5.294776, 4.11946, 1.854234]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.431582, -0.098037, 0.896731], [-0.900431, 0.106787, -0.421688], [-0.054418, -0.989437, -0.134363]], 'translation vector': [4.412532, 3.596741, 1.526323]}\nB: {'rotation matrix': [[-0.43275, -0.095778, 0.896412], [-0.899777, 0.107595, -0.422878], [-0.055947, -0.989571, -0.132741]], 'translation vector': [4.410773, 3.601486, 1.526138]}\nC: {'rotation matrix': [[0.9999520333968362, 0.0006753791567632332, -0.009755571516398104], [-0.0006310509478896895, 0.999990797204237, 0.004384447971913257], [0.009758133830260066, -0.004378955576420755, 0.9999427994349811]], 'translation vector': [0.0016435229369795579, -0.00040384651884517453, 0.0035746064241082287]}\nD: {'rotation matrix': [[-0.433914, -0.093907, 0.896047], [-0.899123, 0.108519, -0.42403], [-0.057419, -0.989649, -0.131522]], 'translation vector': [4.40951, 3.606652, 1.52516]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_30_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_30_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_30_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_30_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.431582, -0.098037, 0.896731], [-0.900431, 0.106787, -0.421688], [-0.054418, -0.989437, -0.134363]], 'translation vector': [4.412532, 3.596741, 1.526323]}\nB: {'rotation matrix': [[-0.43275, -0.095778, 0.896412], [-0.899777, 0.107595, -0.422878], [-0.055947, -0.989571, -0.132741]], 'translation vector': [4.410773, 3.601486, 1.526138]}\nC: {'rotation matrix': [[0.9999520333968362, 0.0006753791567632332, -0.009755571516398104], [-0.0006310509478896895, 0.999990797204237, 0.004384447971913257], [0.009758133830260066, -0.004378955576420755, 0.9999427994349811]], 'translation vector': [0.0016435229369795579, -0.00040384651884517453, 0.0035746064241082287]}\nD: {'rotation matrix': [[-0.433914, -0.093907, 0.896047], [-0.899123, 0.108519, -0.42403], [-0.057419, -0.989649, -0.131522]], 'translation vector': [4.40951, 3.606652, 1.52516]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999917661523451, 0.003965604575831933, -0.00020113541169654973], [-0.0039642406405361414, 0.9999897644646852, 0.002088386147537849], [0.00020867644615939134, -0.0020887952983848278, 0.9999975122366389]], 'translation vector': [0.003940681703816118, 0.0007777989134077623, 0.003188885648093276]}\nB: {'rotation matrix': [[-0.926146, 0.120999, -0.357228], [0.374267, 0.177659, -0.910144], [-0.046662, -0.976625, -0.209824]], 'translation vector': [4.737155, 2.737478, 1.223721]}\nC: {'rotation matrix': [[-0.926101, 0.124421, -0.356169], [0.374063, 0.179874, -0.909792], [-0.049131, -0.975789, -0.213123]], 'translation vector': [4.73486, 2.737298, 1.223615]}\nD: {'rotation matrix': [[-0.927631, 0.118543, -0.354186], [0.370581, 0.173865, -0.912382], [-0.046576, -0.977609, -0.205212]], 'translation vector': [4.731637, 2.739449, 1.226493]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_31_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_31_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_31_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_31_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999917661523451, 0.003965604575831933, -0.00020113541169654973], [-0.0039642406405361414, 0.9999897644646852, 0.002088386147537849], [0.00020867644615939134, -0.0020887952983848278, 0.9999975122366389]], 'translation vector': [0.003940681703816118, 0.0007777989134077623, 0.003188885648093276]}\nB: {'rotation matrix': [[-0.926146, 0.120999, -0.357228], [0.374267, 0.177659, -0.910144], [-0.046662, -0.976625, -0.209824]], 'translation vector': [4.737155, 2.737478, 1.223721]}\nC: {'rotation matrix': [[-0.926101, 0.124421, -0.356169], [0.374063, 0.179874, -0.909792], [-0.049131, -0.975789, -0.213123]], 'translation vector': [4.73486, 2.737298, 1.223615]}\nD: {'rotation matrix': [[-0.927631, 0.118543, -0.354186], [0.370581, 0.173865, -0.912382], [-0.046576, -0.977609, -0.205212]], 'translation vector': [4.731637, 2.739449, 1.226493]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.854414, -0.337949, 0.394674], [-0.51408, -0.439475, 0.736602], [-0.075485, -0.832257, -0.549227]], 'translation vector': [2.728753, 6.764147, 1.410515]}\nB: {'rotation matrix': [[0.857663, -0.338131, 0.387404], [-0.508133, -0.441807, 0.739329], [-0.078832, -0.830948, -0.550737]], 'translation vector': [2.730525, 6.755143, 1.407191]}\nC: {'rotation matrix': [[0.856314, -0.338309, 0.390222], [-0.510605, -0.441176, 0.738002], [-0.077516, -0.83121, -0.550528]], 'translation vector': [2.731703, 6.760056, 1.408417]}\nD: {'rotation matrix': [[0.9999681707679642, 0.006356789386196493, 0.004849115684302276], [-0.0063488135736310385, 0.9999779524760675, -0.0017687198088092734], [-0.004860696689611514, 0.0017388114569044306, 0.9999869431575653]], 'translation vector': [0.001030885579985874, -0.006730226347642976, 0.006769981822561277]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_32_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_32_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_32_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_32_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.854414, -0.337949, 0.394674], [-0.51408, -0.439475, 0.736602], [-0.075485, -0.832257, -0.549227]], 'translation vector': [2.728753, 6.764147, 1.410515]}\nB: {'rotation matrix': [[0.857663, -0.338131, 0.387404], [-0.508133, -0.441807, 0.739329], [-0.078832, -0.830948, -0.550737]], 'translation vector': [2.730525, 6.755143, 1.407191]}\nC: {'rotation matrix': [[0.856314, -0.338309, 0.390222], [-0.510605, -0.441176, 0.738002], [-0.077516, -0.83121, -0.550528]], 'translation vector': [2.731703, 6.760056, 1.408417]}\nD: {'rotation matrix': [[0.9999681707679642, 0.006356789386196493, 0.004849115684302276], [-0.0063488135736310385, 0.9999779524760675, -0.0017687198088092734], [-0.004860696689611514, 0.0017388114569044306, 0.9999869431575653]], 'translation vector': [0.001030885579985874, -0.006730226347642976, 0.006769981822561277]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.230447, -0.471956, 0.850971], [-0.964157, 0.007445, 0.265227], [-0.131511, -0.881591, -0.453324]], 'translation vector': [3.039354, 2.955346, 1.549151]}\nB: {'rotation matrix': [[0.228449, -0.472123, 0.851417], [-0.96468, 0.008044, 0.2633], [-0.131159, -0.881496, -0.45361]], 'translation vector': [3.038737, 2.954341, 1.548813]}\nC: {'rotation matrix': [[0.9999932374461685, -2.1218966376535376e-05, -0.003832905020754294], [2.3397413103181216e-05, 0.9999998755264938, 0.00025510731414015743], [0.003833858200182055, -0.0002556535302727228, 0.9999922097121886]], 'translation vector': [-0.00018189815458224956, -0.001091765193039329, 0.0006659190149727046]}\nD: {'rotation matrix': [[0.234859, -0.471403, 0.850071], [-0.962925, 0.006583, 0.269689], [-0.132728, -0.881894, -0.452379]], 'translation vector': [3.04024, 2.955162, 1.549553]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_33_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_33_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_33_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_33_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.230447, -0.471956, 0.850971], [-0.964157, 0.007445, 0.265227], [-0.131511, -0.881591, -0.453324]], 'translation vector': [3.039354, 2.955346, 1.549151]}\nB: {'rotation matrix': [[0.228449, -0.472123, 0.851417], [-0.96468, 0.008044, 0.2633], [-0.131159, -0.881496, -0.45361]], 'translation vector': [3.038737, 2.954341, 1.548813]}\nC: {'rotation matrix': [[0.9999932374461685, -2.1218966376535376e-05, -0.003832905020754294], [2.3397413103181216e-05, 0.9999998755264938, 0.00025510731414015743], [0.003833858200182055, -0.0002556535302727228, 0.9999922097121886]], 'translation vector': [-0.00018189815458224956, -0.001091765193039329, 0.0006659190149727046]}\nD: {'rotation matrix': [[0.234859, -0.471403, 0.850071], [-0.962925, 0.006583, 0.269689], [-0.132728, -0.881894, -0.452379]], 'translation vector': [3.04024, 2.955162, 1.549553]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999722451927703, 0.006302215099590525, -0.00403981735739725], [-0.006327345454891822, 0.9999605703041478, -0.0062233586210535905], [0.004001166188993241, 0.006248748688806741, 0.9999728906669906]], 'translation vector': [0.005093628494140745, -0.0003734020522905279, 0.0005966377475724594]}\nB: {'rotation matrix': [[-0.852779, -0.130984, 0.505581], [-0.521088, 0.148208, -0.840537], [0.035166, -0.980244, -0.194643]], 'translation vector': [2.708243, 1.722235, 1.600397]}\nC: {'rotation matrix': [[-0.85558, -0.133703, 0.500106], [-0.51643, 0.153622, -0.842437], [0.035809, -0.979042, -0.200484]], 'translation vector': [2.710987, 1.723705, 1.596351]}\nD: {'rotation matrix': [[-0.853917, -0.132599, 0.503232], [-0.519221, 0.151792, -0.841052], [0.035136, -0.979478, -0.198466]], 'translation vector': [2.709099, 1.722802, 1.598917]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_34_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_34_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_34_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_34_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999722451927703, 0.006302215099590525, -0.00403981735739725], [-0.006327345454891822, 0.9999605703041478, -0.0062233586210535905], [0.004001166188993241, 0.006248748688806741, 0.9999728906669906]], 'translation vector': [0.005093628494140745, -0.0003734020522905279, 0.0005966377475724594]}\nB: {'rotation matrix': [[-0.852779, -0.130984, 0.505581], [-0.521088, 0.148208, -0.840537], [0.035166, -0.980244, -0.194643]], 'translation vector': [2.708243, 1.722235, 1.600397]}\nC: {'rotation matrix': [[-0.85558, -0.133703, 0.500106], [-0.51643, 0.153622, -0.842437], [0.035809, -0.979042, -0.200484]], 'translation vector': [2.710987, 1.723705, 1.596351]}\nD: {'rotation matrix': [[-0.853917, -0.132599, 0.503232], [-0.519221, 0.151792, -0.841052], [0.035136, -0.979478, -0.198466]], 'translation vector': [2.709099, 1.722802, 1.598917]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.478873, -0.428944, 0.765955], [-0.87533, -0.166797, 0.453846], [-0.066915, -0.887798, -0.455343]], 'translation vector': [0.725473, 2.084639, 1.401624]}\nB: {'rotation matrix': [[0.9999321559246817, -0.004700941469421, -0.010648795235394873], [0.0046277052826301434, 0.9999657024420984, -0.006814383513752801], [0.010680686020790284, 0.006765103656469935, 0.9999199949357819]], 'translation vector': [-0.010159202650746213, -0.00890278579572934, 0.006564575177659959]}\nC: {'rotation matrix': [[0.476891, -0.427829, 0.767814], [-0.876452, -0.165482, 0.452159], [-0.066387, -0.888582, -0.453888]], 'translation vector': [0.720453, 2.082574, 1.402557]}\nD: {'rotation matrix': [[0.480806, -0.429519, 0.764421], [-0.874127, -0.166433, 0.456292], [-0.068761, -0.887589, -0.455476]], 'translation vector': [0.729586, 2.089959, 1.401763]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_35_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_35_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_35_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_35_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.478873, -0.428944, 0.765955], [-0.87533, -0.166797, 0.453846], [-0.066915, -0.887798, -0.455343]], 'translation vector': [0.725473, 2.084639, 1.401624]}\nB: {'rotation matrix': [[0.9999321559246817, -0.004700941469421, -0.010648795235394873], [0.0046277052826301434, 0.9999657024420984, -0.006814383513752801], [0.010680686020790284, 0.006765103656469935, 0.9999199949357819]], 'translation vector': [-0.010159202650746213, -0.00890278579572934, 0.006564575177659959]}\nC: {'rotation matrix': [[0.476891, -0.427829, 0.767814], [-0.876452, -0.165482, 0.452159], [-0.066387, -0.888582, -0.453888]], 'translation vector': [0.720453, 2.082574, 1.402557]}\nD: {'rotation matrix': [[0.480806, -0.429519, 0.764421], [-0.874127, -0.166433, 0.456292], [-0.068761, -0.887589, -0.455476]], 'translation vector': [0.729586, 2.089959, 1.401763]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.745148, -0.37119, 0.554052], [-0.66666, -0.436838, 0.603934], [0.017857, -0.819385, -0.572966]], 'translation vector': [3.707678, 4.401502, 1.259793]}\nB: {'rotation matrix': [[0.9999956925108978, 0.0027628800894689116, -0.0013776131907199199], [-0.002750831230441356, 0.9999530244932063, 0.009285447903977378], [0.0014027365353670037, -0.00928147923100416, 0.9999562334978788]], 'translation vector': [-0.001323540742452639, -0.0019242276069570963, 0.0020358659652854882]}\nC: {'rotation matrix': [[0.745353, -0.370803, 0.554034], [-0.666429, -0.436771, 0.604239], [0.017933, -0.819595, -0.572662]], 'translation vector': [3.707908, 4.40198, 1.260519]}\nD: {'rotation matrix': [[0.746372, -0.37052, 0.55285], [-0.665272, -0.438418, 0.60432], [0.018468, -0.818844, -0.57372]], 'translation vector': [3.708833, 4.402057, 1.261367]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_36_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_36_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_36_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_36_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.745148, -0.37119, 0.554052], [-0.66666, -0.436838, 0.603934], [0.017857, -0.819385, -0.572966]], 'translation vector': [3.707678, 4.401502, 1.259793]}\nB: {'rotation matrix': [[0.9999956925108978, 0.0027628800894689116, -0.0013776131907199199], [-0.002750831230441356, 0.9999530244932063, 0.009285447903977378], [0.0014027365353670037, -0.00928147923100416, 0.9999562334978788]], 'translation vector': [-0.001323540742452639, -0.0019242276069570963, 0.0020358659652854882]}\nC: {'rotation matrix': [[0.745353, -0.370803, 0.554034], [-0.666429, -0.436771, 0.604239], [0.017933, -0.819595, -0.572662]], 'translation vector': [3.707908, 4.40198, 1.260519]}\nD: {'rotation matrix': [[0.746372, -0.37052, 0.55285], [-0.665272, -0.438418, 0.60432], [0.018468, -0.818844, -0.57372]], 'translation vector': [3.708833, 4.402057, 1.261367]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.116268, -0.545929, 0.829725], [-0.992164, 0.102308, -0.071715], [-0.045736, -0.831562, -0.553546]], 'translation vector': [1.188241, 1.804719, 1.496587]}\nB: {'rotation matrix': [[-0.114381, -0.546538, 0.829586], [-0.992382, 0.101334, -0.070067], [-0.045771, -0.83128, -0.553966]], 'translation vector': [1.18804, 1.806907, 1.497044]}\nC: {'rotation matrix': [[-0.116275, -0.545912, 0.829735], [-0.992183, 0.101947, -0.071965], [-0.045303, -0.831617, -0.553499]], 'translation vector': [1.188215, 1.807271, 1.496983]}\nD: {'rotation matrix': [[0.9999972656726313, 0.0013206868291442259, 0.0020218952923470846], [-0.0013233096218697464, 0.999999141112598, 0.0010787718934225417], [-0.0020206374390544144, -0.0010806530653509267, 0.9999977188154618]], 'translation vector': [-0.0038275910671821123, 0.000160776135250007, -0.0021485328355081296]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_37_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_37_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_37_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_37_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.116268, -0.545929, 0.829725], [-0.992164, 0.102308, -0.071715], [-0.045736, -0.831562, -0.553546]], 'translation vector': [1.188241, 1.804719, 1.496587]}\nB: {'rotation matrix': [[-0.114381, -0.546538, 0.829586], [-0.992382, 0.101334, -0.070067], [-0.045771, -0.83128, -0.553966]], 'translation vector': [1.18804, 1.806907, 1.497044]}\nC: {'rotation matrix': [[-0.116275, -0.545912, 0.829735], [-0.992183, 0.101947, -0.071965], [-0.045303, -0.831617, -0.553499]], 'translation vector': [1.188215, 1.807271, 1.496983]}\nD: {'rotation matrix': [[0.9999972656726313, 0.0013206868291442259, 0.0020218952923470846], [-0.0013233096218697464, 0.999999141112598, 0.0010787718934225417], [-0.0020206374390544144, -0.0010806530653509267, 0.9999977188154618]], 'translation vector': [-0.0038275910671821123, 0.000160776135250007, -0.0021485328355081296]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.997087, -0.025415, -0.071922], [0.054952, -0.414609, 0.908339], [-0.052905, -0.909645, -0.412004]], 'translation vector': [4.407682, 5.403047, 1.49649]}\nB: {'rotation matrix': [[1.0000003154168173, -0.00026066196587795827, 0.0009499731630029714], [0.00026257414143552785, 0.9999962311537921, -0.0026991488717855007], [-0.0009496510997198135, 0.002699452079922895, 0.9999961867215906]], 'translation vector': [-0.0035058102061285012, -0.0001503164701972537, 0.00022028015795205746]}\nC: {'rotation matrix': [[0.996877, -0.026735, -0.074307], [0.056551, -0.415107, 0.908013], [-0.055122, -0.909379, -0.412299]], 'translation vector': [4.407921, 5.402507, 1.494552]}\nD: {'rotation matrix': [[0.997138, -0.02412, -0.07165], [0.055312, -0.413324, 0.908903], [-0.051537, -0.910265, -0.410807]], 'translation vector': [4.410345, 5.401881, 1.497987]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_38_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_38_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_38_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_38_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.997087, -0.025415, -0.071922], [0.054952, -0.414609, 0.908339], [-0.052905, -0.909645, -0.412004]], 'translation vector': [4.407682, 5.403047, 1.49649]}\nB: {'rotation matrix': [[1.0000003154168173, -0.00026066196587795827, 0.0009499731630029714], [0.00026257414143552785, 0.9999962311537921, -0.0026991488717855007], [-0.0009496510997198135, 0.002699452079922895, 0.9999961867215906]], 'translation vector': [-0.0035058102061285012, -0.0001503164701972537, 0.00022028015795205746]}\nC: {'rotation matrix': [[0.996877, -0.026735, -0.074307], [0.056551, -0.415107, 0.908013], [-0.055122, -0.909379, -0.412299]], 'translation vector': [4.407921, 5.402507, 1.494552]}\nD: {'rotation matrix': [[0.997138, -0.02412, -0.07165], [0.055312, -0.413324, 0.908903], [-0.051537, -0.910265, -0.410807]], 'translation vector': [4.410345, 5.401881, 1.497987]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.317061, -0.465845, 0.826112], [-0.947162, 0.200121, -0.250671], [-0.048548, -0.86194, -0.504681]], 'translation vector': [2.298134, 2.388596, 1.453916]}\nB: {'rotation matrix': [[-0.317304, -0.461983, 0.828185], [-0.946993, 0.200626, -0.250908], [-0.05024, -0.863899, -0.501153]], 'translation vector': [2.298876, 2.392571, 1.455489]}\nC: {'rotation matrix': [[0.99999454935107, 0.0002197888751369094, -0.0032128422811739327], [-0.00022937596583946223, 0.9999976816384528, -0.0024943946424807743], [0.0032131009034445414, 0.0024955910558726972, 0.9999912821080568]], 'translation vector': [0.001089045909415276, -0.0004423003337574727, -0.0002086110960047849]}\nD: {'rotation matrix': [[-0.314906, -0.456701, 0.83202], [-0.947639, 0.200286, -0.248728], [-0.053048, -0.866781, -0.49586]], 'translation vector': [2.297376, 2.389925, 1.457247]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_39_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_39_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_39_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_39_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.317061, -0.465845, 0.826112], [-0.947162, 0.200121, -0.250671], [-0.048548, -0.86194, -0.504681]], 'translation vector': [2.298134, 2.388596, 1.453916]}\nB: {'rotation matrix': [[-0.317304, -0.461983, 0.828185], [-0.946993, 0.200626, -0.250908], [-0.05024, -0.863899, -0.501153]], 'translation vector': [2.298876, 2.392571, 1.455489]}\nC: {'rotation matrix': [[0.99999454935107, 0.0002197888751369094, -0.0032128422811739327], [-0.00022937596583946223, 0.9999976816384528, -0.0024943946424807743], [0.0032131009034445414, 0.0024955910558726972, 0.9999912821080568]], 'translation vector': [0.001089045909415276, -0.0004423003337574727, -0.0002086110960047849]}\nD: {'rotation matrix': [[-0.314906, -0.456701, 0.83202], [-0.947639, 0.200286, -0.248728], [-0.053048, -0.866781, -0.49586]], 'translation vector': [2.297376, 2.389925, 1.457247]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.887986, -0.072993, 0.45404], [-0.454573, -0.288743, 0.84261], [0.069596, -0.95462, -0.28958]], 'translation vector': [3.216625, 3.12153, 1.569232]}\nB: {'rotation matrix': [[0.881294, -0.091283, 0.463668], [-0.468392, -0.298889, 0.831429], [0.06269, -0.949912, -0.306165]], 'translation vector': [3.22503, 3.133041, 1.572641]}\nC: {'rotation matrix': [[0.9998534926564413, 0.011234421547280734, -0.012941695383424894], [-0.01131150095792877, 0.9999182350466784, -0.005915937307264116], [0.012873405071388795, 0.006062261057420049, 0.9998987281608317]], 'translation vector': [-0.0006289172879170302, -0.011376901257172278, -0.010410317713380746]}\nD: {'rotation matrix': [[0.883743, -0.084646, 0.460254], [-0.463409, -0.29531, 0.83549], [0.065197, -0.951644, -0.300204]], 'translation vector': [3.211292, 3.12843, 1.571525]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_40_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_40_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_40_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_40_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.887986, -0.072993, 0.45404], [-0.454573, -0.288743, 0.84261], [0.069596, -0.95462, -0.28958]], 'translation vector': [3.216625, 3.12153, 1.569232]}\nB: {'rotation matrix': [[0.881294, -0.091283, 0.463668], [-0.468392, -0.298889, 0.831429], [0.06269, -0.949912, -0.306165]], 'translation vector': [3.22503, 3.133041, 1.572641]}\nC: {'rotation matrix': [[0.9998534926564413, 0.011234421547280734, -0.012941695383424894], [-0.01131150095792877, 0.9999182350466784, -0.005915937307264116], [0.012873405071388795, 0.006062261057420049, 0.9998987281608317]], 'translation vector': [-0.0006289172879170302, -0.011376901257172278, -0.010410317713380746]}\nD: {'rotation matrix': [[0.883743, -0.084646, 0.460254], [-0.463409, -0.29531, 0.83549], [0.065197, -0.951644, -0.300204]], 'translation vector': [3.211292, 3.12843, 1.571525]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999922215867046, 0.00022785537094286064, 0.0038428059210989744], [-0.0002767566400686762, 0.9999180395239896, 0.0128255569964717], [-0.0038395911947271166, -0.01282698821636408, 0.9999095074136637]], 'translation vector': [-0.012178319620715694, 0.0009877403245357463, 0.004998693428563072]}\nB: {'rotation matrix': [[-0.349791, 0.571502, -0.742315], [0.927295, 0.098467, -0.361147], [-0.133303, -0.814672, -0.564394]], 'translation vector': [7.153554, 3.625007, 1.584927]}\nC: {'rotation matrix': [[-0.352738, 0.563812, -0.746788], [0.92567, 0.093586, -0.366575], [-0.13679, -0.820584, -0.554915]], 'translation vector': [7.154875, 3.637451, 1.583088]}\nD: {'rotation matrix': [[-0.346362, 0.577228, -0.739487], [0.929238, 0.103006, -0.354833], [-0.128648, -0.81006, -0.57206]], 'translation vector': [7.154236, 3.613202, 1.583063]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_41_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_41_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_41_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_41_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999922215867046, 0.00022785537094286064, 0.0038428059210989744], [-0.0002767566400686762, 0.9999180395239896, 0.0128255569964717], [-0.0038395911947271166, -0.01282698821636408, 0.9999095074136637]], 'translation vector': [-0.012178319620715694, 0.0009877403245357463, 0.004998693428563072]}\nB: {'rotation matrix': [[-0.349791, 0.571502, -0.742315], [0.927295, 0.098467, -0.361147], [-0.133303, -0.814672, -0.564394]], 'translation vector': [7.153554, 3.625007, 1.584927]}\nC: {'rotation matrix': [[-0.352738, 0.563812, -0.746788], [0.92567, 0.093586, -0.366575], [-0.13679, -0.820584, -0.554915]], 'translation vector': [7.154875, 3.637451, 1.583088]}\nD: {'rotation matrix': [[-0.346362, 0.577228, -0.739487], [0.929238, 0.103006, -0.354833], [-0.128648, -0.81006, -0.57206]], 'translation vector': [7.154236, 3.613202, 1.583063]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.295342, -0.337479, 0.893802], [-0.954338, -0.060275, 0.292587], [-0.044868, -0.939402, -0.33987]], 'translation vector': [3.757863, 4.507889, 1.342911]}\nB: {'rotation matrix': [[0.300826, -0.332026, 0.894015], [-0.952739, -0.063015, 0.297182], [-0.042336, -0.941163, -0.33529]], 'translation vector': [3.757184, 4.502328, 1.344268]}\nC: {'rotation matrix': [[0.280244, -0.341756, 0.897032], [-0.959084, -0.060487, 0.276585], [-0.040265, -0.93784, -0.344724]], 'translation vector': [3.749212, 4.541941, 1.346336]}\nD: {'rotation matrix': [[0.9996750004430802, 0.005976407862532351, -0.024761409167148862], [-0.005880950295372731, 0.9999756244730406, 0.0039275528492273585], [0.024784884729748376, -0.0037817785482656863, 0.9996858791488856]], 'translation vector': [0.014484406993793275, 0.0006255655612603661, -0.006516735656635575]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_42_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_42_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_42_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_42_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.295342, -0.337479, 0.893802], [-0.954338, -0.060275, 0.292587], [-0.044868, -0.939402, -0.33987]], 'translation vector': [3.757863, 4.507889, 1.342911]}\nB: {'rotation matrix': [[0.300826, -0.332026, 0.894015], [-0.952739, -0.063015, 0.297182], [-0.042336, -0.941163, -0.33529]], 'translation vector': [3.757184, 4.502328, 1.344268]}\nC: {'rotation matrix': [[0.280244, -0.341756, 0.897032], [-0.959084, -0.060487, 0.276585], [-0.040265, -0.93784, -0.344724]], 'translation vector': [3.749212, 4.541941, 1.346336]}\nD: {'rotation matrix': [[0.9996750004430802, 0.005976407862532351, -0.024761409167148862], [-0.005880950295372731, 0.9999756244730406, 0.0039275528492273585], [0.024784884729748376, -0.0037817785482656863, 0.9996858791488856]], 'translation vector': [0.014484406993793275, 0.0006255655612603661, -0.006516735656635575]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.408105, -0.298824, 0.862644], [-0.912691, -0.155393, 0.377953], [0.021107, -0.941572, -0.33615]], 'translation vector': [3.68854, 2.987475, 1.504179]}\nB: {'rotation matrix': [[0.414415, -0.280989, 0.865625], [-0.909796, -0.152002, 0.386221], [0.023053, -0.947597, -0.318634]], 'translation vector': [3.695469, 2.977012, 1.528306]}\nC: {'rotation matrix': [[0.9999691985275884, -0.0028723049102575577, -0.007257311467463551], [0.0029984988555096107, 0.9998441859378951, 0.01739980616527257], [0.007206986340996781, -0.017422228683580315, 0.9998224150329578]], 'translation vector': [0.005083024714500617, 0.008976294562036191, -0.004546920528859744]}\nD: {'rotation matrix': [[0.410301, -0.290948, 0.864293], [-0.911655, -0.15496, 0.38062], [0.02319, -0.944106, -0.328824]], 'translation vector': [3.694328, 2.984669, 1.517045]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_43_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_43_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_43_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_43_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.408105, -0.298824, 0.862644], [-0.912691, -0.155393, 0.377953], [0.021107, -0.941572, -0.33615]], 'translation vector': [3.68854, 2.987475, 1.504179]}\nB: {'rotation matrix': [[0.414415, -0.280989, 0.865625], [-0.909796, -0.152002, 0.386221], [0.023053, -0.947597, -0.318634]], 'translation vector': [3.695469, 2.977012, 1.528306]}\nC: {'rotation matrix': [[0.9999691985275884, -0.0028723049102575577, -0.007257311467463551], [0.0029984988555096107, 0.9998441859378951, 0.01739980616527257], [0.007206986340996781, -0.017422228683580315, 0.9998224150329578]], 'translation vector': [0.005083024714500617, 0.008976294562036191, -0.004546920528859744]}\nD: {'rotation matrix': [[0.410301, -0.290948, 0.864293], [-0.911655, -0.15496, 0.38062], [0.02319, -0.944106, -0.328824]], 'translation vector': [3.694328, 2.984669, 1.517045]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.794148, 0.542055, -0.274783], [0.607642, 0.715697, -0.34431], [0.010026, -0.440403, -0.897744]], 'translation vector': [2.029685, 2.312871, 1.199782]}\nB: {'rotation matrix': [[-0.789274, 0.545355, -0.282196], [0.613822, 0.713018, -0.338862], [0.016411, -0.440674, -0.897518]], 'translation vector': [2.029754, 2.312013, 1.198812]}\nC: {'rotation matrix': [[-0.792558, 0.542991, -0.277512], [0.609667, 0.714954, -0.342268], [0.01256, -0.440457, -0.897686]], 'translation vector': [2.028831, 2.312793, 1.199579]}\nD: {'rotation matrix': [[0.9999690183091173, 0.00758939386438217, -0.0016348482581436392], [-0.007592849178659918, 0.9999686021169869, -0.0023322818814674375], [0.0016172805944689142, 0.0023449024761050914, 0.9999961697739403]], 'translation vector': [-0.0009595698380654716, -0.001243015152278204, -0.0008030280503015241]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_44_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_44_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_44_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_44_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.794148, 0.542055, -0.274783], [0.607642, 0.715697, -0.34431], [0.010026, -0.440403, -0.897744]], 'translation vector': [2.029685, 2.312871, 1.199782]}\nB: {'rotation matrix': [[-0.789274, 0.545355, -0.282196], [0.613822, 0.713018, -0.338862], [0.016411, -0.440674, -0.897518]], 'translation vector': [2.029754, 2.312013, 1.198812]}\nC: {'rotation matrix': [[-0.792558, 0.542991, -0.277512], [0.609667, 0.714954, -0.342268], [0.01256, -0.440457, -0.897686]], 'translation vector': [2.028831, 2.312793, 1.199579]}\nD: {'rotation matrix': [[0.9999690183091173, 0.00758939386438217, -0.0016348482581436392], [-0.007592849178659918, 0.9999686021169869, -0.0023322818814674375], [0.0016172805944689142, 0.0023449024761050914, 0.9999961697739403]], 'translation vector': [-0.0009595698380654716, -0.001243015152278204, -0.0008030280503015241]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.595869, 0.488846, -0.637158], [0.802996, -0.351087, 0.481596], [0.011728, -0.798604, -0.601743]], 'translation vector': [3.453696, 1.113575, 1.412785]}\nB: {'rotation matrix': [[0.596116, 0.489381, -0.636516], [0.802814, -0.351792, 0.481386], [0.01166, -0.797966, -0.60259]], 'translation vector': [3.45192, 1.112521, 1.411639]}\nC: {'rotation matrix': [[0.9999972585421386, -0.0019706644639079346, -0.0016038162580140711], [0.0019696489330632795, 0.9999981939869151, -0.0004898637930363391], [0.00160459924260016, 0.0004861135599392089, 0.9999985246041514]], 'translation vector': [0.0004367632436044211, -0.0013470306629629059, 0.001255614697354801]}\nD: {'rotation matrix': [[0.596167, 0.487305, -0.638059], [0.802791, -0.351322, 0.481768], [0.010604, -0.799442, -0.60065]], 'translation vector': [3.452477, 1.114933, 1.412574]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_45_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_45_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_45_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_45_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.595869, 0.488846, -0.637158], [0.802996, -0.351087, 0.481596], [0.011728, -0.798604, -0.601743]], 'translation vector': [3.453696, 1.113575, 1.412785]}\nB: {'rotation matrix': [[0.596116, 0.489381, -0.636516], [0.802814, -0.351792, 0.481386], [0.01166, -0.797966, -0.60259]], 'translation vector': [3.45192, 1.112521, 1.411639]}\nC: {'rotation matrix': [[0.9999972585421386, -0.0019706644639079346, -0.0016038162580140711], [0.0019696489330632795, 0.9999981939869151, -0.0004898637930363391], [0.00160459924260016, 0.0004861135599392089, 0.9999985246041514]], 'translation vector': [0.0004367632436044211, -0.0013470306629629059, 0.001255614697354801]}\nD: {'rotation matrix': [[0.596167, 0.487305, -0.638059], [0.802791, -0.351322, 0.481768], [0.010604, -0.799442, -0.60065]], 'translation vector': [3.452477, 1.114933, 1.412574]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.454351, -0.425578, 0.782591], [-0.890802, 0.223047, -0.395881], [-0.006077, -0.877003, -0.480447]], 'translation vector': [2.248463, 3.862178, 1.517095]}\nB: {'rotation matrix': [[-0.455273, -0.423684, 0.783083], [-0.890312, 0.224946, -0.395908], [-0.008411, -0.877434, -0.479623]], 'translation vector': [2.248543, 3.862554, 1.517483]}\nC: {'rotation matrix': [[0.9999998220095317, 0.00019344203418205028, 0.0003904002954544705], [-0.00019414720576528636, 0.99999726822095, 0.002542005647158053], [-0.00038905761812178927, -0.002542402173661647, 0.9999965389998547]], 'translation vector': [-0.0007329855911066829, -0.0003036787606989222, 0.00038766167203613255]}\nD: {'rotation matrix': [[-0.455182, -0.424042, 0.782942], [-0.890372, 0.223522, -0.39658], [-0.006838, -0.877625, -0.479299]], 'translation vector': [2.247845, 3.863035, 1.516836]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_46_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_46_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_46_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_46_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.454351, -0.425578, 0.782591], [-0.890802, 0.223047, -0.395881], [-0.006077, -0.877003, -0.480447]], 'translation vector': [2.248463, 3.862178, 1.517095]}\nB: {'rotation matrix': [[-0.455273, -0.423684, 0.783083], [-0.890312, 0.224946, -0.395908], [-0.008411, -0.877434, -0.479623]], 'translation vector': [2.248543, 3.862554, 1.517483]}\nC: {'rotation matrix': [[0.9999998220095317, 0.00019344203418205028, 0.0003904002954544705], [-0.00019414720576528636, 0.99999726822095, 0.002542005647158053], [-0.00038905761812178927, -0.002542402173661647, 0.9999965389998547]], 'translation vector': [-0.0007329855911066829, -0.0003036787606989222, 0.00038766167203613255]}\nD: {'rotation matrix': [[-0.455182, -0.424042, 0.782942], [-0.890372, 0.223522, -0.39658], [-0.006838, -0.877625, -0.479299]], 'translation vector': [2.247845, 3.863035, 1.516836]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.503003, -0.389292, 0.771648], [-0.863104, 0.179601, -0.472011], [0.045161, -0.903435, -0.426339]], 'translation vector': [8.991447, 2.792113, 1.935809]}\nB: {'rotation matrix': [[0.9998820105783071, 0.006892269339613531, -0.013715431860878731], [-0.006800370949834618, 0.9999536838119013, 0.00673862053023821], [0.013761408703211613, -0.006644159929287518, 0.9998834142169373]], 'translation vector': [0.0018210588463203337, -0.0009922093443961444, -0.010804184818734797]}\nC: {'rotation matrix': [[-0.507392, -0.392271, 0.767253], [-0.860549, 0.184347, -0.474839], [0.044825, -0.901188, -0.431104]], 'translation vector': [8.996889, 2.787546, 1.938329]}\nD: {'rotation matrix': [[-0.511945, -0.391945, 0.76439], [-0.857826, 0.186392, -0.47895], [0.045246, -0.900909, -0.431643]], 'translation vector': [9.004251, 2.788493, 1.934378]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_47_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_47_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_47_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_47_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.503003, -0.389292, 0.771648], [-0.863104, 0.179601, -0.472011], [0.045161, -0.903435, -0.426339]], 'translation vector': [8.991447, 2.792113, 1.935809]}\nB: {'rotation matrix': [[0.9998820105783071, 0.006892269339613531, -0.013715431860878731], [-0.006800370949834618, 0.9999536838119013, 0.00673862053023821], [0.013761408703211613, -0.006644159929287518, 0.9998834142169373]], 'translation vector': [0.0018210588463203337, -0.0009922093443961444, -0.010804184818734797]}\nC: {'rotation matrix': [[-0.507392, -0.392271, 0.767253], [-0.860549, 0.184347, -0.474839], [0.044825, -0.901188, -0.431104]], 'translation vector': [8.996889, 2.787546, 1.938329]}\nD: {'rotation matrix': [[-0.511945, -0.391945, 0.76439], [-0.857826, 0.186392, -0.47895], [0.045246, -0.900909, -0.431643]], 'translation vector': [9.004251, 2.788493, 1.934378]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.347731, 0.021734, -0.937343], [0.937583, -0.012995, 0.347518], [-0.004627, -0.999679, -0.024896]], 'translation vector': [3.086271, 2.7877, 1.609772]}\nB: {'rotation matrix': [[0.349639, 0.022266, -0.93662], [0.936876, -0.012692, 0.349433], [-0.004108, -0.999671, -0.025298]], 'translation vector': [3.085923, 2.787744, 1.608445]}\nC: {'rotation matrix': [[0.9999983503028658, 0.0012588328917477426, 0.0010046121758577645], [-0.001258491549862339, 0.999999582567488, 0.0007249187267349242], [-0.0010031229358240259, -0.0007259449856603071, 0.9999988669360486]], 'translation vector': [-0.0007969980165536406, 0.0011986171600251172, 0.00041117594518969014]}\nD: {'rotation matrix': [[0.348071, 0.022212, -0.937205], [0.937459, -0.012734, 0.347863], [-0.004208, -0.999672, -0.025256]], 'translation vector': [3.0862, 2.78781, 1.60897]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_48_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_48_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_48_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_48_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.347731, 0.021734, -0.937343], [0.937583, -0.012995, 0.347518], [-0.004627, -0.999679, -0.024896]], 'translation vector': [3.086271, 2.7877, 1.609772]}\nB: {'rotation matrix': [[0.349639, 0.022266, -0.93662], [0.936876, -0.012692, 0.349433], [-0.004108, -0.999671, -0.025298]], 'translation vector': [3.085923, 2.787744, 1.608445]}\nC: {'rotation matrix': [[0.9999983503028658, 0.0012588328917477426, 0.0010046121758577645], [-0.001258491549862339, 0.999999582567488, 0.0007249187267349242], [-0.0010031229358240259, -0.0007259449856603071, 0.9999988669360486]], 'translation vector': [-0.0007969980165536406, 0.0011986171600251172, 0.00041117594518969014]}\nD: {'rotation matrix': [[0.348071, 0.022212, -0.937205], [0.937459, -0.012734, 0.347863], [-0.004208, -0.999672, -0.025256]], 'translation vector': [3.0862, 2.78781, 1.60897]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.671878, -0.274721, 0.687829], [-0.740313, -0.220562, 0.635051], [-0.022753, -0.935885, -0.35157]], 'translation vector': [3.807358, 2.10759, 1.500018]}\nB: {'rotation matrix': [[0.9999995287042943, -0.00016620863165950158, -1.0138120576769867e-05], [0.00016705647891749608, 0.9999995162619548, -0.0013603662243301747], [1.062657980571793e-05, 0.001360311886111166, 0.9999990686101642]], 'translation vector': [-0.004297820144825049, 0.003351067382226791, -0.0005408272137925607]}\nC: {'rotation matrix': [[0.670129, -0.272494, 0.690416], [-0.741875, -0.216557, 0.634606], [-0.023412, -0.93747, -0.347278]], 'translation vector': [3.805446, 2.107442, 1.49456]}\nD: {'rotation matrix': [[0.670813, -0.272809, 0.689627], [-0.741265, -0.217599, 0.634962], [-0.023161, -0.937137, -0.348192]], 'translation vector': [3.805646, 2.107794, 1.49708]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_49_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_49_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_49_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_49_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.671878, -0.274721, 0.687829], [-0.740313, -0.220562, 0.635051], [-0.022753, -0.935885, -0.35157]], 'translation vector': [3.807358, 2.10759, 1.500018]}\nB: {'rotation matrix': [[0.9999995287042943, -0.00016620863165950158, -1.0138120576769867e-05], [0.00016705647891749608, 0.9999995162619548, -0.0013603662243301747], [1.062657980571793e-05, 0.001360311886111166, 0.9999990686101642]], 'translation vector': [-0.004297820144825049, 0.003351067382226791, -0.0005408272137925607]}\nC: {'rotation matrix': [[0.670129, -0.272494, 0.690416], [-0.741875, -0.216557, 0.634606], [-0.023412, -0.93747, -0.347278]], 'translation vector': [3.805446, 2.107442, 1.49456]}\nD: {'rotation matrix': [[0.670813, -0.272809, 0.689627], [-0.741265, -0.217599, 0.634962], [-0.023161, -0.937137, -0.348192]], 'translation vector': [3.805646, 2.107794, 1.49708]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.479262, 0.501356, -0.720382], [0.874939, 0.337636, -0.347107], [0.069203, -0.796646, -0.600472]], 'translation vector': [2.874844, 0.864648, 1.19894]}\nB: {'rotation matrix': [[-0.476726, 0.503154, -0.720812], [0.876238, 0.337562, -0.343889], [0.070289, -0.795543, -0.601806]], 'translation vector': [2.872792, 0.865184, 1.200293]}\nC: {'rotation matrix': [[-0.480917, 0.499307, -0.720702], [0.874259, 0.335212, -0.351147], [0.066258, -0.798953, -0.597732]], 'translation vector': [2.877507, 0.861745, 1.198945]}\nD: {'rotation matrix': [[0.9999458075169825, -0.0037298030767581817, 0.009658870905228063], [0.003830453413616424, 0.9999377550296561, -0.010410573829210937], [-0.009619405173591757, 0.010446297554888797, 0.999899528124321]], 'translation vector': [0.004156407549993579, -0.0031544955662062835, 0.0021419719379069946]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_50_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_50_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_50_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_50_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.479262, 0.501356, -0.720382], [0.874939, 0.337636, -0.347107], [0.069203, -0.796646, -0.600472]], 'translation vector': [2.874844, 0.864648, 1.19894]}\nB: {'rotation matrix': [[-0.476726, 0.503154, -0.720812], [0.876238, 0.337562, -0.343889], [0.070289, -0.795543, -0.601806]], 'translation vector': [2.872792, 0.865184, 1.200293]}\nC: {'rotation matrix': [[-0.480917, 0.499307, -0.720702], [0.874259, 0.335212, -0.351147], [0.066258, -0.798953, -0.597732]], 'translation vector': [2.877507, 0.861745, 1.198945]}\nD: {'rotation matrix': [[0.9999458075169825, -0.0037298030767581817, 0.009658870905228063], [0.003830453413616424, 0.9999377550296561, -0.010410573829210937], [-0.009619405173591757, 0.010446297554888797, 0.999899528124321]], 'translation vector': [0.004156407549993579, -0.0031544955662062835, 0.0021419719379069946]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.408839, -0.490635, 0.769499], [-0.912309, 0.19818, -0.358354], [0.023322, -0.848529, -0.528634]], 'translation vector': [0.933751, 3.504875, 1.495928]}\nB: {'rotation matrix': [[-0.405517, -0.49009, 0.771601], [-0.913834, 0.197477, -0.354839], [0.02153, -0.849008, -0.527941]], 'translation vector': [0.92311, 3.508826, 1.494794]}\nC: {'rotation matrix': [[-0.406934, -0.490269, 0.770741], [-0.913168, 0.197092, -0.356762], [0.023003, -0.848994, -0.527902]], 'translation vector': [0.928096, 3.507151, 1.495325]}\nD: {'rotation matrix': [[0.9999720790780128, 0.0021449080565870337, 0.007189565686557225], [-0.0021449387549180303, 0.9999977939892998, -4.554193667967342e-05], [-0.007190098560018757, 3.036958542106401e-05, 0.9999745575458397]], 'translation vector': [-0.0022708049168844724, -0.01148622047209824, 0.014300413115339472]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_51_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_51_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_51_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_51_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.408839, -0.490635, 0.769499], [-0.912309, 0.19818, -0.358354], [0.023322, -0.848529, -0.528634]], 'translation vector': [0.933751, 3.504875, 1.495928]}\nB: {'rotation matrix': [[-0.405517, -0.49009, 0.771601], [-0.913834, 0.197477, -0.354839], [0.02153, -0.849008, -0.527941]], 'translation vector': [0.92311, 3.508826, 1.494794]}\nC: {'rotation matrix': [[-0.406934, -0.490269, 0.770741], [-0.913168, 0.197092, -0.356762], [0.023003, -0.848994, -0.527902]], 'translation vector': [0.928096, 3.507151, 1.495325]}\nD: {'rotation matrix': [[0.9999720790780128, 0.0021449080565870337, 0.007189565686557225], [-0.0021449387549180303, 0.9999977939892998, -4.554193667967342e-05], [-0.007190098560018757, 3.036958542106401e-05, 0.9999745575458397]], 'translation vector': [-0.0022708049168844724, -0.01148622047209824, 0.014300413115339472]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.812183, -0.277025, 0.513436], [-0.583176, 0.410049, -0.70126], [-0.016267, -0.868975, -0.494589]], 'translation vector': [4.864513, 2.490983, 1.398342]}\nB: {'rotation matrix': [[-0.813534, -0.276094, 0.511796], [-0.581281, 0.411223, -0.702146], [-0.016604, -0.868716, -0.495032]], 'translation vector': [4.865518, 2.490622, 1.399591]}\nC: {'rotation matrix': [[0.999997135129029, 0.001605805084179667, 0.0018057529572375504], [-0.0016158596045621908, 0.9999825699875163, 0.0055795127618636095], [-0.0017979244064656866, -0.005583390464990996, 0.9999826090372864]], 'translation vector': [-0.008521917625347264, -0.003245150959530152, 0.000826648743016356]}\nD: {'rotation matrix': [[-0.814102, -0.273924, 0.512058], [-0.580426, 0.411969, -0.702416], [-0.018543, -0.86905, -0.494377]], 'translation vector': [4.865249, 2.49003, 1.4009]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_52_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_52_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_52_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_52_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.812183, -0.277025, 0.513436], [-0.583176, 0.410049, -0.70126], [-0.016267, -0.868975, -0.494589]], 'translation vector': [4.864513, 2.490983, 1.398342]}\nB: {'rotation matrix': [[-0.813534, -0.276094, 0.511796], [-0.581281, 0.411223, -0.702146], [-0.016604, -0.868716, -0.495032]], 'translation vector': [4.865518, 2.490622, 1.399591]}\nC: {'rotation matrix': [[0.999997135129029, 0.001605805084179667, 0.0018057529572375504], [-0.0016158596045621908, 0.9999825699875163, 0.0055795127618636095], [-0.0017979244064656866, -0.005583390464990996, 0.9999826090372864]], 'translation vector': [-0.008521917625347264, -0.003245150959530152, 0.000826648743016356]}\nD: {'rotation matrix': [[-0.814102, -0.273924, 0.512058], [-0.580426, 0.411969, -0.702416], [-0.018543, -0.86905, -0.494377]], 'translation vector': [4.865249, 2.49003, 1.4009]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.065966, 0.424992, -0.90279], [0.996327, -0.077558, 0.03629], [-0.054596, -0.901868, -0.428547]], 'translation vector': [3.688125, 7.382969, 1.65232]}\nB: {'rotation matrix': [[0.06445, 0.42596, -0.902444], [0.996336, -0.078418, 0.034141], [-0.056225, -0.901337, -0.429453]], 'translation vector': [3.691079, 7.385597, 1.655249]}\nC: {'rotation matrix': [[0.065269, 0.425434, -0.902633], [0.996301, -0.078461, 0.035062], [-0.055905, -0.901582, -0.428981]], 'translation vector': [3.688166, 7.384302, 1.653993]}\nD: {'rotation matrix': [[0.9999823018718307, 0.004888113491700664, -0.003558790677819051], [-0.004889637706536031, 0.9999888180833659, -0.00037767294265026564], [0.0035570250944470354, 0.00039493287161776724, 0.9999941352091379]], 'translation vector': [-0.0029368758378494064, 0.0007877967404965047, -0.003178400438716089]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_53_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_53_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_53_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_53_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.065966, 0.424992, -0.90279], [0.996327, -0.077558, 0.03629], [-0.054596, -0.901868, -0.428547]], 'translation vector': [3.688125, 7.382969, 1.65232]}\nB: {'rotation matrix': [[0.06445, 0.42596, -0.902444], [0.996336, -0.078418, 0.034141], [-0.056225, -0.901337, -0.429453]], 'translation vector': [3.691079, 7.385597, 1.655249]}\nC: {'rotation matrix': [[0.065269, 0.425434, -0.902633], [0.996301, -0.078461, 0.035062], [-0.055905, -0.901582, -0.428981]], 'translation vector': [3.688166, 7.384302, 1.653993]}\nD: {'rotation matrix': [[0.9999823018718307, 0.004888113491700664, -0.003558790677819051], [-0.004889637706536031, 0.9999888180833659, -0.00037767294265026564], [0.0035570250944470354, 0.00039493287161776724, 0.9999941352091379]], 'translation vector': [-0.0029368758378494064, 0.0007877967404965047, -0.003178400438716089]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.664692, -0.099755, 0.740428], [-0.744592, -0.007027, 0.667483], [-0.061382, -0.994987, -0.078948]], 'translation vector': [3.729187, 1.43308, 1.737059]}\nB: {'rotation matrix': [[0.660717, -0.100231, 0.743913], [-0.748068, -0.006018, 0.663595], [-0.062036, -0.994946, -0.078955]], 'translation vector': [3.728547, 1.433503, 1.735599]}\nC: {'rotation matrix': [[0.9999743209792964, 0.004172766279522147, 0.005832278992731541], [-0.004189646737158265, 0.9999872000751636, 0.0029117442423506165], [-0.0058200970417569145, -0.0029366540837721536, 0.9999788200004149]], 'translation vector': [0.0013138555211342773, -0.001864474585307807, 0.0012853107981345424]}\nD: {'rotation matrix': [[0.656146, -0.099388, 0.74806], [-0.75226, -0.007571, 0.658823], [-0.059815, -0.99502, -0.079733]], 'translation vector': [3.729275, 1.433124, 1.734442]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_54_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_54_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_54_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_54_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.664692, -0.099755, 0.740428], [-0.744592, -0.007027, 0.667483], [-0.061382, -0.994987, -0.078948]], 'translation vector': [3.729187, 1.43308, 1.737059]}\nB: {'rotation matrix': [[0.660717, -0.100231, 0.743913], [-0.748068, -0.006018, 0.663595], [-0.062036, -0.994946, -0.078955]], 'translation vector': [3.728547, 1.433503, 1.735599]}\nC: {'rotation matrix': [[0.9999743209792964, 0.004172766279522147, 0.005832278992731541], [-0.004189646737158265, 0.9999872000751636, 0.0029117442423506165], [-0.0058200970417569145, -0.0029366540837721536, 0.9999788200004149]], 'translation vector': [0.0013138555211342773, -0.001864474585307807, 0.0012853107981345424]}\nD: {'rotation matrix': [[0.656146, -0.099388, 0.74806], [-0.75226, -0.007571, 0.658823], [-0.059815, -0.99502, -0.079733]], 'translation vector': [3.729275, 1.433124, 1.734442]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.482382, -0.62548, 0.613257], [-0.875317, -0.317346, 0.364844], [-0.033588, -0.712788, -0.700575]], 'translation vector': [-0.164493, 3.070356, 1.320176]}\nB: {'rotation matrix': [[0.482432, -0.626604, 0.612068], [-0.875266, -0.317577, 0.364766], [-0.034185, -0.711697, -0.701654]], 'translation vector': [-0.163574, 3.070977, 1.321051]}\nC: {'rotation matrix': [[0.9999881393517817, -0.00035757344474597234, -0.005016643883040467], [0.0003166564592981544, 0.999967468385718, -0.008020454596773861], [0.005019965204090123, 0.008019005544894866, 0.9999547455108736]], 'translation vector': [-0.0028720129328290156, -0.004572041684123063, -0.0005047793498673403]}\nD: {'rotation matrix': [[0.482883, -0.62302, 0.615362], [-0.875067, -0.316913, 0.36582], [-0.032897, -0.715131, -0.698216]], 'translation vector': [-0.165581, 3.069752, 1.319227]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_55_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_55_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_55_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_55_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.482382, -0.62548, 0.613257], [-0.875317, -0.317346, 0.364844], [-0.033588, -0.712788, -0.700575]], 'translation vector': [-0.164493, 3.070356, 1.320176]}\nB: {'rotation matrix': [[0.482432, -0.626604, 0.612068], [-0.875266, -0.317577, 0.364766], [-0.034185, -0.711697, -0.701654]], 'translation vector': [-0.163574, 3.070977, 1.321051]}\nC: {'rotation matrix': [[0.9999881393517817, -0.00035757344474597234, -0.005016643883040467], [0.0003166564592981544, 0.999967468385718, -0.008020454596773861], [0.005019965204090123, 0.008019005544894866, 0.9999547455108736]], 'translation vector': [-0.0028720129328290156, -0.004572041684123063, -0.0005047793498673403]}\nD: {'rotation matrix': [[0.482883, -0.62302, 0.615362], [-0.875067, -0.316913, 0.36582], [-0.032897, -0.715131, -0.698216]], 'translation vector': [-0.165581, 3.069752, 1.319227]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.254741, -0.436809, 0.862731], [-0.966661, 0.138975, -0.215064], [-0.025957, -0.888754, -0.457649]], 'translation vector': [1.470391, 3.880589, 1.437084]}\nB: {'rotation matrix': [[-0.255547, -0.436424, 0.862688], [-0.966448, 0.139272, -0.215827], [-0.025956, -0.888897, -0.457372]], 'translation vector': [1.471235, 3.880077, 1.436326]}\nC: {'rotation matrix': [[-0.25517, -0.435877, 0.863076], [-0.966551, 0.138838, -0.215646], [-0.025832, -0.889233, -0.456724]], 'translation vector': [1.470861, 3.880012, 1.43644]}\nD: {'rotation matrix': [[0.9999966837298163, 0.0008619563673921012, 0.0026549103476475175], [-0.0008486493068450321, 0.999987344007734, -0.004872285830769469], [-0.0026592683333448467, 0.0048702326102797195, 0.9999843899240968]], 'translation vector': [-0.0017811924174484517, 0.006401158448355426, 0.0014093231794466143]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_56_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_56_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_56_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_56_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.254741, -0.436809, 0.862731], [-0.966661, 0.138975, -0.215064], [-0.025957, -0.888754, -0.457649]], 'translation vector': [1.470391, 3.880589, 1.437084]}\nB: {'rotation matrix': [[-0.255547, -0.436424, 0.862688], [-0.966448, 0.139272, -0.215827], [-0.025956, -0.888897, -0.457372]], 'translation vector': [1.471235, 3.880077, 1.436326]}\nC: {'rotation matrix': [[-0.25517, -0.435877, 0.863076], [-0.966551, 0.138838, -0.215646], [-0.025832, -0.889233, -0.456724]], 'translation vector': [1.470861, 3.880012, 1.43644]}\nD: {'rotation matrix': [[0.9999966837298163, 0.0008619563673921012, 0.0026549103476475175], [-0.0008486493068450321, 0.999987344007734, -0.004872285830769469], [-0.0026592683333448467, 0.0048702326102797195, 0.9999843899240968]], 'translation vector': [-0.0017811924174484517, 0.006401158448355426, 0.0014093231794466143]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.877903, 0.287785, -0.382709], [0.47693, 0.596815, -0.645252], [0.042713, -0.748994, -0.661199]], 'translation vector': [3.16702, 3.626466, 1.453681]}\nB: {'rotation matrix': [[0.9999791193908465, -0.001916910413648059, -0.0061771002364880544], [0.001968865784627184, 0.9999613398790298, 0.008534807689413037], [0.006161535037181589, -0.008547114556570383, 0.9999441766313318]], 'translation vector': [0.0007167215000418725, 0.004111635033621663, 0.00058908656396639]}\nC: {'rotation matrix': [[-0.874912, 0.294682, -0.384306], [0.482557, 0.597398, -0.640512], [0.040836, -0.745841, -0.664871]], 'translation vector': [3.163697, 3.627347, 1.450583]}\nD: {'rotation matrix': [[-0.871313, 0.303569, -0.385564], [0.489353, 0.596266, -0.636396], [0.036709, -0.743177, -0.668087]], 'translation vector': [3.163155, 3.630899, 1.446354]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_57_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_57_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_57_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_57_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.877903, 0.287785, -0.382709], [0.47693, 0.596815, -0.645252], [0.042713, -0.748994, -0.661199]], 'translation vector': [3.16702, 3.626466, 1.453681]}\nB: {'rotation matrix': [[0.9999791193908465, -0.001916910413648059, -0.0061771002364880544], [0.001968865784627184, 0.9999613398790298, 0.008534807689413037], [0.006161535037181589, -0.008547114556570383, 0.9999441766313318]], 'translation vector': [0.0007167215000418725, 0.004111635033621663, 0.00058908656396639]}\nC: {'rotation matrix': [[-0.874912, 0.294682, -0.384306], [0.482557, 0.597398, -0.640512], [0.040836, -0.745841, -0.664871]], 'translation vector': [3.163697, 3.627347, 1.450583]}\nD: {'rotation matrix': [[-0.871313, 0.303569, -0.385564], [0.489353, 0.596266, -0.636396], [0.036709, -0.743177, -0.668087]], 'translation vector': [3.163155, 3.630899, 1.446354]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.078798, -0.401808, 0.912327], [-0.996147, 0.067086, -0.056491], [-0.038506, -0.913263, -0.405546]], 'translation vector': [2.214502, 1.810217, 1.39288]}\nB: {'rotation matrix': [[-0.078108, -0.404311, 0.91128], [-0.996161, 0.067892, -0.055261], [-0.039526, -0.912098, -0.408062]], 'translation vector': [2.215161, 1.809587, 1.395775]}\nC: {'rotation matrix': [[0.9999986851865202, 0.0008401734129283168, -0.0015770679658950186], [-0.0008378992163381685, 0.9999975925523196, 0.0021246735415499604], [0.0015786984008670537, -0.002123956111471475, 0.9999965494818978]], 'translation vector': [0.0020818624690659426, 0.003854659633225399, 0.00023093351122471795]}\nD: {'rotation matrix': [[-0.078693, -0.405741, 0.910594], [-0.996048, 0.069734, -0.055005], [-0.041182, -0.911324, -0.409625]], 'translation vector': [2.217248, 1.812374, 1.391779]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_58_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_58_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_58_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_58_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.078798, -0.401808, 0.912327], [-0.996147, 0.067086, -0.056491], [-0.038506, -0.913263, -0.405546]], 'translation vector': [2.214502, 1.810217, 1.39288]}\nB: {'rotation matrix': [[-0.078108, -0.404311, 0.91128], [-0.996161, 0.067892, -0.055261], [-0.039526, -0.912098, -0.408062]], 'translation vector': [2.215161, 1.809587, 1.395775]}\nC: {'rotation matrix': [[0.9999986851865202, 0.0008401734129283168, -0.0015770679658950186], [-0.0008378992163381685, 0.9999975925523196, 0.0021246735415499604], [0.0015786984008670537, -0.002123956111471475, 0.9999965494818978]], 'translation vector': [0.0020818624690659426, 0.003854659633225399, 0.00023093351122471795]}\nD: {'rotation matrix': [[-0.078693, -0.405741, 0.910594], [-0.996048, 0.069734, -0.055005], [-0.041182, -0.911324, -0.409625]], 'translation vector': [2.217248, 1.812374, 1.391779]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.529336, -0.227144, 0.817441], [-0.847759, 0.103788, -0.520128], [0.033303, -0.968315, -0.247502]], 'translation vector': [5.896173, 2.790533, 1.549775]}\nB: {'rotation matrix': [[0.9999873040333512, 0.004505537820277509, -0.002397070922509149], [-0.004504137793554688, 0.9999890779279523, 0.0010154010037770195], [0.0024022565915601136, -0.0010036320370672355, 0.999996209316038]], 'translation vector': [-0.001560160498486951, -0.0020049110640587564, -0.0017324624821037915]}\nC: {'rotation matrix': [[-0.531472, -0.2283, 0.815731], [-0.846401, 0.104685, -0.522156], [0.033813, -0.967947, -0.24887]], 'translation vector': [5.895259, 2.788617, 1.559572]}\nD: {'rotation matrix': [[-0.53062, -0.226646, 0.816746], [-0.846944, 0.10358, -0.521495], [0.033596, -0.968454, -0.246918]], 'translation vector': [5.896636, 2.790495, 1.551807]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_59_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_59_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_59_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_59_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.529336, -0.227144, 0.817441], [-0.847759, 0.103788, -0.520128], [0.033303, -0.968315, -0.247502]], 'translation vector': [5.896173, 2.790533, 1.549775]}\nB: {'rotation matrix': [[0.9999873040333512, 0.004505537820277509, -0.002397070922509149], [-0.004504137793554688, 0.9999890779279523, 0.0010154010037770195], [0.0024022565915601136, -0.0010036320370672355, 0.999996209316038]], 'translation vector': [-0.001560160498486951, -0.0020049110640587564, -0.0017324624821037915]}\nC: {'rotation matrix': [[-0.531472, -0.2283, 0.815731], [-0.846401, 0.104685, -0.522156], [0.033813, -0.967947, -0.24887]], 'translation vector': [5.895259, 2.788617, 1.559572]}\nD: {'rotation matrix': [[-0.53062, -0.226646, 0.816746], [-0.846944, 0.10358, -0.521495], [0.033596, -0.968454, -0.246918]], 'translation vector': [5.896636, 2.790495, 1.551807]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999735234224584, -0.0071926018984539266, -0.00013127408012229648], [0.007193266878786128, 0.999950750728307, 0.006784512971560901], [8.152710211630049e-05, -0.006784805304509037, 0.9999770195971557]], 'translation vector': [-0.005319134943293502, -0.0035091612770564717, 0.0004269635666275251]}\nB: {'rotation matrix': [[-0.879528, -0.314344, 0.357236], [-0.47435, 0.638683, -0.605869], [-0.03771, -0.702334, -0.710848]], 'translation vector': [3.140295, 1.690182, 1.269802]}\nC: {'rotation matrix': [[-0.879673, -0.316123, 0.355304], [-0.474189, 0.640089, -0.604509], [-0.036327, -0.700252, -0.712971]], 'translation vector': [3.138628, 1.688987, 1.26968]}\nD: {'rotation matrix': [[-0.879671, -0.317176, 0.354371], [-0.474219, 0.641391, -0.603103], [-0.036001, -0.698582, -0.714624]], 'translation vector': [3.137942, 1.687445, 1.270163]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_60_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_60_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_60_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_60_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999735234224584, -0.0071926018984539266, -0.00013127408012229648], [0.007193266878786128, 0.999950750728307, 0.006784512971560901], [8.152710211630049e-05, -0.006784805304509037, 0.9999770195971557]], 'translation vector': [-0.005319134943293502, -0.0035091612770564717, 0.0004269635666275251]}\nB: {'rotation matrix': [[-0.879528, -0.314344, 0.357236], [-0.47435, 0.638683, -0.605869], [-0.03771, -0.702334, -0.710848]], 'translation vector': [3.140295, 1.690182, 1.269802]}\nC: {'rotation matrix': [[-0.879673, -0.316123, 0.355304], [-0.474189, 0.640089, -0.604509], [-0.036327, -0.700252, -0.712971]], 'translation vector': [3.138628, 1.688987, 1.26968]}\nD: {'rotation matrix': [[-0.879671, -0.317176, 0.354371], [-0.474219, 0.641391, -0.603103], [-0.036001, -0.698582, -0.714624]], 'translation vector': [3.137942, 1.687445, 1.270163]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.303413, -0.396105, 0.866627], [-0.952854, 0.129076, -0.274605], [-0.003088, -0.909088, -0.416593]], 'translation vector': [3.699021, 3.5579, 1.347225]}\nB: {'rotation matrix': [[-0.298621, -0.390383, 0.870877], [-0.954343, 0.1293, -0.26928], [-0.007482, -0.911527, -0.411171]], 'translation vector': [3.695972, 3.555829, 1.344301]}\nC: {'rotation matrix': [[-0.295385, -0.381895, 0.875731], [-0.955321, 0.128105, -0.266366], [-0.010462, -0.915284, -0.402672]], 'translation vector': [3.694636, 3.554343, 1.343555]}\nD: {'rotation matrix': [[0.9999470991216314, -8.645859163103856e-05, -0.010261215579303299], [0.00018576960757566105, 0.999954044961018, 0.009631176353467179], [0.010258362047762553, -0.00963308504359663, 0.9999007352405366]], 'translation vector': [0.002023687193802637, -0.005328769253949428, -0.0010872499872041086]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_61_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_61_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_61_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_61_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.303413, -0.396105, 0.866627], [-0.952854, 0.129076, -0.274605], [-0.003088, -0.909088, -0.416593]], 'translation vector': [3.699021, 3.5579, 1.347225]}\nB: {'rotation matrix': [[-0.298621, -0.390383, 0.870877], [-0.954343, 0.1293, -0.26928], [-0.007482, -0.911527, -0.411171]], 'translation vector': [3.695972, 3.555829, 1.344301]}\nC: {'rotation matrix': [[-0.295385, -0.381895, 0.875731], [-0.955321, 0.128105, -0.266366], [-0.010462, -0.915284, -0.402672]], 'translation vector': [3.694636, 3.554343, 1.343555]}\nD: {'rotation matrix': [[0.9999470991216314, -8.645859163103856e-05, -0.010261215579303299], [0.00018576960757566105, 0.999954044961018, 0.009631176353467179], [0.010258362047762553, -0.00963308504359663, 0.9999007352405366]], 'translation vector': [0.002023687193802637, -0.005328769253949428, -0.0010872499872041086]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.996466, -0.001244, 0.083992], [-0.083908, 0.032374, 0.995948], [-0.003958, -0.999475, 0.032156]], 'translation vector': [2.320184, 5.772667, 1.343957]}\nB: {'rotation matrix': [[0.994982, 0.000179, 0.100051], [-0.099963, 0.043922, 0.994021], [-0.004216, -0.999035, 0.043719]], 'translation vector': [2.304385, 5.780403, 1.335008]}\nC: {'rotation matrix': [[0.995982, -5.3e-05, 0.089553], [-0.089485, 0.038471, 0.995245], [-0.003498, -0.99926, 0.038311]], 'translation vector': [2.323582, 5.777781, 1.339158]}\nD: {'rotation matrix': [[0.9997533523434441, 0.006129226982668424, -0.021333118212536917], [-0.0059433732378316425, 0.9999427902508286, 0.008815866423587717], [0.02138545815854184, -0.008687653795007199, 0.9997333060483455]], 'translation vector': [0.03726599986839263, -0.00376568964599322, 0.0024709716040858254]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_62_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_62_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_62_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_62_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.996466, -0.001244, 0.083992], [-0.083908, 0.032374, 0.995948], [-0.003958, -0.999475, 0.032156]], 'translation vector': [2.320184, 5.772667, 1.343957]}\nB: {'rotation matrix': [[0.994982, 0.000179, 0.100051], [-0.099963, 0.043922, 0.994021], [-0.004216, -0.999035, 0.043719]], 'translation vector': [2.304385, 5.780403, 1.335008]}\nC: {'rotation matrix': [[0.995982, -5.3e-05, 0.089553], [-0.089485, 0.038471, 0.995245], [-0.003498, -0.99926, 0.038311]], 'translation vector': [2.323582, 5.777781, 1.339158]}\nD: {'rotation matrix': [[0.9997533523434441, 0.006129226982668424, -0.021333118212536917], [-0.0059433732378316425, 0.9999427902508286, 0.008815866423587717], [0.02138545815854184, -0.008687653795007199, 0.9997333060483455]], 'translation vector': [0.03726599986839263, -0.00376568964599322, 0.0024709716040858254]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.980568, 0.060238, -0.186705], [0.195333, -0.388226, 0.900625], [-0.018232, -0.919594, -0.392448]], 'translation vector': [0.950732, 0.877848, 1.428266]}\nB: {'rotation matrix': [[0.980128, 0.059665, -0.18918], [0.197203, -0.396203, 0.896735], [-0.02145, -0.916222, -0.400096]], 'translation vector': [0.955184, 0.877183, 1.426427]}\nC: {'rotation matrix': [[0.979822, 0.061197, -0.190271], [0.198756, -0.398709, 0.89528], [-0.021074, -0.915034, -0.402827]], 'translation vector': [0.958185, 0.874355, 1.42036]}\nD: {'rotation matrix': [[0.9999275128023173, 0.008592189796214917, -0.008452948749791484], [-0.008364555101980847, 0.9996139276735185, 0.026478714279731284], [0.008677131864842291, -0.02640733905967608, 0.9996135651618278]], 'translation vector': [0.01623466192676637, 0.01524648577924892, 0.005088344597766196]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_63_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_63_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_63_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_63_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.980568, 0.060238, -0.186705], [0.195333, -0.388226, 0.900625], [-0.018232, -0.919594, -0.392448]], 'translation vector': [0.950732, 0.877848, 1.428266]}\nB: {'rotation matrix': [[0.980128, 0.059665, -0.18918], [0.197203, -0.396203, 0.896735], [-0.02145, -0.916222, -0.400096]], 'translation vector': [0.955184, 0.877183, 1.426427]}\nC: {'rotation matrix': [[0.979822, 0.061197, -0.190271], [0.198756, -0.398709, 0.89528], [-0.021074, -0.915034, -0.402827]], 'translation vector': [0.958185, 0.874355, 1.42036]}\nD: {'rotation matrix': [[0.9999275128023173, 0.008592189796214917, -0.008452948749791484], [-0.008364555101980847, 0.9996139276735185, 0.026478714279731284], [0.008677131864842291, -0.02640733905967608, 0.9996135651618278]], 'translation vector': [0.01623466192676637, 0.01524648577924892, 0.005088344597766196]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.986729, -0.120978, 0.108304], [-0.140205, -0.298357, 0.944101], [-0.081902, -0.946757, -0.311359]], 'translation vector': [1.126551, 1.554919, 1.507245]}\nB: {'rotation matrix': [[0.9999934208085578, -0.0036450224249741864, 0.00012972614797144], [0.003645024213464494, 0.9999784140945303, 0.005329513561381964], [-0.0001495669882096765, -0.005329922173518521, 0.999986531390115]], 'translation vector': [0.0031820360164642736, 0.0011599203443171113, -0.0008228633488331916]}\nC: {'rotation matrix': [[0.986279, -0.125902, 0.106787], [-0.140227, -0.297512, 0.944364], [-0.087128, -0.94638, -0.311084]], 'translation vector': [1.129178, 1.552708, 1.506911]}\nD: {'rotation matrix': [[0.987067, -0.117318, 0.109254], [-0.140068, -0.29963, 0.943717], [-0.077979, -0.946815, -0.312188]], 'translation vector': [1.12401, 1.557217, 1.508026]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_64_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_64_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_64_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_64_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.986729, -0.120978, 0.108304], [-0.140205, -0.298357, 0.944101], [-0.081902, -0.946757, -0.311359]], 'translation vector': [1.126551, 1.554919, 1.507245]}\nB: {'rotation matrix': [[0.9999934208085578, -0.0036450224249741864, 0.00012972614797144], [0.003645024213464494, 0.9999784140945303, 0.005329513561381964], [-0.0001495669882096765, -0.005329922173518521, 0.999986531390115]], 'translation vector': [0.0031820360164642736, 0.0011599203443171113, -0.0008228633488331916]}\nC: {'rotation matrix': [[0.986279, -0.125902, 0.106787], [-0.140227, -0.297512, 0.944364], [-0.087128, -0.94638, -0.311084]], 'translation vector': [1.129178, 1.552708, 1.506911]}\nD: {'rotation matrix': [[0.987067, -0.117318, 0.109254], [-0.140068, -0.29963, 0.943717], [-0.077979, -0.946815, -0.312188]], 'translation vector': [1.12401, 1.557217, 1.508026]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.34785, 0.12218, -0.929555], [0.936582, -0.000241, 0.350448], [0.042594, -0.992508, -0.114515]], 'translation vector': [2.709976, 2.082475, 1.464411]}\nB: {'rotation matrix': [[0.348346, 0.120067, -0.929645], [0.936396, 0.00053, 0.350944], [0.042629, -0.992766, -0.112245]], 'translation vector': [2.711116, 2.081261, 1.464473]}\nC: {'rotation matrix': [[0.9999996813993267, 0.0006538118368534332, -8.345927351154747e-06], [-0.0006528854877680812, 0.9999955547316733, 0.002915205326606667], [1.0134396361542957e-05, -0.002915969719233099, 0.9999961918791215]], 'translation vector': [-0.0015843322088442413, -0.00023090688010451998, -0.00020610665531961558]}\nD: {'rotation matrix': [[0.34832, 0.118845, -0.929811], [0.936428, 0.00049, 0.350861], [0.042154, -0.992913, -0.111119]], 'translation vector': [2.712512, 2.080143, 1.464219]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_65_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_65_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_65_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_65_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.34785, 0.12218, -0.929555], [0.936582, -0.000241, 0.350448], [0.042594, -0.992508, -0.114515]], 'translation vector': [2.709976, 2.082475, 1.464411]}\nB: {'rotation matrix': [[0.348346, 0.120067, -0.929645], [0.936396, 0.00053, 0.350944], [0.042629, -0.992766, -0.112245]], 'translation vector': [2.711116, 2.081261, 1.464473]}\nC: {'rotation matrix': [[0.9999996813993267, 0.0006538118368534332, -8.345927351154747e-06], [-0.0006528854877680812, 0.9999955547316733, 0.002915205326606667], [1.0134396361542957e-05, -0.002915969719233099, 0.9999961918791215]], 'translation vector': [-0.0015843322088442413, -0.00023090688010451998, -0.00020610665531961558]}\nD: {'rotation matrix': [[0.34832, 0.118845, -0.929811], [0.936428, 0.00049, 0.350861], [0.042154, -0.992913, -0.111119]], 'translation vector': [2.712512, 2.080143, 1.464219]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.436759, -0.378371, 0.816135], [-0.888864, -0.041911, 0.45625], [-0.138426, -0.924705, -0.354625]], 'translation vector': [2.634246, 6.766675, 1.418575]}\nB: {'rotation matrix': [[0.9999585207206515, -0.003693386756648477, 0.008341273999683681], [0.0038081841800440604, 0.9998961013275416, -0.013845142600948103], [-0.008289566528701709, 0.013875695158089622, 0.9998694901286989]], 'translation vector': [0.0023225809960010224, 0.0034699322670346255, -0.0016854173948939177]}\nC: {'rotation matrix': [[0.435508, -0.378095, 0.816931], [-0.889287, -0.039919, 0.455605], [-0.139651, -0.924906, -0.35362]], 'translation vector': [2.637863, 6.764602, 1.421504]}\nD: {'rotation matrix': [[0.436668, -0.378575, 0.81609], [-0.888812, -0.041337, 0.456404], [-0.139048, -0.924647, -0.354533]], 'translation vector': [2.636727, 6.764818, 1.421436]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_66_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_66_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_66_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_66_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.436759, -0.378371, 0.816135], [-0.888864, -0.041911, 0.45625], [-0.138426, -0.924705, -0.354625]], 'translation vector': [2.634246, 6.766675, 1.418575]}\nB: {'rotation matrix': [[0.9999585207206515, -0.003693386756648477, 0.008341273999683681], [0.0038081841800440604, 0.9998961013275416, -0.013845142600948103], [-0.008289566528701709, 0.013875695158089622, 0.9998694901286989]], 'translation vector': [0.0023225809960010224, 0.0034699322670346255, -0.0016854173948939177]}\nC: {'rotation matrix': [[0.435508, -0.378095, 0.816931], [-0.889287, -0.039919, 0.455605], [-0.139651, -0.924906, -0.35362]], 'translation vector': [2.637863, 6.764602, 1.421504]}\nD: {'rotation matrix': [[0.436668, -0.378575, 0.81609], [-0.888812, -0.041337, 0.456404], [-0.139048, -0.924647, -0.354533]], 'translation vector': [2.636727, 6.764818, 1.421436]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.994032, -0.08462, 0.068848], [-0.109061, -0.785368, 0.609346], [0.002509, -0.613218, -0.78991]], 'translation vector': [1.310442, 0.50567, 1.185464]}\nB: {'rotation matrix': [[0.994117, -0.083714, 0.068724], [-0.108266, -0.785982, 0.608696], [0.003059, -0.612556, -0.790421]], 'translation vector': [1.309119, 0.507232, 1.184932]}\nC: {'rotation matrix': [[0.9999858152282176, 0.005111825032549412, 0.0011655701624676482], [-0.005114663160965653, 0.9999842406155692, 0.0025048411154926643], [-0.0011536230586058512, -0.002510397377259177, 0.9999958649343827]], 'translation vector': [-0.003249111441538055, -0.00014047167946484862, 0.0018748641056286486]}\nD: {'rotation matrix': [[0.99397, -0.085854, 0.068209], [-0.109644, -0.78528, 0.609356], [0.001247, -0.61316, -0.789958]], 'translation vector': [1.312051, 0.504544, 1.186353]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_67_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_67_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_67_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_67_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.994032, -0.08462, 0.068848], [-0.109061, -0.785368, 0.609346], [0.002509, -0.613218, -0.78991]], 'translation vector': [1.310442, 0.50567, 1.185464]}\nB: {'rotation matrix': [[0.994117, -0.083714, 0.068724], [-0.108266, -0.785982, 0.608696], [0.003059, -0.612556, -0.790421]], 'translation vector': [1.309119, 0.507232, 1.184932]}\nC: {'rotation matrix': [[0.9999858152282176, 0.005111825032549412, 0.0011655701624676482], [-0.005114663160965653, 0.9999842406155692, 0.0025048411154926643], [-0.0011536230586058512, -0.002510397377259177, 0.9999958649343827]], 'translation vector': [-0.003249111441538055, -0.00014047167946484862, 0.0018748641056286486]}\nD: {'rotation matrix': [[0.99397, -0.085854, 0.068209], [-0.109644, -0.78528, 0.609356], [0.001247, -0.61316, -0.789958]], 'translation vector': [1.312051, 0.504544, 1.186353]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.184838, -0.635323, 0.7498], [-0.98276, 0.116273, -0.143746], [0.004144, -0.763444, -0.645861]], 'translation vector': [1.003433, 1.175637, 1.437383]}\nB: {'rotation matrix': [[-0.184201, -0.636583, 0.748887], [-0.982879, 0.11585, -0.143279], [0.00445, -0.762457, -0.647023]], 'translation vector': [1.004527, 1.174467, 1.438164]}\nC: {'rotation matrix': [[-0.184847, -0.63596, 0.749258], [-0.982754, 0.115597, -0.144335], [0.005179, -0.763016, -0.646359]], 'translation vector': [1.003287, 1.175642, 1.437097]}\nD: {'rotation matrix': [[0.9999986055688241, -0.001502869212153915, -0.0010557523049182509], [0.0014994906185130158, 0.9999931829288878, -0.003258507979167455], [0.0010610991854197703, 0.003257203501798957, 0.999994335142242]], 'translation vector': [0.0018601875903911935, -0.0006944560630759433, -0.0003289584369169929]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_68_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_68_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_68_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_68_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.184838, -0.635323, 0.7498], [-0.98276, 0.116273, -0.143746], [0.004144, -0.763444, -0.645861]], 'translation vector': [1.003433, 1.175637, 1.437383]}\nB: {'rotation matrix': [[-0.184201, -0.636583, 0.748887], [-0.982879, 0.11585, -0.143279], [0.00445, -0.762457, -0.647023]], 'translation vector': [1.004527, 1.174467, 1.438164]}\nC: {'rotation matrix': [[-0.184847, -0.63596, 0.749258], [-0.982754, 0.115597, -0.144335], [0.005179, -0.763016, -0.646359]], 'translation vector': [1.003287, 1.175642, 1.437097]}\nD: {'rotation matrix': [[0.9999986055688241, -0.001502869212153915, -0.0010557523049182509], [0.0014994906185130158, 0.9999931829288878, -0.003258507979167455], [0.0010610991854197703, 0.003257203501798957, 0.999994335142242]], 'translation vector': [0.0018601875903911935, -0.0006944560630759433, -0.0003289584369169929]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.200306, -0.502555, 0.841021], [-0.979587, 0.117581, -0.163047], [-0.016948, -0.856512, -0.515849]], 'translation vector': [2.912164, 4.287547, 1.28791]}\nB: {'rotation matrix': [[0.9999873120379679, 0.002880995332778331, 0.004067114056895998], [-0.002882835524779029, 0.9999951519449766, 0.0005437699978073844], [-0.004064760122873662, -0.0005563594395375273, 0.9999918873961958]], 'translation vector': [-0.0006376699678316555, 0.00863085045062073, -0.004612247460962893]}\nC: {'rotation matrix': [[-0.196493, -0.497947, 0.844654], [-0.980345, 0.115377, -0.160041], [-0.017762, -0.859498, -0.51083]], 'translation vector': [2.914041, 4.284364, 1.288676]}\nD: {'rotation matrix': [[-0.191542, -0.494391, 0.847873], [-0.981313, 0.112624, -0.156017], [-0.018357, -0.861913, -0.506724]], 'translation vector': [2.915548, 4.280207, 1.289523]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_69_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_69_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_69_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_69_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.200306, -0.502555, 0.841021], [-0.979587, 0.117581, -0.163047], [-0.016948, -0.856512, -0.515849]], 'translation vector': [2.912164, 4.287547, 1.28791]}\nB: {'rotation matrix': [[0.9999873120379679, 0.002880995332778331, 0.004067114056895998], [-0.002882835524779029, 0.9999951519449766, 0.0005437699978073844], [-0.004064760122873662, -0.0005563594395375273, 0.9999918873961958]], 'translation vector': [-0.0006376699678316555, 0.00863085045062073, -0.004612247460962893]}\nC: {'rotation matrix': [[-0.196493, -0.497947, 0.844654], [-0.980345, 0.115377, -0.160041], [-0.017762, -0.859498, -0.51083]], 'translation vector': [2.914041, 4.284364, 1.288676]}\nD: {'rotation matrix': [[-0.191542, -0.494391, 0.847873], [-0.981313, 0.112624, -0.156017], [-0.018357, -0.861913, -0.506724]], 'translation vector': [2.915548, 4.280207, 1.289523]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.734788, 0.391108, -0.554185], [0.678289, 0.419895, -0.603003], [-0.00314, -0.818977, -0.573818]], 'translation vector': [5.172049, 2.206823, 1.423276]}\nB: {'rotation matrix': [[-0.735622, 0.390311, -0.55364], [0.677385, 0.420178, -0.603821], [-0.003051, -0.819212, -0.573483]], 'translation vector': [5.170603, 2.207022, 1.424387]}\nC: {'rotation matrix': [[0.9999900939462747, -0.004290505816425898, 0.0009195812709430778], [0.0042999758462496885, 0.9999235506202053, -0.011514550430676607], [-0.0008694094505867219, 0.011517701317526751, 0.9999331376988827]], 'translation vector': [-0.00082889643265327, -0.0036811171481734295, -0.003335935402839496]}\nD: {'rotation matrix': [[-0.734029, 0.39088, -0.55535], [0.679109, 0.418321, -0.603174], [-0.003454, -0.81989, -0.57251]], 'translation vector': [5.174113, 2.207384, 1.421993]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_70_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_70_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_70_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_70_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.734788, 0.391108, -0.554185], [0.678289, 0.419895, -0.603003], [-0.00314, -0.818977, -0.573818]], 'translation vector': [5.172049, 2.206823, 1.423276]}\nB: {'rotation matrix': [[-0.735622, 0.390311, -0.55364], [0.677385, 0.420178, -0.603821], [-0.003051, -0.819212, -0.573483]], 'translation vector': [5.170603, 2.207022, 1.424387]}\nC: {'rotation matrix': [[0.9999900939462747, -0.004290505816425898, 0.0009195812709430778], [0.0042999758462496885, 0.9999235506202053, -0.011514550430676607], [-0.0008694094505867219, 0.011517701317526751, 0.9999331376988827]], 'translation vector': [-0.00082889643265327, -0.0036811171481734295, -0.003335935402839496]}\nD: {'rotation matrix': [[-0.734029, 0.39088, -0.55535], [0.679109, 0.418321, -0.603174], [-0.003454, -0.81989, -0.57251]], 'translation vector': [5.174113, 2.207384, 1.421993]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.231441, -0.548452, 0.803514], [-0.97266, -0.114172, 0.202231], [-0.019175, -0.828351, -0.559882]], 'translation vector': [1.70644, 2.067417, 1.36452]}\nB: {'rotation matrix': [[0.226329, -0.547002, 0.805955], [-0.973947, -0.114973, 0.195472], [-0.01426, -0.829199, -0.558772]], 'translation vector': [1.704179, 2.073727, 1.363978]}\nC: {'rotation matrix': [[0.9999250411381765, -0.0006651889723176079, -0.012145965376701085], [0.0006720606381267904, 0.999998823757648, 0.0005907315128157197], [0.012145773622422931, -0.0005986694012902582, 0.9999254709423802]], 'translation vector': [0.004506212132421972, 0.005377300872088764, -0.0022216956686449407]}\nD: {'rotation matrix': [[0.220852, -0.547607, 0.807063], [-0.975257, -0.115603, 0.188439], [-0.009892, -0.828711, -0.559589]], 'translation vector': [1.70298, 2.078271, 1.364741]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_71_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_71_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_71_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_71_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.231441, -0.548452, 0.803514], [-0.97266, -0.114172, 0.202231], [-0.019175, -0.828351, -0.559882]], 'translation vector': [1.70644, 2.067417, 1.36452]}\nB: {'rotation matrix': [[0.226329, -0.547002, 0.805955], [-0.973947, -0.114973, 0.195472], [-0.01426, -0.829199, -0.558772]], 'translation vector': [1.704179, 2.073727, 1.363978]}\nC: {'rotation matrix': [[0.9999250411381765, -0.0006651889723176079, -0.012145965376701085], [0.0006720606381267904, 0.999998823757648, 0.0005907315128157197], [0.012145773622422931, -0.0005986694012902582, 0.9999254709423802]], 'translation vector': [0.004506212132421972, 0.005377300872088764, -0.0022216956686449407]}\nD: {'rotation matrix': [[0.220852, -0.547607, 0.807063], [-0.975257, -0.115603, 0.188439], [-0.009892, -0.828711, -0.559589]], 'translation vector': [1.70298, 2.078271, 1.364741]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.122634, -0.436101, 0.891503], [-0.989963, 0.117329, -0.078783], [-0.070242, -0.892216, -0.446113]], 'translation vector': [3.397121, 4.680246, 1.399477]}\nB: {'rotation matrix': [[-0.131936, -0.437064, 0.889701], [-0.988639, 0.123231, -0.08607], [-0.072021, -0.890949, -0.448357]], 'translation vector': [3.380324, 4.680538, 1.400463]}\nC: {'rotation matrix': [[0.9999977526314023, 0.0018483257426729351, -0.00041760047623725873], [-0.0018481932934636253, 0.9999980766573896, 0.00088155464161366], [0.00041944957876658323, -0.0008809692307902862, 0.9999991600629605]], 'translation vector': [0.0013914092466897898, -0.0023350058272084695, 0.005488229626908758]}\nD: {'rotation matrix': [[-0.127448, -0.436966, 0.890403], [-0.989339, 0.119783, -0.082825], [-0.070464, -0.891467, -0.447574]], 'translation vector': [3.388002, 4.681844, 1.400749]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_72_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_72_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_72_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_72_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.122634, -0.436101, 0.891503], [-0.989963, 0.117329, -0.078783], [-0.070242, -0.892216, -0.446113]], 'translation vector': [3.397121, 4.680246, 1.399477]}\nB: {'rotation matrix': [[-0.131936, -0.437064, 0.889701], [-0.988639, 0.123231, -0.08607], [-0.072021, -0.890949, -0.448357]], 'translation vector': [3.380324, 4.680538, 1.400463]}\nC: {'rotation matrix': [[0.9999977526314023, 0.0018483257426729351, -0.00041760047623725873], [-0.0018481932934636253, 0.9999980766573896, 0.00088155464161366], [0.00041944957876658323, -0.0008809692307902862, 0.9999991600629605]], 'translation vector': [0.0013914092466897898, -0.0023350058272084695, 0.005488229626908758]}\nD: {'rotation matrix': [[-0.127448, -0.436966, 0.890403], [-0.989339, 0.119783, -0.082825], [-0.070464, -0.891467, -0.447574]], 'translation vector': [3.388002, 4.681844, 1.400749]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.018523, 0.490425, -0.871286], [0.99775, 0.065233, 0.015507], [0.064442, -0.869039, -0.49053]], 'translation vector': [3.293662, 2.081986, 1.287803]}\nB: {'rotation matrix': [[-0.012031, 0.49001, -0.871634], [0.99805, 0.059277, 0.019548], [0.061246, -0.869699, -0.489768]], 'translation vector': [3.297196, 2.086649, 1.289788]}\nC: {'rotation matrix': [[0.9998524821662119, 0.011110486941731442, -0.013083720076090504], [-0.011104516921568186, 0.9999377181682902, 0.0005342849090069752], [0.013088749073536516, -0.0003893477266632072, 0.9999144546094512]], 'translation vector': [-0.004299120253105748, -0.007367168150444914, 0.008875125679755236]}\nD: {'rotation matrix': [[-0.025914, 0.492003, -0.870208], [0.997577, 0.068946, 0.009274], [0.06456, -0.867859, -0.492598]], 'translation vector': [3.28927, 2.078913, 1.287729]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_73_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_73_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_73_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_73_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.018523, 0.490425, -0.871286], [0.99775, 0.065233, 0.015507], [0.064442, -0.869039, -0.49053]], 'translation vector': [3.293662, 2.081986, 1.287803]}\nB: {'rotation matrix': [[-0.012031, 0.49001, -0.871634], [0.99805, 0.059277, 0.019548], [0.061246, -0.869699, -0.489768]], 'translation vector': [3.297196, 2.086649, 1.289788]}\nC: {'rotation matrix': [[0.9998524821662119, 0.011110486941731442, -0.013083720076090504], [-0.011104516921568186, 0.9999377181682902, 0.0005342849090069752], [0.013088749073536516, -0.0003893477266632072, 0.9999144546094512]], 'translation vector': [-0.004299120253105748, -0.007367168150444914, 0.008875125679755236]}\nD: {'rotation matrix': [[-0.025914, 0.492003, -0.870208], [0.997577, 0.068946, 0.009274], [0.06456, -0.867859, -0.492598]], 'translation vector': [3.28927, 2.078913, 1.287729]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.963707, 0.155577, -0.216944], [0.257928, 0.333002, -0.906964], [-0.06886, -0.930003, -0.361044]], 'translation vector': [5.977247, 2.820638, 1.467431]}\nB: {'rotation matrix': [[-0.963568, 0.155178, -0.217846], [0.258718, 0.334184, -0.906303], [-0.067837, -0.929646, -0.362157]], 'translation vector': [5.975011, 2.821235, 1.467201]}\nC: {'rotation matrix': [[-0.963289, 0.1552, -0.219062], [0.259892, 0.33449, -0.905855], [-0.067315, -0.929532, -0.362545]], 'translation vector': [5.973778, 2.820463, 1.46621]}\nD: {'rotation matrix': [[0.9999997264810032, 0.0001398888061159318, -0.0006664023459939352], [-0.00013916119976689398, 1.0000000334978612, 0.0010113123861037441], [0.0006662597489821222, -0.0010109047313490711, 0.9999993871422703]], 'translation vector': [0.0010522232087115668, -0.0015450075589019119, 0.0008995589608247201]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_74_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_74_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_74_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_74_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.963707, 0.155577, -0.216944], [0.257928, 0.333002, -0.906964], [-0.06886, -0.930003, -0.361044]], 'translation vector': [5.977247, 2.820638, 1.467431]}\nB: {'rotation matrix': [[-0.963568, 0.155178, -0.217846], [0.258718, 0.334184, -0.906303], [-0.067837, -0.929646, -0.362157]], 'translation vector': [5.975011, 2.821235, 1.467201]}\nC: {'rotation matrix': [[-0.963289, 0.1552, -0.219062], [0.259892, 0.33449, -0.905855], [-0.067315, -0.929532, -0.362545]], 'translation vector': [5.973778, 2.820463, 1.46621]}\nD: {'rotation matrix': [[0.9999997264810032, 0.0001398888061159318, -0.0006664023459939352], [-0.00013916119976689398, 1.0000000334978612, 0.0010113123861037441], [0.0006662597489821222, -0.0010109047313490711, 0.9999993871422703]], 'translation vector': [0.0010522232087115668, -0.0015450075589019119, 0.0008995589608247201]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.608915, -0.355061, 0.709333], [-0.792506, -0.310645, 0.524818], [0.034009, -0.881721, -0.470545]], 'translation vector': [3.226315, 3.403534, 1.349898]}\nB: {'rotation matrix': [[0.615195, -0.359882, 0.701442], [-0.787888, -0.311929, 0.530973], [0.027713, -0.87931, -0.475444]], 'translation vector': [3.233385, 3.405524, 1.366998]}\nC: {'rotation matrix': [[0.613018, -0.357666, 0.704474], [-0.789518, -0.310613, 0.529321], [0.029499, -0.880678, -0.472795]], 'translation vector': [3.232441, 3.405463, 1.362879]}\nD: {'rotation matrix': [[0.9997863328706507, 0.00048772072536455783, -0.020639341477887214], [-0.000435326360773427, 0.9999962939853105, 0.002563461941184151], [0.020641174681034113, -0.002552850852538859, 0.9997836062327491]], 'translation vector': [0.014979793826815802, -0.00028266630712581176, -0.003454343250508085]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_75_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_75_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_75_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_75_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.608915, -0.355061, 0.709333], [-0.792506, -0.310645, 0.524818], [0.034009, -0.881721, -0.470545]], 'translation vector': [3.226315, 3.403534, 1.349898]}\nB: {'rotation matrix': [[0.615195, -0.359882, 0.701442], [-0.787888, -0.311929, 0.530973], [0.027713, -0.87931, -0.475444]], 'translation vector': [3.233385, 3.405524, 1.366998]}\nC: {'rotation matrix': [[0.613018, -0.357666, 0.704474], [-0.789518, -0.310613, 0.529321], [0.029499, -0.880678, -0.472795]], 'translation vector': [3.232441, 3.405463, 1.362879]}\nD: {'rotation matrix': [[0.9997863328706507, 0.00048772072536455783, -0.020639341477887214], [-0.000435326360773427, 0.9999962939853105, 0.002563461941184151], [0.020641174681034113, -0.002552850852538859, 0.9997836062327491]], 'translation vector': [0.014979793826815802, -0.00028266630712581176, -0.003454343250508085]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999991463733822, 0.0008702753414361648, -0.0009523887468317617], [-0.0008598677378153328, 0.999935869624125, 0.011274652427134848], [0.0009622841974666614, -0.011274638788349072, 0.9999353623727418]], 'translation vector': [-0.0018668216035679919, 0.003964358597176476, 0.0035095844812185473]}\nB: {'rotation matrix': [[0.995169, 0.04021, -0.089565], [0.098119, -0.43886, 0.893182], [-0.003392, -0.897655, -0.440686]], 'translation vector': [3.819187, 1.33594, 1.360146]}\nC: {'rotation matrix': [[0.994619, 0.036032, -0.097136], [0.10302, -0.443404, 0.890382], [-0.010989, -0.895597, -0.44473]], 'translation vector': [3.820524, 1.337409, 1.359976]}\nD: {'rotation matrix': [[0.995617, 0.045838, -0.081525], [0.09337, -0.436452, 0.89487], [0.005437, -0.898559, -0.438819]], 'translation vector': [3.821348, 1.335292, 1.36241]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_76_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_76_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_76_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_76_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999991463733822, 0.0008702753414361648, -0.0009523887468317617], [-0.0008598677378153328, 0.999935869624125, 0.011274652427134848], [0.0009622841974666614, -0.011274638788349072, 0.9999353623727418]], 'translation vector': [-0.0018668216035679919, 0.003964358597176476, 0.0035095844812185473]}\nB: {'rotation matrix': [[0.995169, 0.04021, -0.089565], [0.098119, -0.43886, 0.893182], [-0.003392, -0.897655, -0.440686]], 'translation vector': [3.819187, 1.33594, 1.360146]}\nC: {'rotation matrix': [[0.994619, 0.036032, -0.097136], [0.10302, -0.443404, 0.890382], [-0.010989, -0.895597, -0.44473]], 'translation vector': [3.820524, 1.337409, 1.359976]}\nD: {'rotation matrix': [[0.995617, 0.045838, -0.081525], [0.09337, -0.436452, 0.89487], [0.005437, -0.898559, -0.438819]], 'translation vector': [3.821348, 1.335292, 1.36241]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.882555, 0.386221, -0.268197], [0.46123, 0.600144, -0.653524], [-0.091448, -0.700472, -0.707797]], 'translation vector': [4.952963, 3.575409, 1.461658]}\nB: {'rotation matrix': [[-0.884605, 0.387053, -0.260122], [0.456774, 0.606746, -0.650551], [-0.09397, -0.694298, -0.713526]], 'translation vector': [4.944654, 3.579183, 1.459738]}\nC: {'rotation matrix': [[-0.883899, 0.386346, -0.263552], [0.458302, 0.603262, -0.652713], [-0.093182, -0.697719, -0.710286]], 'translation vector': [4.946745, 3.577697, 1.460677]}\nD: {'rotation matrix': [[0.9999447182645155, 0.005004874877878123, -0.00920632642357687], [-0.0050456554798214885, 0.9999777740634089, -0.004314913443862226], [0.009184864298060464, 0.004360756390993736, 0.9999481114756171]], 'translation vector': [0.007685923361448133, 0.0066471354724360054, 0.0017036092209536946]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_77_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_77_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_77_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_77_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.882555, 0.386221, -0.268197], [0.46123, 0.600144, -0.653524], [-0.091448, -0.700472, -0.707797]], 'translation vector': [4.952963, 3.575409, 1.461658]}\nB: {'rotation matrix': [[-0.884605, 0.387053, -0.260122], [0.456774, 0.606746, -0.650551], [-0.09397, -0.694298, -0.713526]], 'translation vector': [4.944654, 3.579183, 1.459738]}\nC: {'rotation matrix': [[-0.883899, 0.386346, -0.263552], [0.458302, 0.603262, -0.652713], [-0.093182, -0.697719, -0.710286]], 'translation vector': [4.946745, 3.577697, 1.460677]}\nD: {'rotation matrix': [[0.9999447182645155, 0.005004874877878123, -0.00920632642357687], [-0.0050456554798214885, 0.9999777740634089, -0.004314913443862226], [0.009184864298060464, 0.004360756390993736, 0.9999481114756171]], 'translation vector': [0.007685923361448133, 0.0066471354724360054, 0.0017036092209536946]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.625113, 0.311868, -0.715523], [0.780406, -0.266348, 0.565708], [-0.014152, -0.912029, -0.409881]], 'translation vector': [1.602076, 0.627028, 1.325196]}\nB: {'rotation matrix': [[0.622635, 0.31497, -0.716324], [0.782419, -0.264717, 0.563689], [-0.012077, -0.911438, -0.411261]], 'translation vector': [1.601839, 0.627416, 1.324643]}\nC: {'rotation matrix': [[0.624152, 0.313246, -0.715759], [0.781196, -0.26537, 0.565077], [-0.012933, -0.911842, -0.410338]], 'translation vector': [1.601807, 0.626749, 1.324787]}\nD: {'rotation matrix': [[0.999966975162773, 0.003632343802915245, 0.007281117690826753], [-0.0036218018444724924, 0.9999920045733598, -0.0017116834889060193], [-0.007286301405287572, 0.0016849146368096719, 0.9999717827304806]], 'translation vector': [-0.0025932164044990547, 0.0007159923074403496, -0.0006736212556601728]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_78_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_78_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_78_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_78_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.625113, 0.311868, -0.715523], [0.780406, -0.266348, 0.565708], [-0.014152, -0.912029, -0.409881]], 'translation vector': [1.602076, 0.627028, 1.325196]}\nB: {'rotation matrix': [[0.622635, 0.31497, -0.716324], [0.782419, -0.264717, 0.563689], [-0.012077, -0.911438, -0.411261]], 'translation vector': [1.601839, 0.627416, 1.324643]}\nC: {'rotation matrix': [[0.624152, 0.313246, -0.715759], [0.781196, -0.26537, 0.565077], [-0.012933, -0.911842, -0.410338]], 'translation vector': [1.601807, 0.626749, 1.324787]}\nD: {'rotation matrix': [[0.999966975162773, 0.003632343802915245, 0.007281117690826753], [-0.0036218018444724924, 0.9999920045733598, -0.0017116834889060193], [-0.007286301405287572, 0.0016849146368096719, 0.9999717827304806]], 'translation vector': [-0.0025932164044990547, 0.0007159923074403496, -0.0006736212556601728]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.198116, 0.271577, -0.941805], [0.979964, -0.034779, 0.196115], [0.020505, -0.961788, -0.273026]], 'translation vector': [3.606948, 3.761193, 1.556592]}\nB: {'rotation matrix': [[0.193825, 0.274451, -0.941864], [0.980909, -0.038747, 0.19057], [0.015807, -0.96082, -0.276722]], 'translation vector': [3.608205, 3.76769, 1.544741]}\nC: {'rotation matrix': [[0.999997674889758, 0.0021739401100205674, -0.00025104493249259135], [-0.002175952729401346, 0.9999848730149307, -0.004981286419615835], [0.0002396488755047073, 0.004981659902978713, 0.9999879544274658]], 'translation vector': [0.0009190453627176964, -0.0033553865594018184, -0.0035327864721872437]}\nD: {'rotation matrix': [[0.190217, 0.28361, -0.939884], [0.981635, -0.040777, 0.186362], [0.014528, -0.958072, -0.286158]], 'translation vector': [3.605221, 3.771751, 1.549751]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_79_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_79_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_79_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_79_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.198116, 0.271577, -0.941805], [0.979964, -0.034779, 0.196115], [0.020505, -0.961788, -0.273026]], 'translation vector': [3.606948, 3.761193, 1.556592]}\nB: {'rotation matrix': [[0.193825, 0.274451, -0.941864], [0.980909, -0.038747, 0.19057], [0.015807, -0.96082, -0.276722]], 'translation vector': [3.608205, 3.76769, 1.544741]}\nC: {'rotation matrix': [[0.999997674889758, 0.0021739401100205674, -0.00025104493249259135], [-0.002175952729401346, 0.9999848730149307, -0.004981286419615835], [0.0002396488755047073, 0.004981659902978713, 0.9999879544274658]], 'translation vector': [0.0009190453627176964, -0.0033553865594018184, -0.0035327864721872437]}\nD: {'rotation matrix': [[0.190217, 0.28361, -0.939884], [0.981635, -0.040777, 0.186362], [0.014528, -0.958072, -0.286158]], 'translation vector': [3.605221, 3.771751, 1.549751]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.631227, -0.321947, 0.705622], [-0.775405, -0.28226, 0.564869], [0.017311, -0.903703, -0.427809]], 'translation vector': [-0.207113, 0.785695, 1.605991]}\nB: {'rotation matrix': [[0.9999995913321388, 0.0005903556746734515, -0.0005873839987845575], [-0.0005915541354645518, 0.9999966980036584, -0.0023348317149788846], [0.0005870160507556152, 0.00233575631512544, 0.9999968115853851]], 'translation vector': [0.0016624461367128474, -0.0028184747771204943, -0.001607026045218174]}\nC: {'rotation matrix': [[0.628117, -0.317695, 0.71031], [-0.777888, -0.278629, 0.563255], [0.018969, -0.906331, -0.422142]], 'translation vector': [-0.210483, 0.781575, 1.607029]}\nD: {'rotation matrix': [[0.626043, -0.313657, 0.713926], [-0.779508, -0.27628, 0.562171], [0.020914, -0.908454, -0.417462]], 'translation vector': [-0.212996, 0.77858, 1.610364]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_80_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_80_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_80_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_80_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.631227, -0.321947, 0.705622], [-0.775405, -0.28226, 0.564869], [0.017311, -0.903703, -0.427809]], 'translation vector': [-0.207113, 0.785695, 1.605991]}\nB: {'rotation matrix': [[0.9999995913321388, 0.0005903556746734515, -0.0005873839987845575], [-0.0005915541354645518, 0.9999966980036584, -0.0023348317149788846], [0.0005870160507556152, 0.00233575631512544, 0.9999968115853851]], 'translation vector': [0.0016624461367128474, -0.0028184747771204943, -0.001607026045218174]}\nC: {'rotation matrix': [[0.628117, -0.317695, 0.71031], [-0.777888, -0.278629, 0.563255], [0.018969, -0.906331, -0.422142]], 'translation vector': [-0.210483, 0.781575, 1.607029]}\nD: {'rotation matrix': [[0.626043, -0.313657, 0.713926], [-0.779508, -0.27628, 0.562171], [0.020914, -0.908454, -0.417462]], 'translation vector': [-0.212996, 0.77858, 1.610364]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999006085846415, 0.014105987403183131, 0.0007516964397581732], [-0.014115634211093402, 0.9997972933579915, 0.014373215967786759], [-0.0005489990557007766, -0.014382715808085993, 0.9998969169271865]], 'translation vector': [-0.02535757146051898, 0.0018154305901567636, 0.010236799804218322]}\nB: {'rotation matrix': [[0.151948, 0.599833, -0.785565], [0.987995, -0.114601, 0.103597], [-0.027885, -0.791875, -0.610046]], 'translation vector': [3.432288, 3.133084, 1.213871]}\nC: {'rotation matrix': [[0.14922, 0.604558, -0.78246], [0.988532, -0.109774, 0.103704], [-0.023198, -0.788961, -0.614005]], 'translation vector': [3.429968, 3.121084, 1.211424]}\nD: {'rotation matrix': [[0.14748, 0.608832, -0.77947], [0.988883, -0.105872, 0.104407], [-0.018958, -0.786202, -0.617678]], 'translation vector': [3.426714, 3.1102, 1.209074]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_81_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_81_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_81_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_81_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999006085846415, 0.014105987403183131, 0.0007516964397581732], [-0.014115634211093402, 0.9997972933579915, 0.014373215967786759], [-0.0005489990557007766, -0.014382715808085993, 0.9998969169271865]], 'translation vector': [-0.02535757146051898, 0.0018154305901567636, 0.010236799804218322]}\nB: {'rotation matrix': [[0.151948, 0.599833, -0.785565], [0.987995, -0.114601, 0.103597], [-0.027885, -0.791875, -0.610046]], 'translation vector': [3.432288, 3.133084, 1.213871]}\nC: {'rotation matrix': [[0.14922, 0.604558, -0.78246], [0.988532, -0.109774, 0.103704], [-0.023198, -0.788961, -0.614005]], 'translation vector': [3.429968, 3.121084, 1.211424]}\nD: {'rotation matrix': [[0.14748, 0.608832, -0.77947], [0.988883, -0.105872, 0.104407], [-0.018958, -0.786202, -0.617678]], 'translation vector': [3.426714, 3.1102, 1.209074]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.720072, 0.306191, -0.62269], [0.693309, -0.280455, 0.663829], [0.028622, -0.909721, -0.414232]], 'translation vector': [3.433251, 3.053234, 1.552574]}\nB: {'rotation matrix': [[0.715824, 0.307759, -0.626802], [0.697706, -0.278807, 0.659904], [0.028335, -0.909698, -0.414302]], 'translation vector': [3.42786, 3.050569, 1.552797]}\nC: {'rotation matrix': [[0.99998073687791, -0.005433231351435887, 0.0028092605219069734], [0.0054430621429484745, 0.9999793374936902, -0.0033485077732525377], [-0.00279094918696337, 0.0033635449074639256, 0.9999906279011188]], 'translation vector': [0.0048247922808197785, -0.007326500694675886, 7.779843116662022e-05]}\nD: {'rotation matrix': [[0.717959, 0.306862, -0.624797], [0.695515, -0.279904, 0.66175], [0.028183, -0.909665, -0.414386]], 'translation vector': [3.431505, 3.053102, 1.552563]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_82_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_82_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_82_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_82_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.720072, 0.306191, -0.62269], [0.693309, -0.280455, 0.663829], [0.028622, -0.909721, -0.414232]], 'translation vector': [3.433251, 3.053234, 1.552574]}\nB: {'rotation matrix': [[0.715824, 0.307759, -0.626802], [0.697706, -0.278807, 0.659904], [0.028335, -0.909698, -0.414302]], 'translation vector': [3.42786, 3.050569, 1.552797]}\nC: {'rotation matrix': [[0.99998073687791, -0.005433231351435887, 0.0028092605219069734], [0.0054430621429484745, 0.9999793374936902, -0.0033485077732525377], [-0.00279094918696337, 0.0033635449074639256, 0.9999906279011188]], 'translation vector': [0.0048247922808197785, -0.007326500694675886, 7.779843116662022e-05]}\nD: {'rotation matrix': [[0.717959, 0.306862, -0.624797], [0.695515, -0.279904, 0.66175], [0.028183, -0.909665, -0.414386]], 'translation vector': [3.431505, 3.053102, 1.552563]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.863341, -0.254981, 0.435461], [-0.503057, 0.367008, -0.782457], [0.039694, -0.894589, -0.445123]], 'translation vector': [2.006748, 3.81545, 1.542323]}\nB: {'rotation matrix': [[-0.863173, -0.254818, 0.43589], [-0.503388, 0.367381, -0.782069], [0.039148, -0.894483, -0.445386]], 'translation vector': [2.007018, 3.816806, 1.542476]}\nC: {'rotation matrix': [[-0.863454, -0.255279, 0.435064], [-0.502805, 0.366433, -0.782888], [0.040433, -0.89474, -0.444754]], 'translation vector': [2.007318, 3.814646, 1.54216]}\nD: {'rotation matrix': [[0.9999972277888285, -0.0019486605164344517, 0.0010264732410869921], [0.0019490971963479567, 0.9999969267726074, -0.0010703044149313807], [-0.0010241404575203601, 0.0010719252856060263, 0.9999989421516985]], 'translation vector': [-0.0025894589048998107, 0.007141119527829198, -0.0014552230705469071]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_83_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_83_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_83_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_83_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.863341, -0.254981, 0.435461], [-0.503057, 0.367008, -0.782457], [0.039694, -0.894589, -0.445123]], 'translation vector': [2.006748, 3.81545, 1.542323]}\nB: {'rotation matrix': [[-0.863173, -0.254818, 0.43589], [-0.503388, 0.367381, -0.782069], [0.039148, -0.894483, -0.445386]], 'translation vector': [2.007018, 3.816806, 1.542476]}\nC: {'rotation matrix': [[-0.863454, -0.255279, 0.435064], [-0.502805, 0.366433, -0.782888], [0.040433, -0.89474, -0.444754]], 'translation vector': [2.007318, 3.814646, 1.54216]}\nD: {'rotation matrix': [[0.9999972277888285, -0.0019486605164344517, 0.0010264732410869921], [0.0019490971963479567, 0.9999969267726074, -0.0010703044149313807], [-0.0010241404575203601, 0.0010719252856060263, 0.9999989421516985]], 'translation vector': [-0.0025894589048998107, 0.007141119527829198, -0.0014552230705469071]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.987787, 0.108072, -0.112241], [0.155697, -0.656841, 0.73778], [0.006009, -0.746244, -0.665645]], 'translation vector': [4.649458, 4.057209, 1.404581]}\nB: {'rotation matrix': [[0.988022, 0.106035, -0.112112], [0.15424, -0.656197, 0.738658], [0.004756, -0.747102, -0.664692]], 'translation vector': [4.650307, 4.057695, 1.405486]}\nC: {'rotation matrix': [[0.9999939235077703, -0.0009864268743032946, -0.003107875618402941], [0.0009789613139893545, 0.9999966009200537, -0.0022176346298812244], [0.003110843270450784, 0.0022141652882606867, 0.9999926277610204]], 'translation vector': [-0.007831508873088033, -0.00424079700623059, -0.0006393879079424902]}\nD: {'rotation matrix': [[0.987654, 0.108357, -0.113131], [0.15654, -0.65545, 0.738837], [0.005906, -0.747425, -0.66432]], 'translation vector': [4.648766, 4.054578, 1.401957]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_84_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_84_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_84_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_84_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.987787, 0.108072, -0.112241], [0.155697, -0.656841, 0.73778], [0.006009, -0.746244, -0.665645]], 'translation vector': [4.649458, 4.057209, 1.404581]}\nB: {'rotation matrix': [[0.988022, 0.106035, -0.112112], [0.15424, -0.656197, 0.738658], [0.004756, -0.747102, -0.664692]], 'translation vector': [4.650307, 4.057695, 1.405486]}\nC: {'rotation matrix': [[0.9999939235077703, -0.0009864268743032946, -0.003107875618402941], [0.0009789613139893545, 0.9999966009200537, -0.0022176346298812244], [0.003110843270450784, 0.0022141652882606867, 0.9999926277610204]], 'translation vector': [-0.007831508873088033, -0.00424079700623059, -0.0006393879079424902]}\nD: {'rotation matrix': [[0.987654, 0.108357, -0.113131], [0.15654, -0.65545, 0.738837], [0.005906, -0.747425, -0.66432]], 'translation vector': [4.648766, 4.054578, 1.401957]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9997717908276891, -0.014061705239683872, 0.016112763216433307], [0.0140800110532502, 0.9999007363199941, -0.0010460438789438157], [-0.01609611583081038, 0.0012720455953134791, 0.9998692891144138]], 'translation vector': [-0.018076948566243978, 0.0017768934909982992, 0.0006580952284183095]}\nB: {'rotation matrix': [[-0.782674, -0.257014, 0.566891], [-0.62216, 0.296092, -0.724739], [0.018416, -0.919931, -0.391647]], 'translation vector': [3.075882, 2.930909, 1.465913]}\nC: {'rotation matrix': [[-0.790591, -0.247688, 0.560015], [-0.61223, 0.301979, -0.730742], [0.011883, -0.920576, -0.390384]], 'translation vector': [3.085087, 2.935415, 1.467454]}\nD: {'rotation matrix': [[-0.773889, -0.266244, 0.574639], [-0.632944, 0.293814, -0.716279], [0.021868, -0.918034, -0.395897]], 'translation vector': [3.064209, 2.92712, 1.46117]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_85_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_85_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_85_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_85_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9997717908276891, -0.014061705239683872, 0.016112763216433307], [0.0140800110532502, 0.9999007363199941, -0.0010460438789438157], [-0.01609611583081038, 0.0012720455953134791, 0.9998692891144138]], 'translation vector': [-0.018076948566243978, 0.0017768934909982992, 0.0006580952284183095]}\nB: {'rotation matrix': [[-0.782674, -0.257014, 0.566891], [-0.62216, 0.296092, -0.724739], [0.018416, -0.919931, -0.391647]], 'translation vector': [3.075882, 2.930909, 1.465913]}\nC: {'rotation matrix': [[-0.790591, -0.247688, 0.560015], [-0.61223, 0.301979, -0.730742], [0.011883, -0.920576, -0.390384]], 'translation vector': [3.085087, 2.935415, 1.467454]}\nD: {'rotation matrix': [[-0.773889, -0.266244, 0.574639], [-0.632944, 0.293814, -0.716279], [0.021868, -0.918034, -0.395897]], 'translation vector': [3.064209, 2.92712, 1.46117]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.516456, 0.471348, -0.714916], [0.852952, -0.209256, 0.47821], [0.075803, -0.856763, -0.510109]], 'translation vector': [4.97866, 0.423553, 1.591931]}\nB: {'rotation matrix': [[0.514459, 0.473148, -0.715167], [0.854068, -0.208015, 0.476757], [0.076811, -0.856073, -0.511116]], 'translation vector': [4.979161, 0.423603, 1.588672]}\nC: {'rotation matrix': [[0.513176, 0.475448, -0.714563], [0.854688, -0.206948, 0.476112], [0.078489, -0.855056, -0.51256]], 'translation vector': [4.976408, 0.420953, 1.588878]}\nD: {'rotation matrix': [[0.9999760932186582, 0.0012257187804321542, -0.006834405192096566], [-0.001218782424370881, 0.999999818921041, 0.0008912149688576313], [0.006835564452074455, -0.0008827669412868106, 0.9999768915925589]], 'translation vector': [-0.008703367750305002, 0.013496314561282197, 0.004560884153690381]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_86_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_86_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_86_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_86_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.516456, 0.471348, -0.714916], [0.852952, -0.209256, 0.47821], [0.075803, -0.856763, -0.510109]], 'translation vector': [4.97866, 0.423553, 1.591931]}\nB: {'rotation matrix': [[0.514459, 0.473148, -0.715167], [0.854068, -0.208015, 0.476757], [0.076811, -0.856073, -0.511116]], 'translation vector': [4.979161, 0.423603, 1.588672]}\nC: {'rotation matrix': [[0.513176, 0.475448, -0.714563], [0.854688, -0.206948, 0.476112], [0.078489, -0.855056, -0.51256]], 'translation vector': [4.976408, 0.420953, 1.588878]}\nD: {'rotation matrix': [[0.9999760932186582, 0.0012257187804321542, -0.006834405192096566], [-0.001218782424370881, 0.999999818921041, 0.0008912149688576313], [0.006835564452074455, -0.0008827669412868106, 0.9999768915925589]], 'translation vector': [-0.008703367750305002, 0.013496314561282197, 0.004560884153690381]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.448824, 0.136877, -0.883075], [0.892984, 0.105987, -0.437432], [0.033721, -0.984902, -0.169799]], 'translation vector': [3.315047, 2.127717, 1.592265]}\nB: {'rotation matrix': [[-0.452202, 0.137197, -0.8813], [0.891317, 0.105713, -0.440885], [0.032677, -0.984887, -0.170089]], 'translation vector': [3.315698, 2.124716, 1.590659]}\nC: {'rotation matrix': [[-0.449366, 0.136914, -0.882794], [0.892692, 0.106685, -0.437859], [0.034232, -0.984821, -0.170162]], 'translation vector': [3.315906, 2.123902, 1.590809]}\nD: {'rotation matrix': [[0.9999985219155758, 3.3460926462502616e-05, -0.0017161305114821916], [-2.2107949587045813e-05, 0.9999776535003064, 0.006715346047559243], [0.0017166465785429835, -0.006715383734168117, 0.9999757458350781]], 'translation vector': [0.0004478029195722488, -0.00269782175769262, 0.002036977128338613]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_87_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_87_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_87_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_87_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.448824, 0.136877, -0.883075], [0.892984, 0.105987, -0.437432], [0.033721, -0.984902, -0.169799]], 'translation vector': [3.315047, 2.127717, 1.592265]}\nB: {'rotation matrix': [[-0.452202, 0.137197, -0.8813], [0.891317, 0.105713, -0.440885], [0.032677, -0.984887, -0.170089]], 'translation vector': [3.315698, 2.124716, 1.590659]}\nC: {'rotation matrix': [[-0.449366, 0.136914, -0.882794], [0.892692, 0.106685, -0.437859], [0.034232, -0.984821, -0.170162]], 'translation vector': [3.315906, 2.123902, 1.590809]}\nD: {'rotation matrix': [[0.9999985219155758, 3.3460926462502616e-05, -0.0017161305114821916], [-2.2107949587045813e-05, 0.9999776535003064, 0.006715346047559243], [0.0017166465785429835, -0.006715383734168117, 0.9999757458350781]], 'translation vector': [0.0004478029195722488, -0.00269782175769262, 0.002036977128338613]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.993234, -0.016226, -0.114989], [0.102235, -0.347461, 0.932104], [-0.055078, -0.937554, -0.343451]], 'translation vector': [2.952414, 4.433719, 1.459459]}\nB: {'rotation matrix': [[0.993467, -0.015486, -0.113064], [0.100672, -0.347655, 0.932202], [-0.053743, -0.937495, -0.343825]], 'translation vector': [2.95506, 4.435545, 1.464879]}\nC: {'rotation matrix': [[0.9999909736646528, 0.004123633453855257, 0.0014048261378039103], [-0.004123349991970072, 0.9999914014789152, -0.00016491391217041438], [-0.0014064329904194771, 0.0001595777583995875, 0.9999985433300184]], 'translation vector': [-0.0007043054873738797, 0.0030876829023283037, 0.0005875691155496909]}\nD: {'rotation matrix': [[0.993543, -0.018943, -0.111866], [0.098443, -0.346258, 0.93296], [-0.056408, -0.937948, -0.342158]], 'translation vector': [2.958581, 4.436487, 1.463224]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_88_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_88_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_88_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_88_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.993234, -0.016226, -0.114989], [0.102235, -0.347461, 0.932104], [-0.055078, -0.937554, -0.343451]], 'translation vector': [2.952414, 4.433719, 1.459459]}\nB: {'rotation matrix': [[0.993467, -0.015486, -0.113064], [0.100672, -0.347655, 0.932202], [-0.053743, -0.937495, -0.343825]], 'translation vector': [2.95506, 4.435545, 1.464879]}\nC: {'rotation matrix': [[0.9999909736646528, 0.004123633453855257, 0.0014048261378039103], [-0.004123349991970072, 0.9999914014789152, -0.00016491391217041438], [-0.0014064329904194771, 0.0001595777583995875, 0.9999985433300184]], 'translation vector': [-0.0007043054873738797, 0.0030876829023283037, 0.0005875691155496909]}\nD: {'rotation matrix': [[0.993543, -0.018943, -0.111866], [0.098443, -0.346258, 0.93296], [-0.056408, -0.937948, -0.342158]], 'translation vector': [2.958581, 4.436487, 1.463224]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.481483, 0.389974, -0.784917], [0.875782, -0.249176, 0.413422], [-0.034359, -0.886471, -0.461507]], 'translation vector': [2.949051, 2.713893, 1.478454]}\nB: {'rotation matrix': [[0.478541, 0.391858, -0.785777], [0.877371, -0.248969, 0.410164], [-0.034908, -0.885699, -0.462947]], 'translation vector': [2.947931, 2.717417, 1.47825]}\nC: {'rotation matrix': [[0.48013, 0.390884, -0.785293], [0.876512, -0.249161, 0.41188], [-0.034667, -0.886075, -0.462245]], 'translation vector': [2.948499, 2.715565, 1.478062]}\nD: {'rotation matrix': [[0.9999997612782662, 0.0004988640565728734, 0.0011620670948559774], [-0.0004998749589789403, 0.9999982856757066, 0.0017162390398127588], [-0.0011605634579595124, -0.0017165704161403005, 0.9999970539013514]], 'translation vector': [0.0003215494383659312, -0.003127483043635193, -0.0005499886913977736]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_89_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_89_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_89_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_89_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.481483, 0.389974, -0.784917], [0.875782, -0.249176, 0.413422], [-0.034359, -0.886471, -0.461507]], 'translation vector': [2.949051, 2.713893, 1.478454]}\nB: {'rotation matrix': [[0.478541, 0.391858, -0.785777], [0.877371, -0.248969, 0.410164], [-0.034908, -0.885699, -0.462947]], 'translation vector': [2.947931, 2.717417, 1.47825]}\nC: {'rotation matrix': [[0.48013, 0.390884, -0.785293], [0.876512, -0.249161, 0.41188], [-0.034667, -0.886075, -0.462245]], 'translation vector': [2.948499, 2.715565, 1.478062]}\nD: {'rotation matrix': [[0.9999997612782662, 0.0004988640565728734, 0.0011620670948559774], [-0.0004998749589789403, 0.9999982856757066, 0.0017162390398127588], [-0.0011605634579595124, -0.0017165704161403005, 0.9999970539013514]], 'translation vector': [0.0003215494383659312, -0.003127483043635193, -0.0005499886913977736]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.564799, -0.352124, 0.746332], [-0.825057, 0.22251, -0.519395], [0.016825, -0.909119, -0.416196]], 'translation vector': [2.054545, 3.84102, 1.387591]}\nB: {'rotation matrix': [[-0.564546, -0.353818, 0.745722], [-0.825222, 0.223074, -0.518891], [0.017242, -0.908323, -0.417914]], 'translation vector': [2.054274, 3.838, 1.389919]}\nC: {'rotation matrix': [[0.9999863571433116, 0.000569020369849223, 0.005277349616356428], [-0.0005964877721919992, 0.9999870648779766, 0.0050113119474318605], [-0.005273998232851741, -0.005013939911459714, 0.9999743290291354]], 'translation vector': [-0.009394794053914524, 0.0032248655541047277, -0.0014403793043347157]}\nD: {'rotation matrix': [[-0.566299, -0.350153, 0.746122], [-0.824022, 0.221689, -0.521385], [0.017157, -0.91008, -0.414077]], 'translation vector': [2.055187, 3.843729, 1.385575]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_90_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_90_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_90_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_90_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.564799, -0.352124, 0.746332], [-0.825057, 0.22251, -0.519395], [0.016825, -0.909119, -0.416196]], 'translation vector': [2.054545, 3.84102, 1.387591]}\nB: {'rotation matrix': [[-0.564546, -0.353818, 0.745722], [-0.825222, 0.223074, -0.518891], [0.017242, -0.908323, -0.417914]], 'translation vector': [2.054274, 3.838, 1.389919]}\nC: {'rotation matrix': [[0.9999863571433116, 0.000569020369849223, 0.005277349616356428], [-0.0005964877721919992, 0.9999870648779766, 0.0050113119474318605], [-0.005273998232851741, -0.005013939911459714, 0.9999743290291354]], 'translation vector': [-0.009394794053914524, 0.0032248655541047277, -0.0014403793043347157]}\nD: {'rotation matrix': [[-0.566299, -0.350153, 0.746122], [-0.824022, 0.221689, -0.521385], [0.017157, -0.91008, -0.414077]], 'translation vector': [2.055187, 3.843729, 1.385575]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.037266, 0.594373, -0.803326], [0.998697, -0.005895, -0.05069], [-0.034865, -0.804168, -0.593379]], 'translation vector': [3.957977, 2.244087, 1.44004]}\nB: {'rotation matrix': [[0.9999996431737382, -0.0003859815143722872, 0.000745633467728039], [0.0003848347144862704, 1.0000000520852899, 0.00069936137332727], [-0.0007460186813055825, -0.0006987638070698045, 0.9999991664939947]], 'translation vector': [0.0003220625244955144, -0.0016866012265464025, 0.00017566976974592308]}\nC: {'rotation matrix': [[-0.03699, 0.597433, -0.801066], [0.998659, -0.006964, -0.051308], [-0.036231, -0.801889, -0.596374]], 'translation vector': [3.95766, 2.242744, 1.440408]}\nD: {'rotation matrix': [[-0.039909, 0.596654, -0.801506], [0.998413, -0.00808, -0.055729], [-0.039727, -0.802458, -0.595385]], 'translation vector': [3.959598, 2.247142, 1.43878]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_91_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_91_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_91_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_91_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.037266, 0.594373, -0.803326], [0.998697, -0.005895, -0.05069], [-0.034865, -0.804168, -0.593379]], 'translation vector': [3.957977, 2.244087, 1.44004]}\nB: {'rotation matrix': [[0.9999996431737382, -0.0003859815143722872, 0.000745633467728039], [0.0003848347144862704, 1.0000000520852899, 0.00069936137332727], [-0.0007460186813055825, -0.0006987638070698045, 0.9999991664939947]], 'translation vector': [0.0003220625244955144, -0.0016866012265464025, 0.00017566976974592308]}\nC: {'rotation matrix': [[-0.03699, 0.597433, -0.801066], [0.998659, -0.006964, -0.051308], [-0.036231, -0.801889, -0.596374]], 'translation vector': [3.95766, 2.242744, 1.440408]}\nD: {'rotation matrix': [[-0.039909, 0.596654, -0.801506], [0.998413, -0.00808, -0.055729], [-0.039727, -0.802458, -0.595385]], 'translation vector': [3.959598, 2.247142, 1.43878]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.875953, -0.064411, 0.478078], [-0.480708, 0.199407, -0.853907], [-0.040331, -0.977798, -0.205634]], 'translation vector': [2.420033, 1.712699, 1.489589]}\nB: {'rotation matrix': [[0.9999951897323609, 0.002443562746852685, 0.0019815417907405445], [-0.0024514031523570133, 0.9999905126626161, 0.0035483645433095957], [-0.001973097402749593, -0.0035542813588159473, 0.9999914538041953]], 'translation vector': [0.004274186437530858, 0.003113397542267471, -0.0028983696999268505]}\nC: {'rotation matrix': [[-0.873782, -0.064754, 0.481987], [-0.484652, 0.197901, -0.852026], [-0.040214, -0.978081, -0.204306]], 'translation vector': [2.408279, 1.71933, 1.490834]}\nD: {'rotation matrix': [[-0.874383, -0.064777, 0.480894], [-0.483517, 0.199683, -0.852255], [-0.04082, -0.977717, -0.20592]], 'translation vector': [2.41403, 1.715424, 1.490696]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_92_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_92_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_92_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_92_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.875953, -0.064411, 0.478078], [-0.480708, 0.199407, -0.853907], [-0.040331, -0.977798, -0.205634]], 'translation vector': [2.420033, 1.712699, 1.489589]}\nB: {'rotation matrix': [[0.9999951897323609, 0.002443562746852685, 0.0019815417907405445], [-0.0024514031523570133, 0.9999905126626161, 0.0035483645433095957], [-0.001973097402749593, -0.0035542813588159473, 0.9999914538041953]], 'translation vector': [0.004274186437530858, 0.003113397542267471, -0.0028983696999268505]}\nC: {'rotation matrix': [[-0.873782, -0.064754, 0.481987], [-0.484652, 0.197901, -0.852026], [-0.040214, -0.978081, -0.204306]], 'translation vector': [2.408279, 1.71933, 1.490834]}\nD: {'rotation matrix': [[-0.874383, -0.064777, 0.480894], [-0.483517, 0.199683, -0.852255], [-0.04082, -0.977717, -0.20592]], 'translation vector': [2.41403, 1.715424, 1.490696]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.222027, -0.462333, 0.858459], [-0.974308, 0.139332, -0.176951], [-0.0378, -0.875691, -0.48139]], 'translation vector': [2.717101, 1.647348, 1.522281]}\nB: {'rotation matrix': [[-0.218431, -0.46311, 0.858963], [-0.975079, 0.138612, -0.173227], [-0.038839, -0.875395, -0.481846]], 'translation vector': [2.716881, 1.647519, 1.52132]}\nC: {'rotation matrix': [[-0.21644, -0.463451, 0.859283], [-0.975487, 0.138488, -0.171017], [-0.039742, -0.875234, -0.482065]], 'translation vector': [2.718464, 1.6518, 1.521331]}\nD: {'rotation matrix': [[0.9999923983379827, -0.0004070717907623284, -0.003958509569048427], [0.0004054339454520325, 1.0000004886800318, -0.0002774375137664041], [0.003959398020789269, 0.00027564706640871675, 0.9999922541189072]], 'translation vector': [-0.005217870906849331, -0.0010876072780465762, 0.0013190166897232292]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_93_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_93_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_93_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_93_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.222027, -0.462333, 0.858459], [-0.974308, 0.139332, -0.176951], [-0.0378, -0.875691, -0.48139]], 'translation vector': [2.717101, 1.647348, 1.522281]}\nB: {'rotation matrix': [[-0.218431, -0.46311, 0.858963], [-0.975079, 0.138612, -0.173227], [-0.038839, -0.875395, -0.481846]], 'translation vector': [2.716881, 1.647519, 1.52132]}\nC: {'rotation matrix': [[-0.21644, -0.463451, 0.859283], [-0.975487, 0.138488, -0.171017], [-0.039742, -0.875234, -0.482065]], 'translation vector': [2.718464, 1.6518, 1.521331]}\nD: {'rotation matrix': [[0.9999923983379827, -0.0004070717907623284, -0.003958509569048427], [0.0004054339454520325, 1.0000004886800318, -0.0002774375137664041], [0.003959398020789269, 0.00027564706640871675, 0.9999922541189072]], 'translation vector': [-0.005217870906849331, -0.0010876072780465762, 0.0013190166897232292]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.937153, -0.143335, 0.318118], [-0.348912, 0.379164, -0.857027], [0.002223, -0.914161, -0.405346]], 'translation vector': [2.695696, 2.482015, 1.468683]}\nB: {'rotation matrix': [[0.9999943196573092, 0.0004252932585082039, -0.003407353327997387], [-0.0004083231357033075, 0.9999884704113977, 0.004802465167927532], [0.0034098975295442984, -0.004801063633517885, 0.9999828785878939]], 'translation vector': [4.878723511492211e-05, -0.0002916568078848991, 6.338042778675224e-05]}\nC: {'rotation matrix': [[-0.936491, -0.141969, 0.320669], [-0.350691, 0.379755, -0.856039], [-0.000244, -0.914129, -0.405424]], 'translation vector': [2.694833, 2.48135, 1.466405]}\nD: {'rotation matrix': [[-0.938082, -0.144128, 0.315008], [-0.346377, 0.376958, -0.859026], [0.005065, -0.914948, -0.40354]], 'translation vector': [2.697706, 2.481531, 1.470994]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_94_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_94_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_94_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_94_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.937153, -0.143335, 0.318118], [-0.348912, 0.379164, -0.857027], [0.002223, -0.914161, -0.405346]], 'translation vector': [2.695696, 2.482015, 1.468683]}\nB: {'rotation matrix': [[0.9999943196573092, 0.0004252932585082039, -0.003407353327997387], [-0.0004083231357033075, 0.9999884704113977, 0.004802465167927532], [0.0034098975295442984, -0.004801063633517885, 0.9999828785878939]], 'translation vector': [4.878723511492211e-05, -0.0002916568078848991, 6.338042778675224e-05]}\nC: {'rotation matrix': [[-0.936491, -0.141969, 0.320669], [-0.350691, 0.379755, -0.856039], [-0.000244, -0.914129, -0.405424]], 'translation vector': [2.694833, 2.48135, 1.466405]}\nD: {'rotation matrix': [[-0.938082, -0.144128, 0.315008], [-0.346377, 0.376958, -0.859026], [0.005065, -0.914948, -0.40354]], 'translation vector': [2.697706, 2.481531, 1.470994]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999926818767, 0.0006670318036002099, -0.0037525660402691484], [-0.0006413298116955215, 0.9999778975651503, 0.0065610016234285765], [0.0037569249243787368, -0.006558428485894876, 0.999971386151166]], 'translation vector': [0.00407147231806082, -0.002381515530327949, 0.00020808612264033854]}\nB: {'rotation matrix': [[0.598948, -0.354434, 0.718079], [-0.795274, -0.158225, 0.585238], [-0.093811, -0.921597, -0.376641]], 'translation vector': [2.366687, 6.228749, 1.483315]}\nC: {'rotation matrix': [[0.595688, -0.354051, 0.720975], [-0.797698, -0.155728, 0.582604], [-0.093996, -0.92217, -0.37519]], 'translation vector': [2.365015, 6.231124, 1.484416]}\nD: {'rotation matrix': [[0.602088, -0.354098, 0.715615], [-0.793005, -0.160904, 0.587582], [-0.092916, -0.921263, -0.37768]], 'translation vector': [2.370181, 6.228135, 1.483056]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_95_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_95_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_95_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_95_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999926818767, 0.0006670318036002099, -0.0037525660402691484], [-0.0006413298116955215, 0.9999778975651503, 0.0065610016234285765], [0.0037569249243787368, -0.006558428485894876, 0.999971386151166]], 'translation vector': [0.00407147231806082, -0.002381515530327949, 0.00020808612264033854]}\nB: {'rotation matrix': [[0.598948, -0.354434, 0.718079], [-0.795274, -0.158225, 0.585238], [-0.093811, -0.921597, -0.376641]], 'translation vector': [2.366687, 6.228749, 1.483315]}\nC: {'rotation matrix': [[0.595688, -0.354051, 0.720975], [-0.797698, -0.155728, 0.582604], [-0.093996, -0.92217, -0.37519]], 'translation vector': [2.365015, 6.231124, 1.484416]}\nD: {'rotation matrix': [[0.602088, -0.354098, 0.715615], [-0.793005, -0.160904, 0.587582], [-0.092916, -0.921263, -0.37768]], 'translation vector': [2.370181, 6.228135, 1.483056]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.749807, 0.343159, -0.565713], [0.658704, 0.306467, -0.687158], [-0.062432, -0.887874, -0.455832]], 'translation vector': [3.78087, 2.559782, 1.382918]}\nB: {'rotation matrix': [[0.9999937348891738, -5.242465291256345e-05, -0.003554980216800454], [4.743594033927849e-05, 0.9999987930802742, -0.0013704756295102494], [0.0035549170275783653, 0.0013698614468464884, 0.9999933438233037]], 'translation vector': [-0.006430893779766134, -0.00441205739948114, -0.013902381507369554]}\nC: {'rotation matrix': [[-0.753941, 0.344397, -0.559431], [0.653858, 0.310968, -0.68976], [-0.063586, -0.885827, -0.459639]], 'translation vector': [3.768856, 2.553297, 1.380708]}\nD: {'rotation matrix': [[-0.758857, 0.345337, -0.552158], [0.64782, 0.313263, -0.694403], [-0.066832, -0.884652, -0.461438]], 'translation vector': [3.75736, 2.54705, 1.379026]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_96_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_96_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_96_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_96_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.749807, 0.343159, -0.565713], [0.658704, 0.306467, -0.687158], [-0.062432, -0.887874, -0.455832]], 'translation vector': [3.78087, 2.559782, 1.382918]}\nB: {'rotation matrix': [[0.9999937348891738, -5.242465291256345e-05, -0.003554980216800454], [4.743594033927849e-05, 0.9999987930802742, -0.0013704756295102494], [0.0035549170275783653, 0.0013698614468464884, 0.9999933438233037]], 'translation vector': [-0.006430893779766134, -0.00441205739948114, -0.013902381507369554]}\nC: {'rotation matrix': [[-0.753941, 0.344397, -0.559431], [0.653858, 0.310968, -0.68976], [-0.063586, -0.885827, -0.459639]], 'translation vector': [3.768856, 2.553297, 1.380708]}\nD: {'rotation matrix': [[-0.758857, 0.345337, -0.552158], [0.64782, 0.313263, -0.694403], [-0.066832, -0.884652, -0.461438]], 'translation vector': [3.75736, 2.54705, 1.379026]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999813823639969, 0.005886631638650806, 0.0017617762591203541], [-0.005897799441000984, 0.9999663260190635, 0.005653603868593601], [-0.0017278375660129883, -0.005663714705693189, 0.9999828776446472]], 'translation vector': [-0.009345482457980114, -0.0002560144178671564, 0.004418422526778709]}\nB: {'rotation matrix': [[0.930353, -0.229821, 0.285704], [-0.366636, -0.593139, 0.716774], [0.004732, -0.771601, -0.636089]], 'translation vector': [0.347034, 1.978598, 1.559374]}\nC: {'rotation matrix': [[0.932658, -0.225033, 0.281975], [-0.360742, -0.590178, 0.722188], [0.003899, -0.775274, -0.631613]], 'translation vector': [0.341015, 1.979035, 1.553548]}\nD: {'rotation matrix': [[0.93165, -0.227962, 0.282953], [-0.363337, -0.592976, 0.718587], [0.003973, -0.772278, -0.635273]], 'translation vector': [0.345174, 1.978811, 1.55669]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_97_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_97_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_97_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_97_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999813823639969, 0.005886631638650806, 0.0017617762591203541], [-0.005897799441000984, 0.9999663260190635, 0.005653603868593601], [-0.0017278375660129883, -0.005663714705693189, 0.9999828776446472]], 'translation vector': [-0.009345482457980114, -0.0002560144178671564, 0.004418422526778709]}\nB: {'rotation matrix': [[0.930353, -0.229821, 0.285704], [-0.366636, -0.593139, 0.716774], [0.004732, -0.771601, -0.636089]], 'translation vector': [0.347034, 1.978598, 1.559374]}\nC: {'rotation matrix': [[0.932658, -0.225033, 0.281975], [-0.360742, -0.590178, 0.722188], [0.003899, -0.775274, -0.631613]], 'translation vector': [0.341015, 1.979035, 1.553548]}\nD: {'rotation matrix': [[0.93165, -0.227962, 0.282953], [-0.363337, -0.592976, 0.718587], [0.003973, -0.772278, -0.635273]], 'translation vector': [0.345174, 1.978811, 1.55669]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.805636, 0.331099, -0.491249], [0.591886, 0.41495, -0.691005], [-0.024947, -0.847461, -0.530271]], 'translation vector': [2.379813, 3.089217, 1.318416]}\nB: {'rotation matrix': [[-0.795605, 0.337599, -0.503031], [0.605104, 0.402607, -0.686846], [-0.029355, -0.850844, -0.524598]], 'translation vector': [2.393777, 3.105406, 1.314663]}\nC: {'rotation matrix': [[0.9993623988750846, 0.01800106003136589, -0.030848731804103247], [-0.01802560053443595, 0.9998375187449363, -0.0005268676766097016], [0.03083392596568167, 0.0010819486754358148, 0.9995239906208094]], 'translation vector': [-0.0016360885893116628, -0.010945290948126685, 0.024052024973950203]}\nD: {'rotation matrix': [[-0.800158, 0.334375, -0.497936], [0.599132, 0.406738, -0.689642], [-0.02807, -0.850152, -0.525789]], 'translation vector': [2.38798, 3.097038, 1.316188]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_98_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_98_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_98_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_98_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.805636, 0.331099, -0.491249], [0.591886, 0.41495, -0.691005], [-0.024947, -0.847461, -0.530271]], 'translation vector': [2.379813, 3.089217, 1.318416]}\nB: {'rotation matrix': [[-0.795605, 0.337599, -0.503031], [0.605104, 0.402607, -0.686846], [-0.029355, -0.850844, -0.524598]], 'translation vector': [2.393777, 3.105406, 1.314663]}\nC: {'rotation matrix': [[0.9993623988750846, 0.01800106003136589, -0.030848731804103247], [-0.01802560053443595, 0.9998375187449363, -0.0005268676766097016], [0.03083392596568167, 0.0010819486754358148, 0.9995239906208094]], 'translation vector': [-0.0016360885893116628, -0.010945290948126685, 0.024052024973950203]}\nD: {'rotation matrix': [[-0.800158, 0.334375, -0.497936], [0.599132, 0.406738, -0.689642], [-0.02807, -0.850152, -0.525789]], 'translation vector': [2.38798, 3.097038, 1.316188]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.991094, 0.095151, -0.093156], [0.13246, 0.632786, -0.762913], [-0.013644, -0.768458, -0.639754]], 'translation vector': [1.823914, 5.346199, 1.288239]}\nB: {'rotation matrix': [[-0.988726, 0.104422, -0.107319], [0.149216, 0.627347, -0.764311], [-0.012484, -0.771707, -0.635855]], 'translation vector': [1.82699, 5.341948, 1.287049]}\nC: {'rotation matrix': [[-0.99302, 0.087717, -0.078848], [0.116766, 0.636826, -0.762115], [-0.016638, -0.766002, -0.642623]], 'translation vector': [1.820977, 5.35315, 1.28763]}\nD: {'rotation matrix': [[0.9995983225918212, 0.019501636556654815, -0.020561779482543004], [-0.019155805749363774, 0.9996737108067424, 0.016883013818148294], [0.020884207942970644, -0.01648212192569651, 0.9996460764084063]], 'translation vector': [0.004177467169132809, 0.0037647300644172432, -0.008346822766605477]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_99_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_99_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_99_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_99_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.991094, 0.095151, -0.093156], [0.13246, 0.632786, -0.762913], [-0.013644, -0.768458, -0.639754]], 'translation vector': [1.823914, 5.346199, 1.288239]}\nB: {'rotation matrix': [[-0.988726, 0.104422, -0.107319], [0.149216, 0.627347, -0.764311], [-0.012484, -0.771707, -0.635855]], 'translation vector': [1.82699, 5.341948, 1.287049]}\nC: {'rotation matrix': [[-0.99302, 0.087717, -0.078848], [0.116766, 0.636826, -0.762115], [-0.016638, -0.766002, -0.642623]], 'translation vector': [1.820977, 5.35315, 1.28763]}\nD: {'rotation matrix': [[0.9995983225918212, 0.019501636556654815, -0.020561779482543004], [-0.019155805749363774, 0.9996737108067424, 0.016883013818148294], [0.020884207942970644, -0.01648212192569651, 0.9996460764084063]], 'translation vector': [0.004177467169132809, 0.0037647300644172432, -0.008346822766605477]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.821677, -0.468788, 0.324168], [-0.569738, 0.691217, -0.444543], [-0.015674, -0.549962, -0.835043]], 'translation vector': [3.090628, 8.002418, 1.936363]}\nB: {'rotation matrix': [[-0.828255, -0.463511, 0.314882], [-0.560231, 0.696605, -0.4482], [-0.011603, -0.547631, -0.83664]], 'translation vector': [3.092081, 8.003743, 1.933112]}\nC: {'rotation matrix': [[-0.825245, -0.467159, 0.317384], [-0.564664, 0.693585, -0.44732], [-0.011164, -0.548364, -0.836165]], 'translation vector': [3.09483, 8.004893, 1.934166]}\nD: {'rotation matrix': [[0.9997794014417315, -0.01936250651215195, 0.008149128878679036], [0.019327506819120155, 0.9998035065129168, 0.004379447300995329], [-0.008232031850993044, -0.004222057574502129, 0.9999569918430281]], 'translation vector': [0.008428449014058259, -0.001816843944377755, 0.00043274814993932154]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_100_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_100_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_100_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_100_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.821677, -0.468788, 0.324168], [-0.569738, 0.691217, -0.444543], [-0.015674, -0.549962, -0.835043]], 'translation vector': [3.090628, 8.002418, 1.936363]}\nB: {'rotation matrix': [[-0.828255, -0.463511, 0.314882], [-0.560231, 0.696605, -0.4482], [-0.011603, -0.547631, -0.83664]], 'translation vector': [3.092081, 8.003743, 1.933112]}\nC: {'rotation matrix': [[-0.825245, -0.467159, 0.317384], [-0.564664, 0.693585, -0.44732], [-0.011164, -0.548364, -0.836165]], 'translation vector': [3.09483, 8.004893, 1.934166]}\nD: {'rotation matrix': [[0.9997794014417315, -0.01936250651215195, 0.008149128878679036], [0.019327506819120155, 0.9998035065129168, 0.004379447300995329], [-0.008232031850993044, -0.004222057574502129, 0.9999569918430281]], 'translation vector': [0.008428449014058259, -0.001816843944377755, 0.00043274814993932154]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.141521, 0.444417, -0.884571], [0.989842, -0.075821, 0.12027], [-0.013619, -0.892605, -0.450633]], 'translation vector': [3.547713, 0.933243, 1.481136]}\nB: {'rotation matrix': [[0.9999998646080918, -0.0007460665530377528, -0.0009402946708239287], [0.0007455507104745661, 1.0000000002230558, -0.0004258129745529856], [0.0009407423454210515, 0.0004259725827619916, 0.9999998211939707]], 'translation vector': [0.0008665006475636616, -0.0026059059125004003, -0.0011105548234366935]}\nC: {'rotation matrix': [[0.140147, 0.44482, -0.884587], [0.990025, -0.076044, 0.118612], [-0.014506, -0.892386, -0.45104]], 'translation vector': [3.548717, 0.935529, 1.481701]}\nD: {'rotation matrix': [[0.140907, 0.444584, -0.884585], [0.989916, -0.076415, 0.11928], [-0.014565, -0.892472, -0.450868]], 'translation vector': [3.549046, 0.934745, 1.482359]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_101_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_101_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_101_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_101_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.141521, 0.444417, -0.884571], [0.989842, -0.075821, 0.12027], [-0.013619, -0.892605, -0.450633]], 'translation vector': [3.547713, 0.933243, 1.481136]}\nB: {'rotation matrix': [[0.9999998646080918, -0.0007460665530377528, -0.0009402946708239287], [0.0007455507104745661, 1.0000000002230558, -0.0004258129745529856], [0.0009407423454210515, 0.0004259725827619916, 0.9999998211939707]], 'translation vector': [0.0008665006475636616, -0.0026059059125004003, -0.0011105548234366935]}\nC: {'rotation matrix': [[0.140147, 0.44482, -0.884587], [0.990025, -0.076044, 0.118612], [-0.014506, -0.892386, -0.45104]], 'translation vector': [3.548717, 0.935529, 1.481701]}\nD: {'rotation matrix': [[0.140907, 0.444584, -0.884585], [0.989916, -0.076415, 0.11928], [-0.014565, -0.892472, -0.450868]], 'translation vector': [3.549046, 0.934745, 1.482359]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999988361601905, 0.001006375069335518, -0.0012640568426656846], [-0.0010070223030151592, 0.9999994457401622, -0.0002863718291939501], [0.00126333848912293, 0.0002872673648295308, 0.9999986249179557]], 'translation vector': [-2.298300292791211e-05, -0.0003253221346838364, -0.001208803459929797]}\nB: {'rotation matrix': [[0.209622, 0.494864, -0.843308], [0.976967, -0.070778, 0.201312], [0.039935, -0.866083, -0.498303]], 'translation vector': [4.529501, 2.292687, 1.525847]}\nC: {'rotation matrix': [[0.210084, 0.49423, -0.843565], [0.976909, -0.071791, 0.201231], [0.038894, -0.866362, -0.4979]], 'translation vector': [4.52972, 2.291977, 1.52688]}\nD: {'rotation matrix': [[0.207746, 0.495681, -0.843292], [0.977345, -0.069508, 0.199914], [0.040478, -0.865719, -0.498891]], 'translation vector': [4.528935, 2.293617, 1.525752]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_102_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_102_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_102_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_102_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999988361601905, 0.001006375069335518, -0.0012640568426656846], [-0.0010070223030151592, 0.9999994457401622, -0.0002863718291939501], [0.00126333848912293, 0.0002872673648295308, 0.9999986249179557]], 'translation vector': [-2.298300292791211e-05, -0.0003253221346838364, -0.001208803459929797]}\nB: {'rotation matrix': [[0.209622, 0.494864, -0.843308], [0.976967, -0.070778, 0.201312], [0.039935, -0.866083, -0.498303]], 'translation vector': [4.529501, 2.292687, 1.525847]}\nC: {'rotation matrix': [[0.210084, 0.49423, -0.843565], [0.976909, -0.071791, 0.201231], [0.038894, -0.866362, -0.4979]], 'translation vector': [4.52972, 2.291977, 1.52688]}\nD: {'rotation matrix': [[0.207746, 0.495681, -0.843292], [0.977345, -0.069508, 0.199914], [0.040478, -0.865719, -0.498891]], 'translation vector': [4.528935, 2.293617, 1.525752]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.08541, 0.640528, -0.76317], [0.996306, -0.061741, 0.059682], [-0.008891, -0.765449, -0.643436]], 'translation vector': [3.003591, 1.574332, 1.432793]}\nB: {'rotation matrix': [[0.08501, 0.641279, -0.762584], [0.996355, -0.060148, 0.06049], [-0.007077, -0.764946, -0.644055]], 'translation vector': [3.00634, 1.575815, 1.433934]}\nC: {'rotation matrix': [[0.9999991614836521, 0.0005695811207308883, -0.0015075373347832835], [-0.0005748316229898535, 0.9999942080654551, -0.0033643004544446934], [0.0015050239127193494, 0.003364654292322913, 0.9999927444380246]], 'translation vector': [-0.0005823890138008103, 0.0017300160779236684, -0.0007769099832195536]}\nD: {'rotation matrix': [[0.085438, 0.641091, -0.762694], [0.996316, -0.060644, 0.060635], [-0.00738, -0.765065, -0.64391]], 'translation vector': [3.005707, 1.574798, 1.4333]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_103_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_103_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_103_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_103_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.08541, 0.640528, -0.76317], [0.996306, -0.061741, 0.059682], [-0.008891, -0.765449, -0.643436]], 'translation vector': [3.003591, 1.574332, 1.432793]}\nB: {'rotation matrix': [[0.08501, 0.641279, -0.762584], [0.996355, -0.060148, 0.06049], [-0.007077, -0.764946, -0.644055]], 'translation vector': [3.00634, 1.575815, 1.433934]}\nC: {'rotation matrix': [[0.9999991614836521, 0.0005695811207308883, -0.0015075373347832835], [-0.0005748316229898535, 0.9999942080654551, -0.0033643004544446934], [0.0015050239127193494, 0.003364654292322913, 0.9999927444380246]], 'translation vector': [-0.0005823890138008103, 0.0017300160779236684, -0.0007769099832195536]}\nD: {'rotation matrix': [[0.085438, 0.641091, -0.762694], [0.996316, -0.060644, 0.060635], [-0.00738, -0.765065, -0.64391]], 'translation vector': [3.005707, 1.574798, 1.4333]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999836186953653, 0.00108451635874016, -0.00565701955892193], [-0.0010583782720287828, 0.9999885698645751, 0.004627019650109258], [0.005661574895801672, -0.004620937029738945, 0.9999737330988163]], 'translation vector': [0.0022502124816869973, 0.004079635447382657, -0.0017077678174191036]}\nB: {'rotation matrix': [[0.764916, -0.419696, 0.48863], [-0.623144, -0.290098, 0.726316], [-0.163081, -0.860057, -0.483431]], 'translation vector': [2.190224, 2.255941, 1.286466]}\nC: {'rotation matrix': [[0.764173, -0.416772, 0.492281], [-0.624792, -0.28869, 0.725461], [-0.160235, -0.861951, -0.481005]], 'translation vector': [2.189569, 2.253508, 1.282023]}\nD: {'rotation matrix': [[0.763152, -0.417481, 0.493263], [-0.626561, -0.291187, 0.722933], [-0.158179, -0.860767, -0.483797]], 'translation vector': [2.190887, 2.252149, 1.282769]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_104_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_104_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_104_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_104_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999836186953653, 0.00108451635874016, -0.00565701955892193], [-0.0010583782720287828, 0.9999885698645751, 0.004627019650109258], [0.005661574895801672, -0.004620937029738945, 0.9999737330988163]], 'translation vector': [0.0022502124816869973, 0.004079635447382657, -0.0017077678174191036]}\nB: {'rotation matrix': [[0.764916, -0.419696, 0.48863], [-0.623144, -0.290098, 0.726316], [-0.163081, -0.860057, -0.483431]], 'translation vector': [2.190224, 2.255941, 1.286466]}\nC: {'rotation matrix': [[0.764173, -0.416772, 0.492281], [-0.624792, -0.28869, 0.725461], [-0.160235, -0.861951, -0.481005]], 'translation vector': [2.189569, 2.253508, 1.282023]}\nD: {'rotation matrix': [[0.763152, -0.417481, 0.493263], [-0.626561, -0.291187, 0.722933], [-0.158179, -0.860767, -0.483797]], 'translation vector': [2.190887, 2.252149, 1.282769]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999685089712825, -0.00485492735446149, 0.006197125791337409], [0.004892366141195013, 0.9999701266660436, -0.005981156791582798], [-0.006168136508286792, 0.006011265809070663, 0.9999622632239159]], 'translation vector': [0.00022924877864394233, 0.00019097290261571587, -0.001928325440709866]}\nB: {'rotation matrix': [[-0.968997, 0.179836, -0.169422], [0.236776, 0.48002, -0.8447], [-0.070582, -0.858627, -0.507719]], 'translation vector': [3.781446, 2.333063, 1.459816]}\nC: {'rotation matrix': [[-0.967651, 0.180929, -0.175829], [0.242263, 0.471818, -0.84776], [-0.070424, -0.862933, -0.500388]], 'translation vector': [3.780886, 2.334988, 1.460004]}\nD: {'rotation matrix': [[-0.968244, 0.180308, -0.173186], [0.239986, 0.476144, -0.845987], [-0.070076, -0.860684, -0.504294]], 'translation vector': [3.781386, 2.333968, 1.460791]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_105_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_105_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_105_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_105_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999685089712825, -0.00485492735446149, 0.006197125791337409], [0.004892366141195013, 0.9999701266660436, -0.005981156791582798], [-0.006168136508286792, 0.006011265809070663, 0.9999622632239159]], 'translation vector': [0.00022924877864394233, 0.00019097290261571587, -0.001928325440709866]}\nB: {'rotation matrix': [[-0.968997, 0.179836, -0.169422], [0.236776, 0.48002, -0.8447], [-0.070582, -0.858627, -0.507719]], 'translation vector': [3.781446, 2.333063, 1.459816]}\nC: {'rotation matrix': [[-0.967651, 0.180929, -0.175829], [0.242263, 0.471818, -0.84776], [-0.070424, -0.862933, -0.500388]], 'translation vector': [3.780886, 2.334988, 1.460004]}\nD: {'rotation matrix': [[-0.968244, 0.180308, -0.173186], [0.239986, 0.476144, -0.845987], [-0.070076, -0.860684, -0.504294]], 'translation vector': [3.781386, 2.333968, 1.460791]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.769476, 0.035457, -0.637691], [0.638618, -0.056212, 0.767468], [-0.008634, -0.997789, -0.065897]], 'translation vector': [3.059908, 3.99174, 1.48793]}\nB: {'rotation matrix': [[0.768334, 0.034359, -0.639126], [0.639975, -0.056434, 0.766321], [-0.009738, -0.997815, -0.065349]], 'translation vector': [3.063556, 3.993645, 1.487647]}\nC: {'rotation matrix': [[0.76637, 0.032495, -0.641577], [0.642284, -0.057724, 0.764291], [-0.012198, -0.997804, -0.065109]], 'translation vector': [3.065239, 3.993527, 1.488269]}\nD: {'rotation matrix': [[0.9999994770672102, 0.0010059593388553243, -0.0005629779278299809], [-0.0010051492699491647, 0.9999992963767129, 0.00013663416634814593], [0.0005621045000587302, -0.0001358922037830382, 1.0000001627120574]], 'translation vector': [-0.004005273497495132, -0.008267648490985158, -0.0009698463604679297]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_106_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_106_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_106_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_106_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.769476, 0.035457, -0.637691], [0.638618, -0.056212, 0.767468], [-0.008634, -0.997789, -0.065897]], 'translation vector': [3.059908, 3.99174, 1.48793]}\nB: {'rotation matrix': [[0.768334, 0.034359, -0.639126], [0.639975, -0.056434, 0.766321], [-0.009738, -0.997815, -0.065349]], 'translation vector': [3.063556, 3.993645, 1.487647]}\nC: {'rotation matrix': [[0.76637, 0.032495, -0.641577], [0.642284, -0.057724, 0.764291], [-0.012198, -0.997804, -0.065109]], 'translation vector': [3.065239, 3.993527, 1.488269]}\nD: {'rotation matrix': [[0.9999994770672102, 0.0010059593388553243, -0.0005629779278299809], [-0.0010051492699491647, 0.9999992963767129, 0.00013663416634814593], [0.0005621045000587302, -0.0001358922037830382, 1.0000001627120574]], 'translation vector': [-0.004005273497495132, -0.008267648490985158, -0.0009698463604679297]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999965024273574, 0.0018995589746770465, -0.002128604659346281], [-0.0018975946490258904, 0.9999971783528935, 0.0010235202394668671], [0.0021305967266473038, -0.00101920750556101, 0.9999966975642395]], 'translation vector': [-0.001221359216795559, -0.0013000622008119134, -0.00023198476015379166]}\nB: {'rotation matrix': [[-0.247804, -0.452831, 0.856468], [-0.967446, 0.162565, -0.193963], [-0.051399, -0.876651, -0.478373]], 'translation vector': [1.577581, 1.960365, 1.31447]}\nC: {'rotation matrix': [[-0.241822, -0.452397, 0.858405], [-0.968762, 0.162689, -0.18717], [-0.054978, -0.876852, -0.477607]], 'translation vector': [1.575634, 1.958436, 1.314538]}\nD: {'rotation matrix': [[-0.251836, -0.455153, 0.854058], [-0.966529, 0.162962, -0.198153], [-0.048989, -0.875374, -0.480959]], 'translation vector': [1.577733, 1.957285, 1.314553]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_107_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_107_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_107_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_107_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999965024273574, 0.0018995589746770465, -0.002128604659346281], [-0.0018975946490258904, 0.9999971783528935, 0.0010235202394668671], [0.0021305967266473038, -0.00101920750556101, 0.9999966975642395]], 'translation vector': [-0.001221359216795559, -0.0013000622008119134, -0.00023198476015379166]}\nB: {'rotation matrix': [[-0.247804, -0.452831, 0.856468], [-0.967446, 0.162565, -0.193963], [-0.051399, -0.876651, -0.478373]], 'translation vector': [1.577581, 1.960365, 1.31447]}\nC: {'rotation matrix': [[-0.241822, -0.452397, 0.858405], [-0.968762, 0.162689, -0.18717], [-0.054978, -0.876852, -0.477607]], 'translation vector': [1.575634, 1.958436, 1.314538]}\nD: {'rotation matrix': [[-0.251836, -0.455153, 0.854058], [-0.966529, 0.162962, -0.198153], [-0.048989, -0.875374, -0.480959]], 'translation vector': [1.577733, 1.957285, 1.314553]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.502725, -0.506922, 0.700212], [-0.864292, 0.279467, -0.418207], [0.016312, -0.815431, -0.578624]], 'translation vector': [4.022235, 5.007849, 1.281956]}\nB: {'rotation matrix': [[0.9994658881112836, -0.01787238768104933, 0.027367099286839433], [0.01746068119707397, 0.9997321901556896, 0.015206345300919908], [-0.027631559291770684, -0.01472107419323857, 0.9995094632380824]], 'translation vector': [-0.030548360022831567, -0.0024606871848007472, 0.004630350985881493]}\nC: {'rotation matrix': [[-0.511887, -0.50554, 0.694551], [-0.858867, 0.284356, -0.426016], [0.017868, -0.814599, -0.579749]], 'translation vector': [4.034731, 5.018784, 1.285057]}\nD: {'rotation matrix': [[-0.524185, -0.50567, 0.685221], [-0.851455, 0.296094, -0.432844], [0.015986, -0.810325, -0.585763]], 'translation vector': [4.046806, 5.029983, 1.286514]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_108_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_108_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_108_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_108_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.502725, -0.506922, 0.700212], [-0.864292, 0.279467, -0.418207], [0.016312, -0.815431, -0.578624]], 'translation vector': [4.022235, 5.007849, 1.281956]}\nB: {'rotation matrix': [[0.9994658881112836, -0.01787238768104933, 0.027367099286839433], [0.01746068119707397, 0.9997321901556896, 0.015206345300919908], [-0.027631559291770684, -0.01472107419323857, 0.9995094632380824]], 'translation vector': [-0.030548360022831567, -0.0024606871848007472, 0.004630350985881493]}\nC: {'rotation matrix': [[-0.511887, -0.50554, 0.694551], [-0.858867, 0.284356, -0.426016], [0.017868, -0.814599, -0.579749]], 'translation vector': [4.034731, 5.018784, 1.285057]}\nD: {'rotation matrix': [[-0.524185, -0.50567, 0.685221], [-0.851455, 0.296094, -0.432844], [0.015986, -0.810325, -0.585763]], 'translation vector': [4.046806, 5.029983, 1.286514]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9997511181043846, 0.002627583195842048, -0.02210613011174363], [-0.002639480814720638, 0.9999968737080126, -0.0005594700790184048], [0.022104273186971824, 0.0006179932127646122, 0.9997554616613505]], 'translation vector': [0.008057060510321179, -0.003086615617105104, 0.008815946351156123]}\nB: {'rotation matrix': [[-0.793492, -0.269336, 0.545737], [-0.608499, 0.36581, -0.70421], [-0.009967, -0.890865, -0.454158]], 'translation vector': [3.342808, 3.719108, 1.377405]}\nC: {'rotation matrix': [[-0.799682, -0.271069, 0.535752], [-0.600405, 0.367946, -0.710021], [-0.004663, -0.889459, -0.456991]], 'translation vector': [3.342098, 3.723592, 1.379504]}\nD: {'rotation matrix': [[-0.786909, -0.267427, 0.556108], [-0.616914, 0.361106, -0.699299], [-0.013802, -0.893356, -0.449137]], 'translation vector': [3.343614, 3.714152, 1.377028]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_109_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_109_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_109_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_109_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9997511181043846, 0.002627583195842048, -0.02210613011174363], [-0.002639480814720638, 0.9999968737080126, -0.0005594700790184048], [0.022104273186971824, 0.0006179932127646122, 0.9997554616613505]], 'translation vector': [0.008057060510321179, -0.003086615617105104, 0.008815946351156123]}\nB: {'rotation matrix': [[-0.793492, -0.269336, 0.545737], [-0.608499, 0.36581, -0.70421], [-0.009967, -0.890865, -0.454158]], 'translation vector': [3.342808, 3.719108, 1.377405]}\nC: {'rotation matrix': [[-0.799682, -0.271069, 0.535752], [-0.600405, 0.367946, -0.710021], [-0.004663, -0.889459, -0.456991]], 'translation vector': [3.342098, 3.723592, 1.379504]}\nD: {'rotation matrix': [[-0.786909, -0.267427, 0.556108], [-0.616914, 0.361106, -0.699299], [-0.013802, -0.893356, -0.449137]], 'translation vector': [3.343614, 3.714152, 1.377028]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.470058, 0.310455, -0.826234], [0.882195, -0.135697, 0.450908], [0.027869, -0.940853, -0.337668]], 'translation vector': [2.719146, 3.165557, 1.444111]}\nB: {'rotation matrix': [[0.468253, 0.310351, -0.827298], [0.883071, -0.132138, 0.45025], [0.030418, -0.941394, -0.335936]], 'translation vector': [2.721684, 3.167619, 1.442076]}\nC: {'rotation matrix': [[0.9999808269927472, 0.005970992787416191, 0.00174631158613173], [-0.00597527498498062, 0.9999782630360619, 0.002650362855816552], [-0.0017306339108793102, -0.002661153861931357, 0.9999943916418343]], 'translation vector': [0.00038154468875628567, 0.0036815080791540167, 0.0005855298747257098]}\nD: {'rotation matrix': [[0.468431, 0.309409, -0.82755], [0.883026, -0.133283, 0.45], [0.028935, -0.941542, -0.33565]], 'translation vector': [2.722082, 3.167839, 1.441818]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_110_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_110_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_110_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_110_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.470058, 0.310455, -0.826234], [0.882195, -0.135697, 0.450908], [0.027869, -0.940853, -0.337668]], 'translation vector': [2.719146, 3.165557, 1.444111]}\nB: {'rotation matrix': [[0.468253, 0.310351, -0.827298], [0.883071, -0.132138, 0.45025], [0.030418, -0.941394, -0.335936]], 'translation vector': [2.721684, 3.167619, 1.442076]}\nC: {'rotation matrix': [[0.9999808269927472, 0.005970992787416191, 0.00174631158613173], [-0.00597527498498062, 0.9999782630360619, 0.002650362855816552], [-0.0017306339108793102, -0.002661153861931357, 0.9999943916418343]], 'translation vector': [0.00038154468875628567, 0.0036815080791540167, 0.0005855298747257098]}\nD: {'rotation matrix': [[0.468431, 0.309409, -0.82755], [0.883026, -0.133283, 0.45], [0.028935, -0.941542, -0.33565]], 'translation vector': [2.722082, 3.167839, 1.441818]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.886617, -0.372394, 0.274287], [-0.453667, -0.584855, 0.672407], [-0.089983, -0.720602, -0.687485]], 'translation vector': [2.862491, 2.429976, 1.648643]}\nB: {'rotation matrix': [[0.9999609940536269, 0.004336106194237876, -0.007665012330121596], [-0.0043994792450735756, 0.9999564170173506, -0.008231049758408536], [0.007628227917166668, 0.008264723677336768, 0.9999364519647637]], 'translation vector': [0.014388650201931252, -0.01870904449863442, 0.020428177278094317]}\nC: {'rotation matrix': [[0.881825, -0.378531, 0.281247], [-0.462696, -0.579299, 0.671062], [-0.091091, -0.721891, -0.685985]], 'translation vector': [2.843046, 2.410197, 1.648909]}\nD: {'rotation matrix': [[0.88465, -0.37678, 0.274647], [-0.457061, -0.584385, 0.670514], [-0.092137, -0.718701, -0.689188]], 'translation vector': [2.852998, 2.419565, 1.649377]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_111_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_111_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_111_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_111_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.886617, -0.372394, 0.274287], [-0.453667, -0.584855, 0.672407], [-0.089983, -0.720602, -0.687485]], 'translation vector': [2.862491, 2.429976, 1.648643]}\nB: {'rotation matrix': [[0.9999609940536269, 0.004336106194237876, -0.007665012330121596], [-0.0043994792450735756, 0.9999564170173506, -0.008231049758408536], [0.007628227917166668, 0.008264723677336768, 0.9999364519647637]], 'translation vector': [0.014388650201931252, -0.01870904449863442, 0.020428177278094317]}\nC: {'rotation matrix': [[0.881825, -0.378531, 0.281247], [-0.462696, -0.579299, 0.671062], [-0.091091, -0.721891, -0.685985]], 'translation vector': [2.843046, 2.410197, 1.648909]}\nD: {'rotation matrix': [[0.88465, -0.37678, 0.274647], [-0.457061, -0.584385, 0.670514], [-0.092137, -0.718701, -0.689188]], 'translation vector': [2.852998, 2.419565, 1.649377]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.937349, 0.183503, -0.296148], [0.348203, 0.521431, -0.779015], [0.011469, -0.833329, -0.552659]], 'translation vector': [1.516432, 1.509609, 1.382559]}\nB: {'rotation matrix': [[0.9999980782363276, 0.001503213640840046, -0.0005306957238598916], [-0.001506954921949503, 0.9999783283684359, -0.006294441787430057], [0.0005221180642734458, 0.006294562264384682, 0.9999799919937622]], 'translation vector': [-0.0001973645495118026, -0.004429123982855679, 0.002277103824624316]}\nC: {'rotation matrix': [[-0.936868, 0.179689, -0.299985], [0.349254, 0.523335, -0.777266], [0.017327, -0.832966, -0.553053]], 'translation vector': [1.516465, 1.50589, 1.383504]}\nD: {'rotation matrix': [[-0.936977, 0.182065, -0.298205], [0.349103, 0.5225, -0.777896], [0.014184, -0.832974, -0.55313]], 'translation vector': [1.516084, 1.508243, 1.382535]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_112_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_112_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_112_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_112_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.937349, 0.183503, -0.296148], [0.348203, 0.521431, -0.779015], [0.011469, -0.833329, -0.552659]], 'translation vector': [1.516432, 1.509609, 1.382559]}\nB: {'rotation matrix': [[0.9999980782363276, 0.001503213640840046, -0.0005306957238598916], [-0.001506954921949503, 0.9999783283684359, -0.006294441787430057], [0.0005221180642734458, 0.006294562264384682, 0.9999799919937622]], 'translation vector': [-0.0001973645495118026, -0.004429123982855679, 0.002277103824624316]}\nC: {'rotation matrix': [[-0.936868, 0.179689, -0.299985], [0.349254, 0.523335, -0.777266], [0.017327, -0.832966, -0.553053]], 'translation vector': [1.516465, 1.50589, 1.383504]}\nD: {'rotation matrix': [[-0.936977, 0.182065, -0.298205], [0.349103, 0.5225, -0.777896], [0.014184, -0.832974, -0.55313]], 'translation vector': [1.516084, 1.508243, 1.382535]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.857254, 0.207542, -0.471213], [0.514274, 0.300265, -0.803345], [-0.025239, -0.931003, -0.364137]], 'translation vector': [3.165454, 3.656282, 1.333704]}\nB: {'rotation matrix': [[0.99999640966509, -0.0010007660526934368, -0.0025990335284116336], [0.0009919500135136654, 0.9999945413184362, -0.003311856723933156], [0.0026023838793041037, 0.0033091782066769597, 0.999990415293157]], 'translation vector': [0.003805164660490079, -0.0038731744338753593, -0.0029462366598167478]}\nC: {'rotation matrix': [[-0.857583, 0.210228, -0.469422], [0.513576, 0.300052, -0.803871], [-0.028145, -0.930469, -0.365287]], 'translation vector': [3.164042, 3.653142, 1.337743]}\nD: {'rotation matrix': [[-0.856761, 0.210709, -0.470704], [0.514795, 0.294981, -0.804967], [-0.030765, -0.931981, -0.3612]], 'translation vector': [3.165054, 3.650114, 1.341357]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_113_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_113_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_113_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_113_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.857254, 0.207542, -0.471213], [0.514274, 0.300265, -0.803345], [-0.025239, -0.931003, -0.364137]], 'translation vector': [3.165454, 3.656282, 1.333704]}\nB: {'rotation matrix': [[0.99999640966509, -0.0010007660526934368, -0.0025990335284116336], [0.0009919500135136654, 0.9999945413184362, -0.003311856723933156], [0.0026023838793041037, 0.0033091782066769597, 0.999990415293157]], 'translation vector': [0.003805164660490079, -0.0038731744338753593, -0.0029462366598167478]}\nC: {'rotation matrix': [[-0.857583, 0.210228, -0.469422], [0.513576, 0.300052, -0.803871], [-0.028145, -0.930469, -0.365287]], 'translation vector': [3.164042, 3.653142, 1.337743]}\nD: {'rotation matrix': [[-0.856761, 0.210709, -0.470704], [0.514795, 0.294981, -0.804967], [-0.030765, -0.931981, -0.3612]], 'translation vector': [3.165054, 3.650114, 1.341357]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.496753, -0.455066, 0.739021], [-0.867832, 0.250391, -0.429153], [0.010249, -0.854529, -0.519303]], 'translation vector': [1.585927, 4.408765, 1.329075]}\nB: {'rotation matrix': [[-0.500222, -0.451271, 0.739008], [-0.865841, 0.250951, -0.432832], [0.009869, -0.856375, -0.51626]], 'translation vector': [1.58204, 4.414393, 1.331803]}\nC: {'rotation matrix': [[0.9999646386789894, 0.004658434745366248, 0.0070851516010133645], [-0.004681817359291345, 0.999983580482257, 0.0033767996583214267], [-0.007069472768969729, -0.0034085438936624457, 0.9999685018088521]], 'translation vector': [-1.9089315443032717e-05, 0.003691149021725071, -0.009076217757424399]}\nD: {'rotation matrix': [[-0.49386, -0.45517, 0.740893], [-0.869462, 0.246928, -0.427859], [0.011802, -0.855481, -0.5177]], 'translation vector': [1.591466, 4.4048, 1.328646]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_114_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_114_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_114_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_114_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.496753, -0.455066, 0.739021], [-0.867832, 0.250391, -0.429153], [0.010249, -0.854529, -0.519303]], 'translation vector': [1.585927, 4.408765, 1.329075]}\nB: {'rotation matrix': [[-0.500222, -0.451271, 0.739008], [-0.865841, 0.250951, -0.432832], [0.009869, -0.856375, -0.51626]], 'translation vector': [1.58204, 4.414393, 1.331803]}\nC: {'rotation matrix': [[0.9999646386789894, 0.004658434745366248, 0.0070851516010133645], [-0.004681817359291345, 0.999983580482257, 0.0033767996583214267], [-0.007069472768969729, -0.0034085438936624457, 0.9999685018088521]], 'translation vector': [-1.9089315443032717e-05, 0.003691149021725071, -0.009076217757424399]}\nD: {'rotation matrix': [[-0.49386, -0.45517, 0.740893], [-0.869462, 0.246928, -0.427859], [0.011802, -0.855481, -0.5177]], 'translation vector': [1.591466, 4.4048, 1.328646]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.216454, 0.211748, -0.953053], [0.970078, -0.156619, 0.185523], [-0.109982, -0.964693, -0.239313]], 'translation vector': [4.876326, 2.835873, 1.673403]}\nB: {'rotation matrix': [[0.9999815116666099, 0.003828941338965109, -0.004550071168900241], [-0.0038043597787837257, 0.999978256845473, 0.005451903660299082], [0.004571630830927769, -0.005433064547306739, 0.9999746221671975]], 'translation vector': [-0.0021931397990639923, 0.004370231111501255, 0.000887941045025542]}\nC: {'rotation matrix': [[0.223921, 0.203392, -0.953148], [0.967778, -0.16198, 0.192793], [-0.115179, -0.965606, -0.233109]], 'translation vector': [4.877863, 2.835087, 1.676992]}\nD: {'rotation matrix': [[0.219557, 0.208101, -0.953147], [0.969026, -0.159743, 0.188338], [-0.113066, -0.964975, -0.236728]], 'translation vector': [4.875911, 2.83788, 1.674953]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_115_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_115_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_115_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_115_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.216454, 0.211748, -0.953053], [0.970078, -0.156619, 0.185523], [-0.109982, -0.964693, -0.239313]], 'translation vector': [4.876326, 2.835873, 1.673403]}\nB: {'rotation matrix': [[0.9999815116666099, 0.003828941338965109, -0.004550071168900241], [-0.0038043597787837257, 0.999978256845473, 0.005451903660299082], [0.004571630830927769, -0.005433064547306739, 0.9999746221671975]], 'translation vector': [-0.0021931397990639923, 0.004370231111501255, 0.000887941045025542]}\nC: {'rotation matrix': [[0.223921, 0.203392, -0.953148], [0.967778, -0.16198, 0.192793], [-0.115179, -0.965606, -0.233109]], 'translation vector': [4.877863, 2.835087, 1.676992]}\nD: {'rotation matrix': [[0.219557, 0.208101, -0.953147], [0.969026, -0.159743, 0.188338], [-0.113066, -0.964975, -0.236728]], 'translation vector': [4.875911, 2.83788, 1.674953]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999402380460055, 0.006292872204581464, 0.009000301608311233], [-0.006374858865950918, 0.9999377706786996, 0.009079354369281548], [-0.008942504139812743, -0.009135104885608418, 0.9999183041835895]], 'translation vector': [3.939302955568991e-05, -0.002970151993936021, 0.008448821166219922]}\nB: {'rotation matrix': [[-0.997375, -0.070877, -0.01485], [-0.014261, 0.393282, -0.919307], [0.070998, -0.916682, -0.393261]], 'translation vector': [7.372805, 2.63008, 1.348598]}\nC: {'rotation matrix': [[-0.997269, -0.072413, -0.014556], [-0.015372, 0.396244, -0.918017], [0.072244, -0.915286, -0.396275]], 'translation vector': [7.36901, 2.625689, 1.34671]}\nD: {'rotation matrix': [[-0.997198, -0.073599, -0.01342], [-0.016859, 0.39584, -0.918165], [0.072888, -0.915366, -0.395972]], 'translation vector': [7.365971, 2.622898, 1.345074]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_116_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_116_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_116_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_116_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999402380460055, 0.006292872204581464, 0.009000301608311233], [-0.006374858865950918, 0.9999377706786996, 0.009079354369281548], [-0.008942504139812743, -0.009135104885608418, 0.9999183041835895]], 'translation vector': [3.939302955568991e-05, -0.002970151993936021, 0.008448821166219922]}\nB: {'rotation matrix': [[-0.997375, -0.070877, -0.01485], [-0.014261, 0.393282, -0.919307], [0.070998, -0.916682, -0.393261]], 'translation vector': [7.372805, 2.63008, 1.348598]}\nC: {'rotation matrix': [[-0.997269, -0.072413, -0.014556], [-0.015372, 0.396244, -0.918017], [0.072244, -0.915286, -0.396275]], 'translation vector': [7.36901, 2.625689, 1.34671]}\nD: {'rotation matrix': [[-0.997198, -0.073599, -0.01342], [-0.016859, 0.39584, -0.918165], [0.072888, -0.915366, -0.395972]], 'translation vector': [7.365971, 2.622898, 1.345074]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.051446, 0.205786, -0.977244], [0.99734, -0.040014, -0.06093], [-0.051642, -0.977778, -0.20318]], 'translation vector': [3.492872, 2.502008, 1.69891]}\nB: {'rotation matrix': [[-0.043348, 0.1967, -0.979505], [0.997776, -0.041176, -0.052425], [-0.050644, -0.979599, -0.194477]], 'translation vector': [3.495688, 2.502278, 1.699202]}\nC: {'rotation matrix': [[-0.045349, 0.201463, -0.978446], [0.997609, -0.041995, -0.054884], [-0.052147, -0.978595, -0.199077]], 'translation vector': [3.49477, 2.503383, 1.707673]}\nD: {'rotation matrix': [[0.999952584360321, 0.006117610645767056, 0.007552374046773563], [-0.006133995866929935, 0.9999788341295721, 0.0021300038842573614], [-0.007539309696335425, -0.0021763143472021637, 0.9999686644670424]], 'translation vector': [-0.005541149058866601, -0.004329021249491083, -0.004737026405577716]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_117_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_117_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_117_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_117_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.051446, 0.205786, -0.977244], [0.99734, -0.040014, -0.06093], [-0.051642, -0.977778, -0.20318]], 'translation vector': [3.492872, 2.502008, 1.69891]}\nB: {'rotation matrix': [[-0.043348, 0.1967, -0.979505], [0.997776, -0.041176, -0.052425], [-0.050644, -0.979599, -0.194477]], 'translation vector': [3.495688, 2.502278, 1.699202]}\nC: {'rotation matrix': [[-0.045349, 0.201463, -0.978446], [0.997609, -0.041995, -0.054884], [-0.052147, -0.978595, -0.199077]], 'translation vector': [3.49477, 2.503383, 1.707673]}\nD: {'rotation matrix': [[0.999952584360321, 0.006117610645767056, 0.007552374046773563], [-0.006133995866929935, 0.9999788341295721, 0.0021300038842573614], [-0.007539309696335425, -0.0021763143472021637, 0.9999686644670424]], 'translation vector': [-0.005541149058866601, -0.004329021249491083, -0.004737026405577716]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.92969, -0.177823, 0.322577], [-0.368073, 0.414955, -0.832066], [0.014105, -0.892296, -0.451231]], 'translation vector': [2.094699, 1.923867, 1.362793]}\nB: {'rotation matrix': [[-0.929496, -0.179835, 0.322021], [-0.368436, 0.412208, -0.833271], [0.017112, -0.893165, -0.449403]], 'translation vector': [2.092189, 1.927801, 1.363214]}\nC: {'rotation matrix': [[0.9999967402226891, -0.00025435497097484245, -0.0026972206948773017], [0.0002554991423686922, 0.9999998064927808, 0.00039832318899401273], [0.0026965738295479497, -0.00039944612541857925, 0.9999956863286328]], 'translation vector': [0.0007808272698826002, -3.308771117738196e-05, 0.0032529965763865576]}\nD: {'rotation matrix': [[-0.929672, -0.179046, 0.321952], [-0.368044, 0.413573, -0.832767], [0.015953, -0.892693, -0.450384]], 'translation vector': [2.09373, 1.925922, 1.362599]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_118_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_118_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_118_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_118_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.92969, -0.177823, 0.322577], [-0.368073, 0.414955, -0.832066], [0.014105, -0.892296, -0.451231]], 'translation vector': [2.094699, 1.923867, 1.362793]}\nB: {'rotation matrix': [[-0.929496, -0.179835, 0.322021], [-0.368436, 0.412208, -0.833271], [0.017112, -0.893165, -0.449403]], 'translation vector': [2.092189, 1.927801, 1.363214]}\nC: {'rotation matrix': [[0.9999967402226891, -0.00025435497097484245, -0.0026972206948773017], [0.0002554991423686922, 0.9999998064927808, 0.00039832318899401273], [0.0026965738295479497, -0.00039944612541857925, 0.9999956863286328]], 'translation vector': [0.0007808272698826002, -3.308771117738196e-05, 0.0032529965763865576]}\nD: {'rotation matrix': [[-0.929672, -0.179046, 0.321952], [-0.368044, 0.413573, -0.832767], [0.015953, -0.892693, -0.450384]], 'translation vector': [2.09373, 1.925922, 1.362599]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.462347, -0.272387, 0.843825], [-0.885267, -0.195868, 0.421827], [0.050379, -0.942041, -0.331694]], 'translation vector': [2.976725, 2.047585, 1.44742]}\nB: {'rotation matrix': [[0.9999659967455217, 0.000730682433916761, -0.008205012696381919], [-0.0006795411337798277, 0.9999808235064653, 0.006171696278259806], [0.008208518663254718, -0.006164897409722228, 0.9999474019525653]], 'translation vector': [0.003919003542433852, -0.0016103743394202397, 0.004248482748549165]}\nC: {'rotation matrix': [[0.463845, -0.2716, 0.843257], [-0.884483, -0.196093, 0.423364], [0.050371, -0.942221, -0.331182]], 'translation vector': [2.976598, 2.048301, 1.445946]}\nD: {'rotation matrix': [[0.465329, -0.271694, 0.842408], [-0.883772, -0.195467, 0.425135], [0.049156, -0.942324, -0.331072]], 'translation vector': [2.978186, 2.04869, 1.446578]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_119_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_119_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_119_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_119_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.462347, -0.272387, 0.843825], [-0.885267, -0.195868, 0.421827], [0.050379, -0.942041, -0.331694]], 'translation vector': [2.976725, 2.047585, 1.44742]}\nB: {'rotation matrix': [[0.9999659967455217, 0.000730682433916761, -0.008205012696381919], [-0.0006795411337798277, 0.9999808235064653, 0.006171696278259806], [0.008208518663254718, -0.006164897409722228, 0.9999474019525653]], 'translation vector': [0.003919003542433852, -0.0016103743394202397, 0.004248482748549165]}\nC: {'rotation matrix': [[0.463845, -0.2716, 0.843257], [-0.884483, -0.196093, 0.423364], [0.050371, -0.942221, -0.331182]], 'translation vector': [2.976598, 2.048301, 1.445946]}\nD: {'rotation matrix': [[0.465329, -0.271694, 0.842408], [-0.883772, -0.195467, 0.425135], [0.049156, -0.942324, -0.331072]], 'translation vector': [2.978186, 2.04869, 1.446578]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.819777, 0.236537, -0.521552], [0.57264, -0.327401, 0.751593], [0.007023, -0.9148, -0.403846]], 'translation vector': [2.353185, 1.22719, 1.374303]}\nB: {'rotation matrix': [[0.999987888286007, -0.002291943522874061, -0.004318624647106336], [0.002305286110298337, 0.9999924577563948, 0.0032570259245620907], [0.0043115657326277725, -0.0032678213093349246, 0.9999859589268988]], 'translation vector': [0.0029862992996512183, 0.0027957678410703846, 0.00028412393673649117]}\nC: {'rotation matrix': [[0.818568, 0.239176, -0.522246], [0.574347, -0.327388, 0.750296], [0.008476, -0.914118, -0.405359]], 'translation vector': [2.353795, 1.227513, 1.374115]}\nD: {'rotation matrix': [[0.821096, 0.234783, -0.520267], [0.570754, -0.327501, 0.752983], [0.006399, -0.915216, -0.402913]], 'translation vector': [2.353373, 1.227232, 1.3746]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_120_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_120_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_120_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_120_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.819777, 0.236537, -0.521552], [0.57264, -0.327401, 0.751593], [0.007023, -0.9148, -0.403846]], 'translation vector': [2.353185, 1.22719, 1.374303]}\nB: {'rotation matrix': [[0.999987888286007, -0.002291943522874061, -0.004318624647106336], [0.002305286110298337, 0.9999924577563948, 0.0032570259245620907], [0.0043115657326277725, -0.0032678213093349246, 0.9999859589268988]], 'translation vector': [0.0029862992996512183, 0.0027957678410703846, 0.00028412393673649117]}\nC: {'rotation matrix': [[0.818568, 0.239176, -0.522246], [0.574347, -0.327388, 0.750296], [0.008476, -0.914118, -0.405359]], 'translation vector': [2.353795, 1.227513, 1.374115]}\nD: {'rotation matrix': [[0.821096, 0.234783, -0.520267], [0.570754, -0.327501, 0.752983], [0.006399, -0.915216, -0.402913]], 'translation vector': [2.353373, 1.227232, 1.3746]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.416416, 0.454823, -0.787232], [0.907976, 0.163592, -0.38577], [-0.046672, -0.875428, -0.481091]], 'translation vector': [2.42158, 4.677908, 1.279661]}\nB: {'rotation matrix': [[-0.425105, 0.447282, -0.786908], [0.904054, 0.167175, -0.393368], [-0.044395, -0.878631, -0.475434]], 'translation vector': [2.418032, 4.676476, 1.278379]}\nC: {'rotation matrix': [[-0.405457, 0.458134, -0.791023], [0.912898, 0.15832, -0.376234], [-0.047131, -0.87467, -0.482422]], 'translation vector': [2.427205, 4.676823, 1.279665]}\nD: {'rotation matrix': [[0.999724588457419, 0.011399918490649034, -0.02049681987065299], [-0.011547405749944445, 0.9999079075389897, -0.007141934731129424], [0.020413044185954875, 0.00737693223574732, 0.9997642455565067]], 'translation vector': [0.0017021170863906754, -0.0007418791262142621, 0.002807958654513776]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_121_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_121_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_121_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_121_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.416416, 0.454823, -0.787232], [0.907976, 0.163592, -0.38577], [-0.046672, -0.875428, -0.481091]], 'translation vector': [2.42158, 4.677908, 1.279661]}\nB: {'rotation matrix': [[-0.425105, 0.447282, -0.786908], [0.904054, 0.167175, -0.393368], [-0.044395, -0.878631, -0.475434]], 'translation vector': [2.418032, 4.676476, 1.278379]}\nC: {'rotation matrix': [[-0.405457, 0.458134, -0.791023], [0.912898, 0.15832, -0.376234], [-0.047131, -0.87467, -0.482422]], 'translation vector': [2.427205, 4.676823, 1.279665]}\nD: {'rotation matrix': [[0.999724588457419, 0.011399918490649034, -0.02049681987065299], [-0.011547405749944445, 0.9999079075389897, -0.007141934731129424], [0.020413044185954875, 0.00737693223574732, 0.9997642455565067]], 'translation vector': [0.0017021170863906754, -0.0007418791262142621, 0.002807958654513776]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999991921613425, -0.0009534576315807749, 0.0007322231548443674], [0.0009539109746141209, 0.9999998849157189, 0.0006356436900991667], [-0.0007323849744554394, -0.0006353709242038023, 0.9999990842009482]], 'translation vector': [-0.0019568440092229133, 0.0040178639573226205, -0.0007055440032766036]}\nB: {'rotation matrix': [[-0.677088, 0.408379, -0.612192], [0.735888, 0.380882, -0.559819], [0.004555, -0.829551, -0.558412]], 'translation vector': [3.089066, 2.044868, 1.438859]}\nC: {'rotation matrix': [[-0.677557, 0.408197, -0.611794], [0.735465, 0.379263, -0.561472], [0.002839, -0.830382, -0.557187]], 'translation vector': [3.090277, 2.045193, 1.438377]}\nD: {'rotation matrix': [[-0.677242, 0.408267, -0.612096], [0.73575, 0.380087, -0.56054], [0.003799, -0.829971, -0.557794]], 'translation vector': [3.089461, 2.045596, 1.437863]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_122_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_122_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_122_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_122_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999991921613425, -0.0009534576315807749, 0.0007322231548443674], [0.0009539109746141209, 0.9999998849157189, 0.0006356436900991667], [-0.0007323849744554394, -0.0006353709242038023, 0.9999990842009482]], 'translation vector': [-0.0019568440092229133, 0.0040178639573226205, -0.0007055440032766036]}\nB: {'rotation matrix': [[-0.677088, 0.408379, -0.612192], [0.735888, 0.380882, -0.559819], [0.004555, -0.829551, -0.558412]], 'translation vector': [3.089066, 2.044868, 1.438859]}\nC: {'rotation matrix': [[-0.677557, 0.408197, -0.611794], [0.735465, 0.379263, -0.561472], [0.002839, -0.830382, -0.557187]], 'translation vector': [3.090277, 2.045193, 1.438377]}\nD: {'rotation matrix': [[-0.677242, 0.408267, -0.612096], [0.73575, 0.380087, -0.56054], [0.003799, -0.829971, -0.557794]], 'translation vector': [3.089461, 2.045596, 1.437863]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999933784388596, -0.0034228660793026973, 0.0010334015378228609], [0.003443417069472685, 0.9997723135838638, -0.0210485719107263], [-0.0009619583367667721, 0.021052364637286505, 0.9997776329131831]], 'translation vector': [0.004991626612900646, -0.0024493216023662445, -0.003027628610638544]}\nB: {'rotation matrix': [[-0.068724, 0.196407, -0.978111], [0.997631, 0.016511, -0.06678], [0.003034, -0.980384, -0.197076]], 'translation vector': [6.624384, 2.565858, 1.44421]}\nC: {'rotation matrix': [[-0.062271, 0.18592, -0.98059], [0.998056, 0.014281, -0.060673], [0.002724, -0.982461, -0.186448]], 'translation vector': [6.625182, 2.564143, 1.442555]}\nD: {'rotation matrix': [[-0.067121, 0.19262, -0.978975], [0.997737, 0.016917, -0.065078], [0.004026, -0.981128, -0.19332]], 'translation vector': [6.625297, 2.569471, 1.443187]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_123_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_123_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_123_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_123_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999933784388596, -0.0034228660793026973, 0.0010334015378228609], [0.003443417069472685, 0.9997723135838638, -0.0210485719107263], [-0.0009619583367667721, 0.021052364637286505, 0.9997776329131831]], 'translation vector': [0.004991626612900646, -0.0024493216023662445, -0.003027628610638544]}\nB: {'rotation matrix': [[-0.068724, 0.196407, -0.978111], [0.997631, 0.016511, -0.06678], [0.003034, -0.980384, -0.197076]], 'translation vector': [6.624384, 2.565858, 1.44421]}\nC: {'rotation matrix': [[-0.062271, 0.18592, -0.98059], [0.998056, 0.014281, -0.060673], [0.002724, -0.982461, -0.186448]], 'translation vector': [6.625182, 2.564143, 1.442555]}\nD: {'rotation matrix': [[-0.067121, 0.19262, -0.978975], [0.997737, 0.016917, -0.065078], [0.004026, -0.981128, -0.19332]], 'translation vector': [6.625297, 2.569471, 1.443187]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9997697997860496, 0.001019787082464491, -0.021429481643640898], [-0.0009730181353209979, 0.9999969701208679, 0.002158925702055335], [0.021432088446976687, -0.002138201439636503, 0.9997675908556951]], 'translation vector': [0.004448630857523561, 0.0024420113720010628, -0.001058932632110654]}\nB: {'rotation matrix': [[-0.999487, 0.010341, 0.030333], [-0.019706, 0.548122, -0.836166], [-0.025273, -0.836334, -0.547637]], 'translation vector': [4.843515, 3.430529, 1.401708]}\nC: {'rotation matrix': [[-0.998846, 0.024735, 0.04116], [-0.020973, 0.546345, -0.837298], [-0.043199, -0.837195, -0.545196]], 'translation vector': [4.840129, 3.432139, 1.401112]}\nD: {'rotation matrix': [[-0.99921, 0.020117, 0.034274], [-0.017632, 0.548494, -0.835969], [-0.035616, -0.835913, -0.547706]], 'translation vector': [4.841137, 3.430736, 1.401886]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_124_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_124_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_124_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_124_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9997697997860496, 0.001019787082464491, -0.021429481643640898], [-0.0009730181353209979, 0.9999969701208679, 0.002158925702055335], [0.021432088446976687, -0.002138201439636503, 0.9997675908556951]], 'translation vector': [0.004448630857523561, 0.0024420113720010628, -0.001058932632110654]}\nB: {'rotation matrix': [[-0.999487, 0.010341, 0.030333], [-0.019706, 0.548122, -0.836166], [-0.025273, -0.836334, -0.547637]], 'translation vector': [4.843515, 3.430529, 1.401708]}\nC: {'rotation matrix': [[-0.998846, 0.024735, 0.04116], [-0.020973, 0.546345, -0.837298], [-0.043199, -0.837195, -0.545196]], 'translation vector': [4.840129, 3.432139, 1.401112]}\nD: {'rotation matrix': [[-0.99921, 0.020117, 0.034274], [-0.017632, 0.548494, -0.835969], [-0.035616, -0.835913, -0.547706]], 'translation vector': [4.841137, 3.430736, 1.401886]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.973747, -0.109977, 0.199301], [-0.227471, -0.502961, 0.833839], [0.008537, -0.857284, -0.514773]], 'translation vector': [3.554081, 1.206281, 1.35243]}\nB: {'rotation matrix': [[0.974665, -0.108996, 0.195317], [-0.223559, -0.502331, 0.835276], [0.007072, -0.857778, -0.513971]], 'translation vector': [3.555352, 1.206811, 1.353912]}\nC: {'rotation matrix': [[0.975504, -0.107044, 0.192183], [-0.219886, -0.500464, 0.837368], [0.006546, -0.859114, -0.511742]], 'translation vector': [3.5544, 1.207723, 1.355687]}\nD: {'rotation matrix': [[0.9999906036647181, 0.002816837265478036, -0.0034361334791159484], [-0.0028229898836968203, 0.9999947431038866, -0.0015384023641953812], [0.0034319714995403, 0.0015489580591809052, 0.9999926274107623]], 'translation vector': [0.0002503237708082473, -0.0002760600759463827, -0.00019478740093437086]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_125_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_125_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_125_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_125_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.973747, -0.109977, 0.199301], [-0.227471, -0.502961, 0.833839], [0.008537, -0.857284, -0.514773]], 'translation vector': [3.554081, 1.206281, 1.35243]}\nB: {'rotation matrix': [[0.974665, -0.108996, 0.195317], [-0.223559, -0.502331, 0.835276], [0.007072, -0.857778, -0.513971]], 'translation vector': [3.555352, 1.206811, 1.353912]}\nC: {'rotation matrix': [[0.975504, -0.107044, 0.192183], [-0.219886, -0.500464, 0.837368], [0.006546, -0.859114, -0.511742]], 'translation vector': [3.5544, 1.207723, 1.355687]}\nD: {'rotation matrix': [[0.9999906036647181, 0.002816837265478036, -0.0034361334791159484], [-0.0028229898836968203, 0.9999947431038866, -0.0015384023641953812], [0.0034319714995403, 0.0015489580591809052, 0.9999926274107623]], 'translation vector': [0.0002503237708082473, -0.0002760600759463827, -0.00019478740093437086]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.153679, 0.256881, -0.954146], [0.987333, 0.001369, -0.158656], [-0.03945, -0.966442, -0.253837]], 'translation vector': [1.842026, 1.203469, 1.473211]}\nB: {'rotation matrix': [[-0.151778, 0.257722, -0.954224], [0.987593, 0.000186, -0.157036], [-0.040294, -0.966219, -0.254553]], 'translation vector': [1.842306, 1.202322, 1.472604]}\nC: {'rotation matrix': [[-0.149914, 0.257434, -0.954596], [0.987791, -0.002361, -0.155764], [-0.042353, -0.966293, -0.253937]], 'translation vector': [1.843622, 1.201203, 1.472192]}\nD: {'rotation matrix': [[0.9999992738268638, -0.00012046241721948631, -0.0012199092118460354], [0.0001219402230910216, 0.9999989013124573, 0.0017389291337165482], [0.0012191992898708535, -0.0017382297355628953, 0.9999985296461054]], 'translation vector': [9.159323589502666e-05, -0.0060291788848427785, 8.443913047839757e-05]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_126_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_126_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_126_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_126_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.153679, 0.256881, -0.954146], [0.987333, 0.001369, -0.158656], [-0.03945, -0.966442, -0.253837]], 'translation vector': [1.842026, 1.203469, 1.473211]}\nB: {'rotation matrix': [[-0.151778, 0.257722, -0.954224], [0.987593, 0.000186, -0.157036], [-0.040294, -0.966219, -0.254553]], 'translation vector': [1.842306, 1.202322, 1.472604]}\nC: {'rotation matrix': [[-0.149914, 0.257434, -0.954596], [0.987791, -0.002361, -0.155764], [-0.042353, -0.966293, -0.253937]], 'translation vector': [1.843622, 1.201203, 1.472192]}\nD: {'rotation matrix': [[0.9999992738268638, -0.00012046241721948631, -0.0012199092118460354], [0.0001219402230910216, 0.9999989013124573, 0.0017389291337165482], [0.0012191992898708535, -0.0017382297355628953, 0.9999985296461054]], 'translation vector': [9.159323589502666e-05, -0.0060291788848427785, 8.443913047839757e-05]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.14824, 0.422945, -0.893948], [0.983241, -0.033972, -0.17912], [-0.106127, -0.905518, -0.410821]], 'translation vector': [4.004252, 0.906944, 2.572337]}\nB: {'rotation matrix': [[-0.144176, 0.428291, -0.892065], [0.983875, -0.034383, -0.175522], [-0.105847, -0.902987, -0.416427]], 'translation vector': [4.001886, 0.906293, 2.57387]}\nC: {'rotation matrix': [[0.9999802090304492, -0.0006608909445885984, -0.006377640026736867], [0.0006622709420172097, 0.9999989071934312, 0.0002893250642296396], [0.006377955510407445, -0.00029429125031086645, 0.9999796120375704]], 'translation vector': [0.0015109144602648002, -0.0040381270140653625, -0.0009682382039999382]}\nD: {'rotation matrix': [[-0.139849, 0.432923, -0.890517], [0.98469, -0.03371, -0.171026], [-0.10406, -0.9008, -0.42158]], 'translation vector': [3.996022, 0.9047, 2.579904]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_127_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_127_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_127_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_127_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.14824, 0.422945, -0.893948], [0.983241, -0.033972, -0.17912], [-0.106127, -0.905518, -0.410821]], 'translation vector': [4.004252, 0.906944, 2.572337]}\nB: {'rotation matrix': [[-0.144176, 0.428291, -0.892065], [0.983875, -0.034383, -0.175522], [-0.105847, -0.902987, -0.416427]], 'translation vector': [4.001886, 0.906293, 2.57387]}\nC: {'rotation matrix': [[0.9999802090304492, -0.0006608909445885984, -0.006377640026736867], [0.0006622709420172097, 0.9999989071934312, 0.0002893250642296396], [0.006377955510407445, -0.00029429125031086645, 0.9999796120375704]], 'translation vector': [0.0015109144602648002, -0.0040381270140653625, -0.0009682382039999382]}\nD: {'rotation matrix': [[-0.139849, 0.432923, -0.890517], [0.98469, -0.03371, -0.171026], [-0.10406, -0.9008, -0.42158]], 'translation vector': [3.996022, 0.9047, 2.579904]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.95128, 0.171677, -0.256112], [0.307849, -0.482535, 0.819993], [0.017191, -0.858887, -0.511877]], 'translation vector': [2.918653, 3.427386, 1.515216]}\nB: {'rotation matrix': [[0.9999949680507799, -0.0030198797325234252, -0.001049278425799119], [0.0030204031206264347, 0.9999953358677364, 0.000916435423205514], [0.0010474297666525848, -0.0009198822713830659, 0.9999990387486941]], 'translation vector': [-0.00019299961249297226, -0.0019013116010877518, -0.0012501965874700538]}\nC: {'rotation matrix': [[0.951329, 0.168071, -0.258311], [0.307858, -0.480204, 0.821357], [0.014004, -0.860905, -0.508574]], 'translation vector': [2.920244, 3.426191, 1.515625]}\nD: {'rotation matrix': [[0.951137, 0.174865, -0.254481], [0.308106, -0.483514, 0.81932], [0.020225, -0.857693, -0.513765]], 'translation vector': [2.916759, 3.427486, 1.515303]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_128_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_128_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_128_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_128_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.95128, 0.171677, -0.256112], [0.307849, -0.482535, 0.819993], [0.017191, -0.858887, -0.511877]], 'translation vector': [2.918653, 3.427386, 1.515216]}\nB: {'rotation matrix': [[0.9999949680507799, -0.0030198797325234252, -0.001049278425799119], [0.0030204031206264347, 0.9999953358677364, 0.000916435423205514], [0.0010474297666525848, -0.0009198822713830659, 0.9999990387486941]], 'translation vector': [-0.00019299961249297226, -0.0019013116010877518, -0.0012501965874700538]}\nC: {'rotation matrix': [[0.951329, 0.168071, -0.258311], [0.307858, -0.480204, 0.821357], [0.014004, -0.860905, -0.508574]], 'translation vector': [2.920244, 3.426191, 1.515625]}\nD: {'rotation matrix': [[0.951137, 0.174865, -0.254481], [0.308106, -0.483514, 0.81932], [0.020225, -0.857693, -0.513765]], 'translation vector': [2.916759, 3.427486, 1.515303]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999948635923562, 0.0019820469436732306, 0.002154140600797888], [-0.0019903388630383317, 0.9999924231566063, 0.003542917026556551], [-0.0021468846137928286, -0.0035469945961230523, 0.999992296254944]], 'translation vector': [-5.7007563989186494e-05, -0.0006793783086549987, -0.00012474555531971632]}\nB: {'rotation matrix': [[-0.933451, -0.165748, 0.318116], [-0.358704, 0.434072, -0.826385], [-0.001114, -0.885499, -0.464639]], 'translation vector': [1.119556, 2.234202, 1.400117]}\nC: {'rotation matrix': [[-0.933995, -0.170592, 0.31393], [-0.357261, 0.435306, -0.826362], [0.004315, -0.883973, -0.467519]], 'translation vector': [1.117768, 2.23249, 1.399859]}\nD: {'rotation matrix': [[-0.93341, -0.169242, 0.31639], [-0.358807, 0.435851, -0.825404], [0.001794, -0.883963, -0.467553]], 'translation vector': [1.117643, 2.232584, 1.400741]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_129_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_129_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_129_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_129_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999948635923562, 0.0019820469436732306, 0.002154140600797888], [-0.0019903388630383317, 0.9999924231566063, 0.003542917026556551], [-0.0021468846137928286, -0.0035469945961230523, 0.999992296254944]], 'translation vector': [-5.7007563989186494e-05, -0.0006793783086549987, -0.00012474555531971632]}\nB: {'rotation matrix': [[-0.933451, -0.165748, 0.318116], [-0.358704, 0.434072, -0.826385], [-0.001114, -0.885499, -0.464639]], 'translation vector': [1.119556, 2.234202, 1.400117]}\nC: {'rotation matrix': [[-0.933995, -0.170592, 0.31393], [-0.357261, 0.435306, -0.826362], [0.004315, -0.883973, -0.467519]], 'translation vector': [1.117768, 2.23249, 1.399859]}\nD: {'rotation matrix': [[-0.93341, -0.169242, 0.31639], [-0.358807, 0.435851, -0.825404], [0.001794, -0.883963, -0.467553]], 'translation vector': [1.117643, 2.232584, 1.400741]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.748267, 0.274864, -0.603777], [0.662514, -0.356598, 0.658721], [-0.034247, -0.892909, -0.448932]], 'translation vector': [2.689408, 2.67138, 1.313352]}\nB: {'rotation matrix': [[0.746223, 0.273332, -0.606993], [0.664679, -0.356291, 0.656703], [-0.036768, -0.893502, -0.447551]], 'translation vector': [2.678885, 2.679979, 1.310144]}\nC: {'rotation matrix': [[0.9999399674236885, 0.0008968793534765223, 0.01089808273108475], [-0.000989300454985631, 0.9999632239345498, 0.008537240164508266], [-0.010889507071785043, -0.008547033142130317, 0.9999033050990314]], 'translation vector': [-0.01197293045142045, -0.02229876967815736, 0.03026515941132618]}\nD: {'rotation matrix': [[0.750155, 0.270973, -0.603193], [0.660278, -0.356699, 0.660908], [-0.03607, -0.894058, -0.446497]], 'translation vector': [2.698287, 2.659688, 1.315667]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_130_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_130_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_130_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_130_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.748267, 0.274864, -0.603777], [0.662514, -0.356598, 0.658721], [-0.034247, -0.892909, -0.448932]], 'translation vector': [2.689408, 2.67138, 1.313352]}\nB: {'rotation matrix': [[0.746223, 0.273332, -0.606993], [0.664679, -0.356291, 0.656703], [-0.036768, -0.893502, -0.447551]], 'translation vector': [2.678885, 2.679979, 1.310144]}\nC: {'rotation matrix': [[0.9999399674236885, 0.0008968793534765223, 0.01089808273108475], [-0.000989300454985631, 0.9999632239345498, 0.008537240164508266], [-0.010889507071785043, -0.008547033142130317, 0.9999033050990314]], 'translation vector': [-0.01197293045142045, -0.02229876967815736, 0.03026515941132618]}\nD: {'rotation matrix': [[0.750155, 0.270973, -0.603193], [0.660278, -0.356699, 0.660908], [-0.03607, -0.894058, -0.446497]], 'translation vector': [2.698287, 2.659688, 1.315667]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.696591, -0.332063, 0.636], [-0.715704, 0.259464, -0.648419], [0.050297, -0.90687, -0.418398]], 'translation vector': [0.055261, 3.785911, 1.510756]}\nB: {'rotation matrix': [[0.9999892202506441, -0.00432806217171397, -0.0014665396078038379], [0.0043283836537151305, 0.9999910348386083, -5.023853182741767e-05], [0.0014665058361795998, 4.44487020677531e-05, 0.9999983390207243]], 'translation vector': [0.0011882249140264811, -0.004541217231683825, 0.003677767622445316]}\nC: {'rotation matrix': [[-0.698852, -0.328674, 0.635279], [-0.713642, 0.26058, -0.65024], [0.048176, -0.907784, -0.416662]], 'translation vector': [0.047395, 3.788746, 1.502043]}\nD: {'rotation matrix': [[-0.698666, -0.330448, 0.634563], [-0.713793, 0.261647, -0.649647], [0.048643, -0.906832, -0.418676]], 'translation vector': [0.050863, 3.788018, 1.507423]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_131_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_131_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_131_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_131_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.696591, -0.332063, 0.636], [-0.715704, 0.259464, -0.648419], [0.050297, -0.90687, -0.418398]], 'translation vector': [0.055261, 3.785911, 1.510756]}\nB: {'rotation matrix': [[0.9999892202506441, -0.00432806217171397, -0.0014665396078038379], [0.0043283836537151305, 0.9999910348386083, -5.023853182741767e-05], [0.0014665058361795998, 4.44487020677531e-05, 0.9999983390207243]], 'translation vector': [0.0011882249140264811, -0.004541217231683825, 0.003677767622445316]}\nC: {'rotation matrix': [[-0.698852, -0.328674, 0.635279], [-0.713642, 0.26058, -0.65024], [0.048176, -0.907784, -0.416662]], 'translation vector': [0.047395, 3.788746, 1.502043]}\nD: {'rotation matrix': [[-0.698666, -0.330448, 0.634563], [-0.713793, 0.261647, -0.649647], [0.048643, -0.906832, -0.418676]], 'translation vector': [0.050863, 3.788018, 1.507423]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999994384686345, -0.0006788559243182462, -0.0008560007213187077], [0.0006780059132462143, 0.9999998393114027, -0.000711634963528042], [0.0008572035387077444, 0.0007099766954480035, 0.9999996039685791]], 'translation vector': [0.0021169101928586176, 0.0016932348180918044, -0.0014691902955634717]}\nB: {'rotation matrix': [[-0.895004, 0.171136, -0.411923], [0.445772, 0.376296, -0.812213], [0.016006, -0.910557, -0.413074]], 'translation vector': [2.821576, 5.408109, 1.547241]}\nC: {'rotation matrix': [[-0.895238, 0.170954, -0.411491], [0.44529, 0.377097, -0.812105], [0.016339, -0.910259, -0.413716]], 'translation vector': [2.819563, 5.407667, 1.547957]}\nD: {'rotation matrix': [[-0.895239, 0.171618, -0.411211], [0.445324, 0.376235, -0.812486], [0.015275, -0.910491, -0.413246]], 'translation vector': [2.820169, 5.40833, 1.547624]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_132_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_132_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_132_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_132_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999994384686345, -0.0006788559243182462, -0.0008560007213187077], [0.0006780059132462143, 0.9999998393114027, -0.000711634963528042], [0.0008572035387077444, 0.0007099766954480035, 0.9999996039685791]], 'translation vector': [0.0021169101928586176, 0.0016932348180918044, -0.0014691902955634717]}\nB: {'rotation matrix': [[-0.895004, 0.171136, -0.411923], [0.445772, 0.376296, -0.812213], [0.016006, -0.910557, -0.413074]], 'translation vector': [2.821576, 5.408109, 1.547241]}\nC: {'rotation matrix': [[-0.895238, 0.170954, -0.411491], [0.44529, 0.377097, -0.812105], [0.016339, -0.910259, -0.413716]], 'translation vector': [2.819563, 5.407667, 1.547957]}\nD: {'rotation matrix': [[-0.895239, 0.171618, -0.411211], [0.445324, 0.376235, -0.812486], [0.015275, -0.910491, -0.413246]], 'translation vector': [2.820169, 5.40833, 1.547624]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.637806, -0.366719, 0.67729], [-0.770059, -0.286951, 0.569797], [-0.014606, -0.884972, -0.465415]], 'translation vector': [2.635432, 2.237918, 1.453759]}\nB: {'rotation matrix': [[0.639756, -0.365353, 0.676188], [-0.768417, -0.286037, 0.572466], [-0.015738, -0.885833, -0.463738]], 'translation vector': [2.635672, 2.238828, 1.45525]}\nC: {'rotation matrix': [[0.999992450941996, 0.003938168732013472, 0.0002619981628988685], [-0.003940060772482587, 0.9999806955317443, 0.004722690751934828], [-0.00024268998302535557, -0.004722987499164323, 0.9999892225354342]], 'translation vector': [-0.005978537327156946, -0.0007775878287423765, 0.0022633181070532693]}\nD: {'rotation matrix': [[0.636585, -0.368058, 0.677712], [-0.771071, -0.287285, 0.568258], [-0.014455, -0.884308, -0.46668]], 'translation vector': [2.636608, 2.236841, 1.454577]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_133_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_133_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_133_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_133_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.637806, -0.366719, 0.67729], [-0.770059, -0.286951, 0.569797], [-0.014606, -0.884972, -0.465415]], 'translation vector': [2.635432, 2.237918, 1.453759]}\nB: {'rotation matrix': [[0.639756, -0.365353, 0.676188], [-0.768417, -0.286037, 0.572466], [-0.015738, -0.885833, -0.463738]], 'translation vector': [2.635672, 2.238828, 1.45525]}\nC: {'rotation matrix': [[0.999992450941996, 0.003938168732013472, 0.0002619981628988685], [-0.003940060772482587, 0.9999806955317443, 0.004722690751934828], [-0.00024268998302535557, -0.004722987499164323, 0.9999892225354342]], 'translation vector': [-0.005978537327156946, -0.0007775878287423765, 0.0022633181070532693]}\nD: {'rotation matrix': [[0.636585, -0.368058, 0.677712], [-0.771071, -0.287285, 0.568258], [-0.014455, -0.884308, -0.46668]], 'translation vector': [2.636608, 2.236841, 1.454577]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.354317, -0.208867, 0.911501], [-0.934632, 0.110757, -0.337929], [-0.030372, -0.971652, -0.234457]], 'translation vector': [0.531753, 4.839624, 1.62588]}\nB: {'rotation matrix': [[-0.359065, -0.216471, 0.907862], [-0.932968, 0.109695, -0.342839], [-0.025373, -0.970107, -0.241348]], 'translation vector': [0.533016, 4.840936, 1.625213]}\nC: {'rotation matrix': [[0.9999996059479784, -0.0012011060402040321, 0.0006047856645127561], [0.0011984060508814654, 0.9999921678086844, 0.003627178785230534], [-0.000607755557205814, -0.0036253860701085153, 0.999994001644102]], 'translation vector': [-0.0020457167014393818, -0.01042060812880563, 0.003252619468668172]}\nD: {'rotation matrix': [[-0.356177, -0.213479, 0.909706], [-0.934025, 0.10958, -0.339984], [-0.027106, -0.970783, -0.238424]], 'translation vector': [0.532497, 4.839391, 1.625248]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_134_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_134_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_134_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_134_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.354317, -0.208867, 0.911501], [-0.934632, 0.110757, -0.337929], [-0.030372, -0.971652, -0.234457]], 'translation vector': [0.531753, 4.839624, 1.62588]}\nB: {'rotation matrix': [[-0.359065, -0.216471, 0.907862], [-0.932968, 0.109695, -0.342839], [-0.025373, -0.970107, -0.241348]], 'translation vector': [0.533016, 4.840936, 1.625213]}\nC: {'rotation matrix': [[0.9999996059479784, -0.0012011060402040321, 0.0006047856645127561], [0.0011984060508814654, 0.9999921678086844, 0.003627178785230534], [-0.000607755557205814, -0.0036253860701085153, 0.999994001644102]], 'translation vector': [-0.0020457167014393818, -0.01042060812880563, 0.003252619468668172]}\nD: {'rotation matrix': [[-0.356177, -0.213479, 0.909706], [-0.934025, 0.10958, -0.339984], [-0.027106, -0.970783, -0.238424]], 'translation vector': [0.532497, 4.839391, 1.625248]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999831834333464, -3.507485354961902e-05, -0.005860591935981322], [1.2894070442508321e-05, 0.999992942835117, -0.003855834283817326], [0.00585975547828403, 0.003855906716345711, 0.9999751812718962]], 'translation vector': [-9.176265998767086e-05, -0.003526493044568424, -0.0013563859537040202]}\nB: {'rotation matrix': [[-0.77208, 0.081888, -0.630228], [0.634233, 0.036058, -0.772301], [-0.040517, -0.995989, -0.079776]], 'translation vector': [4.355151, 2.275217, 1.510745]}\nC: {'rotation matrix': [[-0.769009, 0.085964, -0.633432], [0.638035, 0.042436, -0.768838], [-0.039212, -0.995394, -0.087482]], 'translation vector': [4.353152, 2.272772, 1.50454]}\nD: {'rotation matrix': [[-0.770144, 0.083681, -0.632358], [0.636632, 0.03909, -0.770176], [-0.03973, -0.995726, -0.083379]], 'translation vector': [4.354443, 2.273597, 1.508503]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_135_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_135_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_135_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_135_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999831834333464, -3.507485354961902e-05, -0.005860591935981322], [1.2894070442508321e-05, 0.999992942835117, -0.003855834283817326], [0.00585975547828403, 0.003855906716345711, 0.9999751812718962]], 'translation vector': [-9.176265998767086e-05, -0.003526493044568424, -0.0013563859537040202]}\nB: {'rotation matrix': [[-0.77208, 0.081888, -0.630228], [0.634233, 0.036058, -0.772301], [-0.040517, -0.995989, -0.079776]], 'translation vector': [4.355151, 2.275217, 1.510745]}\nC: {'rotation matrix': [[-0.769009, 0.085964, -0.633432], [0.638035, 0.042436, -0.768838], [-0.039212, -0.995394, -0.087482]], 'translation vector': [4.353152, 2.272772, 1.50454]}\nD: {'rotation matrix': [[-0.770144, 0.083681, -0.632358], [0.636632, 0.03909, -0.770176], [-0.03973, -0.995726, -0.083379]], 'translation vector': [4.354443, 2.273597, 1.508503]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.872251, 0.269436, -0.408146], [0.489057, 0.477878, -0.729696], [-0.001562, -0.836084, -0.548599]], 'translation vector': [2.680995, 3.11951, 1.281605]}\nB: {'rotation matrix': [[0.9999175427731898, 0.009289450845399635, 0.008882740398963147], [-0.00915148449802066, 0.9998381508008006, -0.015456879997285015], [-0.00902528905222651, 0.015374430101541683, 0.9998409413313208]], 'translation vector': [-0.02453370105938546, 0.014905487027389919, -0.03059606364374634]}\nC: {'rotation matrix': [[-0.872521, 0.262383, -0.412143], [0.488544, 0.478168, -0.729849], [0.005573, -0.838159, -0.545398]], 'translation vector': [2.690634, 3.125973, 1.284562]}\nD: {'rotation matrix': [[-0.871338, 0.255355, -0.419003], [0.490471, 0.478377, -0.728419], [0.014436, -0.840208, -0.542072]], 'translation vector': [2.702949, 3.129856, 1.287257]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_136_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_136_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_136_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_136_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.872251, 0.269436, -0.408146], [0.489057, 0.477878, -0.729696], [-0.001562, -0.836084, -0.548599]], 'translation vector': [2.680995, 3.11951, 1.281605]}\nB: {'rotation matrix': [[0.9999175427731898, 0.009289450845399635, 0.008882740398963147], [-0.00915148449802066, 0.9998381508008006, -0.015456879997285015], [-0.00902528905222651, 0.015374430101541683, 0.9998409413313208]], 'translation vector': [-0.02453370105938546, 0.014905487027389919, -0.03059606364374634]}\nC: {'rotation matrix': [[-0.872521, 0.262383, -0.412143], [0.488544, 0.478168, -0.729849], [0.005573, -0.838159, -0.545398]], 'translation vector': [2.690634, 3.125973, 1.284562]}\nD: {'rotation matrix': [[-0.871338, 0.255355, -0.419003], [0.490471, 0.478377, -0.728419], [0.014436, -0.840208, -0.542072]], 'translation vector': [2.702949, 3.129856, 1.287257]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.333056, -0.473197, 0.815573], [-0.9429, 0.170506, -0.286124], [-0.003667, -0.864299, -0.502965]], 'translation vector': [2.099262, 2.343947, 1.49878]}\nB: {'rotation matrix': [[-0.341507, -0.468371, 0.814864], [-0.939879, 0.169312, -0.296582], [0.000944, -0.867158, -0.498033]], 'translation vector': [2.09227, 2.339374, 1.500507]}\nC: {'rotation matrix': [[-0.349241, -0.464358, 0.813882], [-0.937028, 0.170072, -0.305049], [0.003234, -0.869165, -0.494512]], 'translation vector': [2.088692, 2.33782, 1.505356]}\nD: {'rotation matrix': [[0.9999635418289009, -0.005130634438046651, -0.00688590414124517], [0.00513692191174813, 0.9999857138339602, 0.0010725741392655886], [0.00688015226508057, -0.0011072559806139443, 0.9999748317531684]], 'translation vector': [-0.026001101753266642, -0.0071394396285136, 0.008639096069164354]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_137_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_137_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_137_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_137_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.333056, -0.473197, 0.815573], [-0.9429, 0.170506, -0.286124], [-0.003667, -0.864299, -0.502965]], 'translation vector': [2.099262, 2.343947, 1.49878]}\nB: {'rotation matrix': [[-0.341507, -0.468371, 0.814864], [-0.939879, 0.169312, -0.296582], [0.000944, -0.867158, -0.498033]], 'translation vector': [2.09227, 2.339374, 1.500507]}\nC: {'rotation matrix': [[-0.349241, -0.464358, 0.813882], [-0.937028, 0.170072, -0.305049], [0.003234, -0.869165, -0.494512]], 'translation vector': [2.088692, 2.33782, 1.505356]}\nD: {'rotation matrix': [[0.9999635418289009, -0.005130634438046651, -0.00688590414124517], [0.00513692191174813, 0.9999857138339602, 0.0010725741392655886], [0.00688015226508057, -0.0011072559806139443, 0.9999748317531684]], 'translation vector': [-0.026001101753266642, -0.0071394396285136, 0.008639096069164354]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.030402, 0.425954, -0.904234], [0.998503, -0.028211, -0.04686], [-0.045469, -0.904305, -0.424459]], 'translation vector': [2.422483, 1.358004, 3.279846]}\nB: {'rotation matrix': [[-0.030422, 0.425378, -0.904504], [0.99853, -0.027681, -0.046602], [-0.044861, -0.904592, -0.42391]], 'translation vector': [2.423117, 1.357937, 3.279462]}\nC: {'rotation matrix': [[0.9999413192700902, -0.00031349790801336034, -0.010858677567769605], [0.00020550611921538246, 0.9999497154111819, -0.010006476843209223], [0.010861290904906616, 0.010003092803207396, 0.9998904023572843]], 'translation vector': [-0.0025713849698387747, -0.003845445277962156, -0.00016886354172340745]}\nD: {'rotation matrix': [[-0.029484, 0.425058, -0.904686], [0.998566, -0.027942, -0.045671], [-0.044692, -0.904735, -0.423624]], 'translation vector': [2.421348, 1.3572, 3.28135]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_138_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_138_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_138_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_138_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.030402, 0.425954, -0.904234], [0.998503, -0.028211, -0.04686], [-0.045469, -0.904305, -0.424459]], 'translation vector': [2.422483, 1.358004, 3.279846]}\nB: {'rotation matrix': [[-0.030422, 0.425378, -0.904504], [0.99853, -0.027681, -0.046602], [-0.044861, -0.904592, -0.42391]], 'translation vector': [2.423117, 1.357937, 3.279462]}\nC: {'rotation matrix': [[0.9999413192700902, -0.00031349790801336034, -0.010858677567769605], [0.00020550611921538246, 0.9999497154111819, -0.010006476843209223], [0.010861290904906616, 0.010003092803207396, 0.9998904023572843]], 'translation vector': [-0.0025713849698387747, -0.003845445277962156, -0.00016886354172340745]}\nD: {'rotation matrix': [[-0.029484, 0.425058, -0.904686], [0.998566, -0.027942, -0.045671], [-0.044692, -0.904735, -0.423624]], 'translation vector': [2.421348, 1.3572, 3.28135]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.765303, 0.126374, -0.631143], [0.606826, -0.468638, 0.641982], [-0.214648, -0.874305, -0.435336]], 'translation vector': [4.259322, 3.776065, 1.503445]}\nB: {'rotation matrix': [[0.771053, 0.12608, -0.624165], [0.599963, -0.472273, 0.645757], [-0.213359, -0.872388, -0.439791]], 'translation vector': [4.254354, 3.773882, 1.500145]}\nC: {'rotation matrix': [[0.9999284250414853, 0.0013159481143052354, -0.011853419692681406], [-0.0013716672277964584, 0.9999877220001062, -0.004620807232633075], [0.011847871043956123, 0.004636177506016426, 0.9999191533457081]], 'translation vector': [-0.0029675207616195465, 0.002877998549804417, -0.005058945419356364]}\nD: {'rotation matrix': [[0.774333, 0.127442, -0.619813], [0.595393, -0.478434, 0.645452], [-0.214281, -0.868826, -0.446345]], 'translation vector': [4.253978, 3.779827, 1.501383]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_139_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_139_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_139_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_139_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.765303, 0.126374, -0.631143], [0.606826, -0.468638, 0.641982], [-0.214648, -0.874305, -0.435336]], 'translation vector': [4.259322, 3.776065, 1.503445]}\nB: {'rotation matrix': [[0.771053, 0.12608, -0.624165], [0.599963, -0.472273, 0.645757], [-0.213359, -0.872388, -0.439791]], 'translation vector': [4.254354, 3.773882, 1.500145]}\nC: {'rotation matrix': [[0.9999284250414853, 0.0013159481143052354, -0.011853419692681406], [-0.0013716672277964584, 0.9999877220001062, -0.004620807232633075], [0.011847871043956123, 0.004636177506016426, 0.9999191533457081]], 'translation vector': [-0.0029675207616195465, 0.002877998549804417, -0.005058945419356364]}\nD: {'rotation matrix': [[0.774333, 0.127442, -0.619813], [0.595393, -0.478434, 0.645452], [-0.214281, -0.868826, -0.446345]], 'translation vector': [4.253978, 3.779827, 1.501383]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.90788, 0.151333, -0.390964], [0.408575, 0.110464, -0.906016], [-0.093922, -0.982291, -0.162118]], 'translation vector': [8.818443, 3.831761, 1.477683]}\nB: {'rotation matrix': [[0.9999985311997388, 0.0014822343891824376, 0.0004132906130215321], [-0.0014816934035833144, 0.9999987577699637, 9.7059989439923e-05], [-0.00041331535706021807, -9.78109674475786e-05, 1.0000005199156523]], 'translation vector': [-0.004405482725633902, -0.00022188509424603264, 0.00016042860388854052]}\nC: {'rotation matrix': [[-0.90752, 0.150251, -0.392214], [0.409699, 0.111045, -0.905437], [-0.092489, -0.982392, -0.162333]], 'translation vector': [8.816371, 3.832904, 1.475888]}\nD: {'rotation matrix': [[-0.907271, 0.148469, -0.393466], [0.410673, 0.111241, -0.904972], [-0.090591, -0.982641, -0.161898]], 'translation vector': [8.814532, 3.834109, 1.474353]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_140_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_140_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_140_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_140_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.90788, 0.151333, -0.390964], [0.408575, 0.110464, -0.906016], [-0.093922, -0.982291, -0.162118]], 'translation vector': [8.818443, 3.831761, 1.477683]}\nB: {'rotation matrix': [[0.9999985311997388, 0.0014822343891824376, 0.0004132906130215321], [-0.0014816934035833144, 0.9999987577699637, 9.7059989439923e-05], [-0.00041331535706021807, -9.78109674475786e-05, 1.0000005199156523]], 'translation vector': [-0.004405482725633902, -0.00022188509424603264, 0.00016042860388854052]}\nC: {'rotation matrix': [[-0.90752, 0.150251, -0.392214], [0.409699, 0.111045, -0.905437], [-0.092489, -0.982392, -0.162333]], 'translation vector': [8.816371, 3.832904, 1.475888]}\nD: {'rotation matrix': [[-0.907271, 0.148469, -0.393466], [0.410673, 0.111241, -0.904972], [-0.090591, -0.982641, -0.161898]], 'translation vector': [8.814532, 3.834109, 1.474353]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.999990277128188, -0.004263613841972057, 0.00022215499723240068], [0.004262324646153357, 0.9999855274813817, 0.003172824698500956], [-0.00023539502566653948, -0.003171844248534696, 0.9999944918809965]], 'translation vector': [0.0008976741232249452, 0.001107833658419377, 0.002287318056557207]}\nB: {'rotation matrix': [[0.982661, 0.058297, -0.176007], [0.185241, -0.268064, 0.945425], [0.007934, -0.961636, -0.274215]], 'translation vector': [4.071507, 1.217171, 1.479186]}\nC: {'rotation matrix': [[0.982484, 0.05703, -0.177406], [0.186213, -0.264319, 0.946288], [0.007075, -0.962748, -0.270309]], 'translation vector': [4.071419, 1.216069, 1.480649]}\nD: {'rotation matrix': [[0.98266, 0.058843, -0.175828], [0.185228, -0.2691, 0.945133], [0.008299, -0.961313, -0.275333]], 'translation vector': [4.071304, 1.217707, 1.478697]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_141_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_141_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_141_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_141_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.999990277128188, -0.004263613841972057, 0.00022215499723240068], [0.004262324646153357, 0.9999855274813817, 0.003172824698500956], [-0.00023539502566653948, -0.003171844248534696, 0.9999944918809965]], 'translation vector': [0.0008976741232249452, 0.001107833658419377, 0.002287318056557207]}\nB: {'rotation matrix': [[0.982661, 0.058297, -0.176007], [0.185241, -0.268064, 0.945425], [0.007934, -0.961636, -0.274215]], 'translation vector': [4.071507, 1.217171, 1.479186]}\nC: {'rotation matrix': [[0.982484, 0.05703, -0.177406], [0.186213, -0.264319, 0.946288], [0.007075, -0.962748, -0.270309]], 'translation vector': [4.071419, 1.216069, 1.480649]}\nD: {'rotation matrix': [[0.98266, 0.058843, -0.175828], [0.185228, -0.2691, 0.945133], [0.008299, -0.961313, -0.275333]], 'translation vector': [4.071304, 1.217707, 1.478697]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.566945, -0.119787, 0.815], [-0.823733, -0.07511, 0.561981], [-0.006103, -0.989954, -0.141256]], 'translation vector': [0.25398, 0.970235, 1.632712]}\nB: {'rotation matrix': [[0.566333, -0.122518, 0.81502], [-0.824133, -0.073956, 0.561548], [-0.008524, -0.989707, -0.142854]], 'translation vector': [0.252647, 0.969528, 1.633147]}\nC: {'rotation matrix': [[0.565918, -0.124531, 0.815003], [-0.824401, -0.073416, 0.561226], [-0.010056, -0.989496, -0.144211]], 'translation vector': [0.251636, 0.969331, 1.634009]}\nD: {'rotation matrix': [[0.9999988126320164, 0.0003312007428265276, -0.0004969284405179408], [-0.00033114016656371985, 0.9999998966821256, -0.0003458941517538565], [0.0004968245827711156, 0.000346552992150063, 0.9999997733502279]], 'translation vector': [5.9331917010907453e-05, -0.0008045063572443834, -0.0004101833402060384]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_142_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_142_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_142_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_142_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.566945, -0.119787, 0.815], [-0.823733, -0.07511, 0.561981], [-0.006103, -0.989954, -0.141256]], 'translation vector': [0.25398, 0.970235, 1.632712]}\nB: {'rotation matrix': [[0.566333, -0.122518, 0.81502], [-0.824133, -0.073956, 0.561548], [-0.008524, -0.989707, -0.142854]], 'translation vector': [0.252647, 0.969528, 1.633147]}\nC: {'rotation matrix': [[0.565918, -0.124531, 0.815003], [-0.824401, -0.073416, 0.561226], [-0.010056, -0.989496, -0.144211]], 'translation vector': [0.251636, 0.969331, 1.634009]}\nD: {'rotation matrix': [[0.9999988126320164, 0.0003312007428265276, -0.0004969284405179408], [-0.00033114016656371985, 0.9999998966821256, -0.0003458941517538565], [0.0004968245827711156, 0.000346552992150063, 0.9999997733502279]], 'translation vector': [5.9331917010907453e-05, -0.0008045063572443834, -0.0004101833402060384]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999807090620153, 0.004568922096108109, 0.004345106491864972], [-0.004581791622211755, 0.9999843128892885, 0.0029713844388234126], [-0.00433269576861675, -0.002991018231335416, 0.9999858202835764]], 'translation vector': [-0.011188164884389007, 0.009307427071622687, -0.0007429783939219003]}\nB: {'rotation matrix': [[-0.942483, -0.17354, 0.285674], [-0.333358, 0.550552, -0.765353], [-0.024459, -0.816564, -0.576737]], 'translation vector': [2.733535, 1.660706, 1.301168]}\nC: {'rotation matrix': [[-0.942594, -0.174193, 0.284909], [-0.333124, 0.550105, -0.765776], [-0.023337, -0.816726, -0.576554]], 'translation vector': [2.730048, 1.657302, 1.301829]}\nD: {'rotation matrix': [[-0.942586, -0.174069, 0.285013], [-0.333135, 0.550225, -0.765686], [-0.023539, -0.816672, -0.576622]], 'translation vector': [2.726519, 1.654368, 1.301906]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_143_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_143_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_143_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_143_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999807090620153, 0.004568922096108109, 0.004345106491864972], [-0.004581791622211755, 0.9999843128892885, 0.0029713844388234126], [-0.00433269576861675, -0.002991018231335416, 0.9999858202835764]], 'translation vector': [-0.011188164884389007, 0.009307427071622687, -0.0007429783939219003]}\nB: {'rotation matrix': [[-0.942483, -0.17354, 0.285674], [-0.333358, 0.550552, -0.765353], [-0.024459, -0.816564, -0.576737]], 'translation vector': [2.733535, 1.660706, 1.301168]}\nC: {'rotation matrix': [[-0.942594, -0.174193, 0.284909], [-0.333124, 0.550105, -0.765776], [-0.023337, -0.816726, -0.576554]], 'translation vector': [2.730048, 1.657302, 1.301829]}\nD: {'rotation matrix': [[-0.942586, -0.174069, 0.285013], [-0.333135, 0.550225, -0.765686], [-0.023539, -0.816672, -0.576622]], 'translation vector': [2.726519, 1.654368, 1.301906]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.662528, 0.400848, -0.632754], [0.747812, -0.402283, 0.528154], [-0.042837, -0.823097, -0.566283]], 'translation vector': [1.744816, 2.25794, 1.331918]}\nB: {'rotation matrix': [[0.662009, 0.40559, -0.630271], [0.748112, -0.408664, 0.522802], [-0.045526, -0.817613, -0.573966]], 'translation vector': [1.743048, 2.25768, 1.329749]}\nC: {'rotation matrix': [[0.6641, 0.398897, -0.632339], [0.746639, -0.397674, 0.533278], [-0.038742, -0.826279, -0.561927]], 'translation vector': [1.744615, 2.258795, 1.335923]}\nD: {'rotation matrix': [[0.9999981013423613, 0.0016775919055887754, -0.0009118672263504091], [-0.0016584483846788572, 0.999788802812158, 0.020485068642363036], [0.0009453490386347853, -0.020482492872648857, 0.9997898845593165]], 'translation vector': [0.0006281413363966593, 0.0014761974203534312, 0.005568137578716104]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_144_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_144_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_144_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_144_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.662528, 0.400848, -0.632754], [0.747812, -0.402283, 0.528154], [-0.042837, -0.823097, -0.566283]], 'translation vector': [1.744816, 2.25794, 1.331918]}\nB: {'rotation matrix': [[0.662009, 0.40559, -0.630271], [0.748112, -0.408664, 0.522802], [-0.045526, -0.817613, -0.573966]], 'translation vector': [1.743048, 2.25768, 1.329749]}\nC: {'rotation matrix': [[0.6641, 0.398897, -0.632339], [0.746639, -0.397674, 0.533278], [-0.038742, -0.826279, -0.561927]], 'translation vector': [1.744615, 2.258795, 1.335923]}\nD: {'rotation matrix': [[0.9999981013423613, 0.0016775919055887754, -0.0009118672263504091], [-0.0016584483846788572, 0.999788802812158, 0.020485068642363036], [0.0009453490386347853, -0.020482492872648857, 0.9997898845593165]], 'translation vector': [0.0006281413363966593, 0.0014761974203534312, 0.005568137578716104]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.44362, -0.475187, 0.759868], [-0.895973, 0.254861, -0.363701], [-0.020835, -0.842166, -0.538816]], 'translation vector': [2.452095, 1.901161, 1.451891]}\nB: {'rotation matrix': [[-0.440436, -0.475273, 0.761664], [-0.897497, 0.254555, -0.360142], [-0.02272, -0.84221, -0.538671]], 'translation vector': [2.449051, 1.900731, 1.449924]}\nC: {'rotation matrix': [[-0.441002, -0.475612, 0.761125], [-0.897234, 0.254511, -0.360825], [-0.022102, -0.842032, -0.538975]], 'translation vector': [2.451296, 1.899939, 1.450426]}\nD: {'rotation matrix': [[0.9999985864023682, -0.0015621221187893434, 0.0006936316269794283], [0.0015661737335896364, 0.9999849325621631, -0.005207380366478063], [-0.0006858389600734047, 0.005207931011548828, 0.9999859575854745]], 'translation vector': [-0.0017105359831024458, -0.002297103154811353, -0.000983146020886283]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_145_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_145_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_145_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_145_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.44362, -0.475187, 0.759868], [-0.895973, 0.254861, -0.363701], [-0.020835, -0.842166, -0.538816]], 'translation vector': [2.452095, 1.901161, 1.451891]}\nB: {'rotation matrix': [[-0.440436, -0.475273, 0.761664], [-0.897497, 0.254555, -0.360142], [-0.02272, -0.84221, -0.538671]], 'translation vector': [2.449051, 1.900731, 1.449924]}\nC: {'rotation matrix': [[-0.441002, -0.475612, 0.761125], [-0.897234, 0.254511, -0.360825], [-0.022102, -0.842032, -0.538975]], 'translation vector': [2.451296, 1.899939, 1.450426]}\nD: {'rotation matrix': [[0.9999985864023682, -0.0015621221187893434, 0.0006936316269794283], [0.0015661737335896364, 0.9999849325621631, -0.005207380366478063], [-0.0006858389600734047, 0.005207931011548828, 0.9999859575854745]], 'translation vector': [-0.0017105359831024458, -0.002297103154811353, -0.000983146020886283]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.648777, 0.514326, -0.560854], [0.760441, -0.465898, 0.452404], [-0.028617, -0.720006, -0.693378]], 'translation vector': [1.800914, 1.822078, 1.233863]}\nB: {'rotation matrix': [[0.644427, 0.520052, -0.560589], [0.76413, -0.465418, 0.446645], [-0.028629, -0.716192, -0.697315]], 'translation vector': [1.79848, 1.820985, 1.232666]}\nC: {'rotation matrix': [[0.9998704626137827, 0.011052401167760776, -0.011708701735332047], [-0.011032627408678441, 0.9999372608185829, 0.0017110617555067353], [0.011728123043277196, -0.001580982237636182, 0.9999298219541505]], 'translation vector': [-0.004951638312360451, 0.0003388210922784518, 0.0025384925972402606]}\nD: {'rotation matrix': [[0.639937, 0.524455, -0.56163], [0.767882, -0.464002, 0.441656], [-0.028969, -0.713897, -0.699651]], 'translation vector': [1.797021, 1.819882, 1.231178]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_146_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_146_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_146_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_146_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.648777, 0.514326, -0.560854], [0.760441, -0.465898, 0.452404], [-0.028617, -0.720006, -0.693378]], 'translation vector': [1.800914, 1.822078, 1.233863]}\nB: {'rotation matrix': [[0.644427, 0.520052, -0.560589], [0.76413, -0.465418, 0.446645], [-0.028629, -0.716192, -0.697315]], 'translation vector': [1.79848, 1.820985, 1.232666]}\nC: {'rotation matrix': [[0.9998704626137827, 0.011052401167760776, -0.011708701735332047], [-0.011032627408678441, 0.9999372608185829, 0.0017110617555067353], [0.011728123043277196, -0.001580982237636182, 0.9999298219541505]], 'translation vector': [-0.004951638312360451, 0.0003388210922784518, 0.0025384925972402606]}\nD: {'rotation matrix': [[0.639937, 0.524455, -0.56163], [0.767882, -0.464002, 0.441656], [-0.028969, -0.713897, -0.699651]], 'translation vector': [1.797021, 1.819882, 1.231178]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.480833, -0.463789, 0.74411], [-0.876135, 0.287476, -0.386968], [-0.034442, -0.838008, -0.54457]], 'translation vector': [3.084943, 2.078791, 1.469333]}\nB: {'rotation matrix': [[0.9999951018718091, -0.0022147244398398017, -0.0018850296839706259], [0.002209800973893798, 0.9999949398931387, -0.0022506347742728594], [0.0018898473296588083, 0.0022461573677036097, 0.9999958361862953]], 'translation vector': [0.004387368548260717, 1.4705105160661702e-05, 0.0008301488900060994]}\nC: {'rotation matrix': [[-0.476687, -0.464053, 0.746608], [-0.878377, 0.285219, -0.383541], [-0.034963, -0.838633, -0.543574]], 'translation vector': [3.080459, 2.078543, 1.469168]}\nD: {'rotation matrix': [[-0.479567, -0.463643, 0.745017], [-0.876821, 0.286706, -0.385985], [-0.034641, -0.838352, -0.544027]], 'translation vector': [3.083795, 2.079285, 1.469908]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_147_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_147_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_147_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_147_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.480833, -0.463789, 0.74411], [-0.876135, 0.287476, -0.386968], [-0.034442, -0.838008, -0.54457]], 'translation vector': [3.084943, 2.078791, 1.469333]}\nB: {'rotation matrix': [[0.9999951018718091, -0.0022147244398398017, -0.0018850296839706259], [0.002209800973893798, 0.9999949398931387, -0.0022506347742728594], [0.0018898473296588083, 0.0022461573677036097, 0.9999958361862953]], 'translation vector': [0.004387368548260717, 1.4705105160661702e-05, 0.0008301488900060994]}\nC: {'rotation matrix': [[-0.476687, -0.464053, 0.746608], [-0.878377, 0.285219, -0.383541], [-0.034963, -0.838633, -0.543574]], 'translation vector': [3.080459, 2.078543, 1.469168]}\nD: {'rotation matrix': [[-0.479567, -0.463643, 0.745017], [-0.876821, 0.286706, -0.385985], [-0.034641, -0.838352, -0.544027]], 'translation vector': [3.083795, 2.079285, 1.469908]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.724797, -0.022386, -0.688599], [0.687785, -0.034918, 0.725075], [-0.040276, -0.999139, -0.009911]], 'translation vector': [1.871804, 0.814995, 1.597738]}\nB: {'rotation matrix': [[0.729664, -0.019712, -0.683522], [0.682722, -0.035273, 0.729827], [-0.038496, -0.999183, -0.01228]], 'translation vector': [1.870321, 0.812422, 1.590842]}\nC: {'rotation matrix': [[0.9999971996131989, 0.0007346987095039795, -0.0017367305867328272], [-0.0007296724426740893, 0.9999944712211791, 0.003354984500413017], [0.0017384108444949038, -0.003353839188726123, 0.9999931577143994]], 'translation vector': [-0.0004548504518708807, 0.001951786856664972, -7.749986575322776e-05]}\nD: {'rotation matrix': [[0.728234, -0.022145, -0.684971], [0.684198, -0.033912, 0.728508], [-0.039361, -0.99918, -0.009544]], 'translation vector': [1.869489, 0.812101, 1.591189]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_148_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_148_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_148_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_148_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.724797, -0.022386, -0.688599], [0.687785, -0.034918, 0.725075], [-0.040276, -0.999139, -0.009911]], 'translation vector': [1.871804, 0.814995, 1.597738]}\nB: {'rotation matrix': [[0.729664, -0.019712, -0.683522], [0.682722, -0.035273, 0.729827], [-0.038496, -0.999183, -0.01228]], 'translation vector': [1.870321, 0.812422, 1.590842]}\nC: {'rotation matrix': [[0.9999971996131989, 0.0007346987095039795, -0.0017367305867328272], [-0.0007296724426740893, 0.9999944712211791, 0.003354984500413017], [0.0017384108444949038, -0.003353839188726123, 0.9999931577143994]], 'translation vector': [-0.0004548504518708807, 0.001951786856664972, -7.749986575322776e-05]}\nD: {'rotation matrix': [[0.728234, -0.022145, -0.684971], [0.684198, -0.033912, 0.728508], [-0.039361, -0.99918, -0.009544]], 'translation vector': [1.869489, 0.812101, 1.591189]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999774991279762, 0.0031690326046340087, -0.005996753212157376], [-0.003090362324076843, 0.9999123878108386, 0.012868971987653289], [0.006038036583570517, -0.012849517542798453, 0.9998986268025651]], 'translation vector': [-0.00016965780714706113, -0.008841569485431133, 0.004505805451807898]}\nB: {'rotation matrix': [[0.651481, -0.368876, 0.66295], [-0.758449, -0.337487, 0.557546], [0.018072, -0.866045, -0.49964]], 'translation vector': [2.471969, 4.600353, 1.449958]}\nC: {'rotation matrix': [[0.655694, -0.362412, 0.662362], [-0.754768, -0.337631, 0.562433], [0.019802, -0.868713, -0.49492]], 'translation vector': [2.472568, 4.599315, 1.447954]}\nD: {'rotation matrix': [[0.660006, -0.356371, 0.661356], [-0.750952, -0.338178, 0.567192], [0.021525, -0.870997, -0.490817]], 'translation vector': [2.470351, 4.598146, 1.447521]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_149_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_149_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_149_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_149_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999774991279762, 0.0031690326046340087, -0.005996753212157376], [-0.003090362324076843, 0.9999123878108386, 0.012868971987653289], [0.006038036583570517, -0.012849517542798453, 0.9998986268025651]], 'translation vector': [-0.00016965780714706113, -0.008841569485431133, 0.004505805451807898]}\nB: {'rotation matrix': [[0.651481, -0.368876, 0.66295], [-0.758449, -0.337487, 0.557546], [0.018072, -0.866045, -0.49964]], 'translation vector': [2.471969, 4.600353, 1.449958]}\nC: {'rotation matrix': [[0.655694, -0.362412, 0.662362], [-0.754768, -0.337631, 0.562433], [0.019802, -0.868713, -0.49492]], 'translation vector': [2.472568, 4.599315, 1.447954]}\nD: {'rotation matrix': [[0.660006, -0.356371, 0.661356], [-0.750952, -0.338178, 0.567192], [0.021525, -0.870997, -0.490817]], 'translation vector': [2.470351, 4.598146, 1.447521]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.550599, -0.608246, 0.571732], [-0.834661, 0.412205, -0.365279], [-0.013491, -0.678325, -0.734639]], 'translation vector': [2.153644, 1.764514, 1.342866]}\nB: {'rotation matrix': [[0.9999831845983815, -0.004759897354970956, 0.0032519818043716545], [0.004774936203352942, 0.9999789289359345, -0.004542082720936493], [-0.003229136166379075, 0.004558818034209968, 0.9999846215935988]], 'translation vector': [0.0033377456840164577, 0.0021763424534348985, -0.0009933558524138353]}\nC: {'rotation matrix': [[-0.553936, -0.603859, 0.573158], [-0.832399, 0.415212, -0.36703], [-0.016348, -0.680407, -0.732652]], 'translation vector': [2.15044, 1.76409, 1.342895]}\nD: {'rotation matrix': [[-0.551929, -0.606401, 0.572409], [-0.833765, 0.413226, -0.366169], [-0.014489, -0.679354, -0.733668]], 'translation vector': [2.152355, 1.764444, 1.342815]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_150_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_150_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_150_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_150_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.550599, -0.608246, 0.571732], [-0.834661, 0.412205, -0.365279], [-0.013491, -0.678325, -0.734639]], 'translation vector': [2.153644, 1.764514, 1.342866]}\nB: {'rotation matrix': [[0.9999831845983815, -0.004759897354970956, 0.0032519818043716545], [0.004774936203352942, 0.9999789289359345, -0.004542082720936493], [-0.003229136166379075, 0.004558818034209968, 0.9999846215935988]], 'translation vector': [0.0033377456840164577, 0.0021763424534348985, -0.0009933558524138353]}\nC: {'rotation matrix': [[-0.553936, -0.603859, 0.573158], [-0.832399, 0.415212, -0.36703], [-0.016348, -0.680407, -0.732652]], 'translation vector': [2.15044, 1.76409, 1.342895]}\nD: {'rotation matrix': [[-0.551929, -0.606401, 0.572409], [-0.833765, 0.413226, -0.366169], [-0.014489, -0.679354, -0.733668]], 'translation vector': [2.152355, 1.764444, 1.342815]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.254539, -0.436075, 0.863162], [-0.966632, 0.141348, -0.213642], [-0.028842, -0.88874, -0.457503]], 'translation vector': [1.735428, 0.748356, 1.43337]}\nB: {'rotation matrix': [[-0.254681, -0.434936, 0.863695], [-0.966604, 0.140846, -0.2141], [-0.028528, -0.889378, -0.456282]], 'translation vector': [1.735372, 0.748905, 1.433089]}\nC: {'rotation matrix': [[1.0000001555675329, -0.0009323657310693132, 0.0004792288755131117], [0.0009313044295569888, 0.9999972868981549, 0.0021526068874492825], [-0.0004812492477208415, -0.0021517811624316304, 0.9999974478268276]], 'translation vector': [0.0025109364715583116, 0.0011773606935274739, 0.0008952278670837366]}\nD: {'rotation matrix': [[-0.254466, -0.434667, 0.863893], [-0.966638, 0.141384, -0.213593], [-0.029299, -0.889424, -0.456143]], 'translation vector': [1.735598, 0.749181, 1.433436]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_151_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_151_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_151_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_151_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.254539, -0.436075, 0.863162], [-0.966632, 0.141348, -0.213642], [-0.028842, -0.88874, -0.457503]], 'translation vector': [1.735428, 0.748356, 1.43337]}\nB: {'rotation matrix': [[-0.254681, -0.434936, 0.863695], [-0.966604, 0.140846, -0.2141], [-0.028528, -0.889378, -0.456282]], 'translation vector': [1.735372, 0.748905, 1.433089]}\nC: {'rotation matrix': [[1.0000001555675329, -0.0009323657310693132, 0.0004792288755131117], [0.0009313044295569888, 0.9999972868981549, 0.0021526068874492825], [-0.0004812492477208415, -0.0021517811624316304, 0.9999974478268276]], 'translation vector': [0.0025109364715583116, 0.0011773606935274739, 0.0008952278670837366]}\nD: {'rotation matrix': [[-0.254466, -0.434667, 0.863893], [-0.966638, 0.141384, -0.213593], [-0.029299, -0.889424, -0.456143]], 'translation vector': [1.735598, 0.749181, 1.433436]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.874972, 0.120483, -0.468943], [0.467945, 0.459094, -0.755156], [0.124305, -0.88018, -0.458074]], 'translation vector': [3.924764, 3.210204, 1.74232]}\nB: {'rotation matrix': [[-0.872887, 0.124749, -0.471706], [0.47258, 0.456684, -0.75373], [0.121394, -0.880839, -0.457587]], 'translation vector': [3.924155, 3.192614, 1.742181]}\nC: {'rotation matrix': [[-0.873832, 0.122242, -0.470612], [0.470279, 0.458348, -0.754158], [0.123514, -0.880326, -0.458007]], 'translation vector': [3.924731, 3.201921, 1.742944]}\nD: {'rotation matrix': [[0.9999981203399105, -2.162890380847432e-05, -0.0011856852248069943], [2.6074892574446623e-05, 0.9999907421564089, 0.004166234818762547], [0.0011853825032994, -0.0041669736288708105, 0.9999895516522684]], 'translation vector': [0.0031018447373007962, -0.004160693298826068, -0.00448172332630925]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_152_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_152_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_152_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_152_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.874972, 0.120483, -0.468943], [0.467945, 0.459094, -0.755156], [0.124305, -0.88018, -0.458074]], 'translation vector': [3.924764, 3.210204, 1.74232]}\nB: {'rotation matrix': [[-0.872887, 0.124749, -0.471706], [0.47258, 0.456684, -0.75373], [0.121394, -0.880839, -0.457587]], 'translation vector': [3.924155, 3.192614, 1.742181]}\nC: {'rotation matrix': [[-0.873832, 0.122242, -0.470612], [0.470279, 0.458348, -0.754158], [0.123514, -0.880326, -0.458007]], 'translation vector': [3.924731, 3.201921, 1.742944]}\nD: {'rotation matrix': [[0.9999981203399105, -2.162890380847432e-05, -0.0011856852248069943], [2.6074892574446623e-05, 0.9999907421564089, 0.004166234818762547], [0.0011853825032994, -0.0041669736288708105, 0.9999895516522684]], 'translation vector': [0.0031018447373007962, -0.004160693298826068, -0.00448172332630925]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.910706, 0.180669, -0.371448], [0.412445, 0.446604, -0.793999], [0.022439, -0.876301, -0.481241]], 'translation vector': [3.200821, 1.957629, 1.277707]}\nB: {'rotation matrix': [[-0.909073, 0.181004, -0.375266], [0.415983, 0.444783, -0.793175], [0.023344, -0.877158, -0.479635]], 'translation vector': [3.199567, 1.957217, 1.278564]}\nC: {'rotation matrix': [[-0.912991, 0.180032, -0.36611], [0.407432, 0.448869, -0.795309], [0.021154, -0.875275, -0.483163]], 'translation vector': [3.201129, 1.957814, 1.275703]}\nD: {'rotation matrix': [[0.9998091200876347, 0.0024899817556151226, -0.01932364538042797], [-0.0024534091295022355, 0.9999947392396931, 0.0019198938596762963], [0.019328984816227378, -0.0018724870057808746, 0.9998116886101109]], 'translation vector': [-0.0018727616018998638, 0.007005445282938005, 8.97167203275373e-05]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_153_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_153_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_153_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_153_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.910706, 0.180669, -0.371448], [0.412445, 0.446604, -0.793999], [0.022439, -0.876301, -0.481241]], 'translation vector': [3.200821, 1.957629, 1.277707]}\nB: {'rotation matrix': [[-0.909073, 0.181004, -0.375266], [0.415983, 0.444783, -0.793175], [0.023344, -0.877158, -0.479635]], 'translation vector': [3.199567, 1.957217, 1.278564]}\nC: {'rotation matrix': [[-0.912991, 0.180032, -0.36611], [0.407432, 0.448869, -0.795309], [0.021154, -0.875275, -0.483163]], 'translation vector': [3.201129, 1.957814, 1.275703]}\nD: {'rotation matrix': [[0.9998091200876347, 0.0024899817556151226, -0.01932364538042797], [-0.0024534091295022355, 0.9999947392396931, 0.0019198938596762963], [0.019328984816227378, -0.0018724870057808746, 0.9998116886101109]], 'translation vector': [-0.0018727616018998638, 0.007005445282938005, 8.97167203275373e-05]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999837026106017, -0.005604330268430811, -0.00017160014562389917], [0.005604883614299803, 0.9999826757832918, 0.0019021058633347495], [0.00016097085173832394, -0.0019036155522551726, 0.9999985288928479]], 'translation vector': [-0.0002532483433466126, 0.009030133518173555, 0.0039788864378584865]}\nB: {'rotation matrix': [[-0.221399, -0.409647, 0.88497], [-0.971033, 0.176243, -0.161347], [-0.089875, -0.895057, -0.436801]], 'translation vector': [2.157726, 10.114248, 1.730212]}\nC: {'rotation matrix': [[-0.233773, -0.418838, 0.877454], [-0.967259, 0.191883, -0.166106], [-0.098797, -0.887556, -0.449982]], 'translation vector': [2.163177, 10.11361, 1.729991]}\nD: {'rotation matrix': [[-0.208812, -0.406957, 0.88926], [-0.974382, 0.164243, -0.153636], [-0.083532, -0.898561, -0.430827]], 'translation vector': [2.154821, 10.118629, 1.726458]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_154_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_154_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_154_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_154_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999837026106017, -0.005604330268430811, -0.00017160014562389917], [0.005604883614299803, 0.9999826757832918, 0.0019021058633347495], [0.00016097085173832394, -0.0019036155522551726, 0.9999985288928479]], 'translation vector': [-0.0002532483433466126, 0.009030133518173555, 0.0039788864378584865]}\nB: {'rotation matrix': [[-0.221399, -0.409647, 0.88497], [-0.971033, 0.176243, -0.161347], [-0.089875, -0.895057, -0.436801]], 'translation vector': [2.157726, 10.114248, 1.730212]}\nC: {'rotation matrix': [[-0.233773, -0.418838, 0.877454], [-0.967259, 0.191883, -0.166106], [-0.098797, -0.887556, -0.449982]], 'translation vector': [2.163177, 10.11361, 1.729991]}\nD: {'rotation matrix': [[-0.208812, -0.406957, 0.88926], [-0.974382, 0.164243, -0.153636], [-0.083532, -0.898561, -0.430827]], 'translation vector': [2.154821, 10.118629, 1.726458]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.864148, -0.19236, 0.465022], [-0.502138, -0.268523, 0.822042], [-0.033259, -0.943871, -0.328635]], 'translation vector': [3.016374, 2.015361, 1.429191]}\nB: {'rotation matrix': [[0.864117, -0.192314, 0.465099], [-0.502115, -0.266283, 0.822784], [-0.034385, -0.944515, -0.326663]], 'translation vector': [3.015528, 2.015384, 1.428328]}\nC: {'rotation matrix': [[0.864639, -0.19262, 0.464001], [-0.501175, -0.266422, 0.823312], [-0.034966, -0.944414, -0.326895]], 'translation vector': [3.015996, 2.015925, 1.430969]}\nD: {'rotation matrix': [[0.9999953542146168, 0.0030947830980374525, -0.0008131951628852412], [-0.0030994452771746766, 0.999974002426472, -0.006530521364187943], [0.00079388588123918, 0.0065324457136649635, 0.999978487244639]], 'translation vector': [-0.004468736350602409, -0.006227529584318603, -8.95755650023311e-05]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_155_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_155_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_155_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_155_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.864148, -0.19236, 0.465022], [-0.502138, -0.268523, 0.822042], [-0.033259, -0.943871, -0.328635]], 'translation vector': [3.016374, 2.015361, 1.429191]}\nB: {'rotation matrix': [[0.864117, -0.192314, 0.465099], [-0.502115, -0.266283, 0.822784], [-0.034385, -0.944515, -0.326663]], 'translation vector': [3.015528, 2.015384, 1.428328]}\nC: {'rotation matrix': [[0.864639, -0.19262, 0.464001], [-0.501175, -0.266422, 0.823312], [-0.034966, -0.944414, -0.326895]], 'translation vector': [3.015996, 2.015925, 1.430969]}\nD: {'rotation matrix': [[0.9999953542146168, 0.0030947830980374525, -0.0008131951628852412], [-0.0030994452771746766, 0.999974002426472, -0.006530521364187943], [0.00079388588123918, 0.0065324457136649635, 0.999978487244639]], 'translation vector': [-0.004468736350602409, -0.006227529584318603, -8.95755650023311e-05]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[1.0000001305586184, 4.504585202989606e-05, -0.0008677063367108602], [-4.562855519759043e-05, 0.9999983740715708, -0.001562168642633918], [0.0008668198225227341, 0.0015626467626293078, 0.9999978423516577]], 'translation vector': [0.004048003920170018, -0.000314296777893075, -0.001713133752151652]}\nB: {'rotation matrix': [[0.932237, 0.077212, -0.353515], [0.361617, -0.233791, 0.902538], [-0.012962, -0.969216, -0.24587]], 'translation vector': [5.874094, 3.546493, 1.351525]}\nC: {'rotation matrix': [[0.932237, 0.075134, -0.353962], [0.361548, -0.233262, 0.902703], [-0.014743, -0.969507, -0.24462]], 'translation vector': [5.877609, 3.546074, 1.353866]}\nD: {'rotation matrix': [[0.932245, 0.073757, -0.354232], [0.36147, -0.233417, 0.902694], [-0.016104, -0.969576, -0.244262]], 'translation vector': [5.878981, 3.545327, 1.355029]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_156_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_156_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_156_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_156_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[1.0000001305586184, 4.504585202989606e-05, -0.0008677063367108602], [-4.562855519759043e-05, 0.9999983740715708, -0.001562168642633918], [0.0008668198225227341, 0.0015626467626293078, 0.9999978423516577]], 'translation vector': [0.004048003920170018, -0.000314296777893075, -0.001713133752151652]}\nB: {'rotation matrix': [[0.932237, 0.077212, -0.353515], [0.361617, -0.233791, 0.902538], [-0.012962, -0.969216, -0.24587]], 'translation vector': [5.874094, 3.546493, 1.351525]}\nC: {'rotation matrix': [[0.932237, 0.075134, -0.353962], [0.361548, -0.233262, 0.902703], [-0.014743, -0.969507, -0.24462]], 'translation vector': [5.877609, 3.546074, 1.353866]}\nD: {'rotation matrix': [[0.932245, 0.073757, -0.354232], [0.36147, -0.233417, 0.902694], [-0.016104, -0.969576, -0.244262]], 'translation vector': [5.878981, 3.545327, 1.355029]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.967201, -0.070709, 0.243972], [-0.252115, 0.384385, -0.88808], [-0.030984, -0.920461, -0.389605]], 'translation vector': [2.768098, 4.614564, 1.418844]}\nB: {'rotation matrix': [[-0.968215, -0.068836, 0.240461], [-0.248279, 0.380909, -0.890655], [-0.030285, -0.922047, -0.385893]], 'translation vector': [2.770223, 4.618487, 1.418033]}\nC: {'rotation matrix': [[0.9999417292568685, -0.00016387346525461165, 0.010781387930699021], [0.00016087662233447972, 0.9999998236818622, 0.00023678654461243325], [-0.010781286291867453, -0.000235115727098794, 0.9999413646146618]], 'translation vector': [-0.004146218083109776, -0.009777600200150782, -9.031272009885072e-05]}\nD: {'rotation matrix': [[-0.966004, -0.071389, 0.248475], [-0.25638, 0.388148, -0.885218], [-0.03325, -0.918828, -0.393256]], 'translation vector': [2.766369, 4.610029, 1.423364]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_157_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_157_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_157_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_157_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.967201, -0.070709, 0.243972], [-0.252115, 0.384385, -0.88808], [-0.030984, -0.920461, -0.389605]], 'translation vector': [2.768098, 4.614564, 1.418844]}\nB: {'rotation matrix': [[-0.968215, -0.068836, 0.240461], [-0.248279, 0.380909, -0.890655], [-0.030285, -0.922047, -0.385893]], 'translation vector': [2.770223, 4.618487, 1.418033]}\nC: {'rotation matrix': [[0.9999417292568685, -0.00016387346525461165, 0.010781387930699021], [0.00016087662233447972, 0.9999998236818622, 0.00023678654461243325], [-0.010781286291867453, -0.000235115727098794, 0.9999413646146618]], 'translation vector': [-0.004146218083109776, -0.009777600200150782, -9.031272009885072e-05]}\nD: {'rotation matrix': [[-0.966004, -0.071389, 0.248475], [-0.25638, 0.388148, -0.885218], [-0.03325, -0.918828, -0.393256]], 'translation vector': [2.766369, 4.610029, 1.423364]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.819175, -0.184232, 0.543149], [-0.572951, -0.305906, 0.760361], [0.02607, -0.934066, -0.356147]], 'translation vector': [4.417876, 1.783865, 1.2754]}\nB: {'rotation matrix': [[0.815086, -0.186535, 0.548488], [-0.578729, -0.305625, 0.756086], [0.026595, -0.933701, -0.357064]], 'translation vector': [4.412413, 1.788676, 1.273151]}\nC: {'rotation matrix': [[0.9999460053456457, -0.005013531268923329, 0.00905468563745482], [0.0050087072013681256, 0.9999874429708866, 0.000635311021125049], [-0.009056848323318739, -0.0005890721337560823, 0.9999585496849335]], 'translation vector': [-0.01186208886847595, 0.011040583723211927, 0.005770402802990571]}\nD: {'rotation matrix': [[0.823616, -0.181983, 0.537158], [-0.566609, -0.305313, 0.765336], [0.024723, -0.934701, -0.354574]], 'translation vector': [4.422497, 1.777795, 1.277376]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_158_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_158_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_158_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_158_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.819175, -0.184232, 0.543149], [-0.572951, -0.305906, 0.760361], [0.02607, -0.934066, -0.356147]], 'translation vector': [4.417876, 1.783865, 1.2754]}\nB: {'rotation matrix': [[0.815086, -0.186535, 0.548488], [-0.578729, -0.305625, 0.756086], [0.026595, -0.933701, -0.357064]], 'translation vector': [4.412413, 1.788676, 1.273151]}\nC: {'rotation matrix': [[0.9999460053456457, -0.005013531268923329, 0.00905468563745482], [0.0050087072013681256, 0.9999874429708866, 0.000635311021125049], [-0.009056848323318739, -0.0005890721337560823, 0.9999585496849335]], 'translation vector': [-0.01186208886847595, 0.011040583723211927, 0.005770402802990571]}\nD: {'rotation matrix': [[0.823616, -0.181983, 0.537158], [-0.566609, -0.305313, 0.765336], [0.024723, -0.934701, -0.354574]], 'translation vector': [4.422497, 1.777795, 1.277376]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9994834257163207, -0.009826144818556364, 0.03061241068052498], [0.009193271124449974, 0.9997423555682264, 0.020759381716764332], [-0.0308087787211077, -0.020466954157546582, 0.999315737926551]], 'translation vector': [-0.012172691673165703, -0.014778681947341665, 0.009780168112213383]}\nB: {'rotation matrix': [[0.091644, -0.414787, 0.905292], [-0.995361, -0.064895, 0.071028], [0.029288, -0.907601, -0.418811]], 'translation vector': [1.315562, 0.832314, 1.493138]}\nC: {'rotation matrix': [[0.099917, -0.418908, 0.902515], [-0.994362, -0.074403, 0.07555], [0.035502, -0.904975, -0.42398]], 'translation vector': [1.316564, 0.827844, 1.497329]}\nD: {'rotation matrix': [[0.095483, -0.419292, 0.902816], [-0.994923, -0.069178, 0.073096], [0.031806, -0.905212, -0.423769]], 'translation vector': [1.317067, 0.829593, 1.4954]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_159_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_159_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_159_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_159_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9994834257163207, -0.009826144818556364, 0.03061241068052498], [0.009193271124449974, 0.9997423555682264, 0.020759381716764332], [-0.0308087787211077, -0.020466954157546582, 0.999315737926551]], 'translation vector': [-0.012172691673165703, -0.014778681947341665, 0.009780168112213383]}\nB: {'rotation matrix': [[0.091644, -0.414787, 0.905292], [-0.995361, -0.064895, 0.071028], [0.029288, -0.907601, -0.418811]], 'translation vector': [1.315562, 0.832314, 1.493138]}\nC: {'rotation matrix': [[0.099917, -0.418908, 0.902515], [-0.994362, -0.074403, 0.07555], [0.035502, -0.904975, -0.42398]], 'translation vector': [1.316564, 0.827844, 1.497329]}\nD: {'rotation matrix': [[0.095483, -0.419292, 0.902816], [-0.994923, -0.069178, 0.073096], [0.031806, -0.905212, -0.423769]], 'translation vector': [1.317067, 0.829593, 1.4954]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.529968, 0.414216, -0.739972], [0.844868, 0.182762, -0.502789], [-0.073025, -0.891641, -0.446816]], 'translation vector': [5.418643, 4.410617, 1.386009]}\nB: {'rotation matrix': [[-0.531013, 0.418177, -0.736989], [0.843374, 0.176517, -0.507507], [-0.082137, -0.89105, -0.446413]], 'translation vector': [5.416763, 4.405288, 1.382813]}\nC: {'rotation matrix': [[0.9999262982512661, -0.011126048765088865, -0.004724929479929227], [0.011121653443920318, 0.9999373200698624, -0.0008601073344245507], [0.00473500376540592, 0.0008076436796648745, 0.9999882661936769]], 'translation vector': [-0.020480989578110398, -0.004401497485728045, 0.008145336008434256]}\nD: {'rotation matrix': [[-0.533157, 0.409147, -0.740501], [0.84318, 0.185365, -0.504666], [-0.06922, -0.893442, -0.443813]], 'translation vector': [5.417671, 4.419961, 1.384383]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_160_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_160_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_160_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_160_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.529968, 0.414216, -0.739972], [0.844868, 0.182762, -0.502789], [-0.073025, -0.891641, -0.446816]], 'translation vector': [5.418643, 4.410617, 1.386009]}\nB: {'rotation matrix': [[-0.531013, 0.418177, -0.736989], [0.843374, 0.176517, -0.507507], [-0.082137, -0.89105, -0.446413]], 'translation vector': [5.416763, 4.405288, 1.382813]}\nC: {'rotation matrix': [[0.9999262982512661, -0.011126048765088865, -0.004724929479929227], [0.011121653443920318, 0.9999373200698624, -0.0008601073344245507], [0.00473500376540592, 0.0008076436796648745, 0.9999882661936769]], 'translation vector': [-0.020480989578110398, -0.004401497485728045, 0.008145336008434256]}\nD: {'rotation matrix': [[-0.533157, 0.409147, -0.740501], [0.84318, 0.185365, -0.504666], [-0.06922, -0.893442, -0.443813]], 'translation vector': [5.417671, 4.419961, 1.384383]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.848714, 0.230926, -0.475771], [0.528497, 0.337392, -0.77901], [-0.019373, -0.912601, -0.408393]], 'translation vector': [1.792868, 5.329395, 1.618046]}\nB: {'rotation matrix': [[-0.84982, 0.23191, -0.473312], [0.526645, 0.337444, -0.780241], [-0.021229, -0.912332, -0.408901]], 'translation vector': [1.792819, 5.327111, 1.618396]}\nC: {'rotation matrix': [[-0.847697, 0.231565, -0.477271], [0.530179, 0.339488, -0.776955], [-0.017888, -0.911661, -0.410554]], 'translation vector': [1.79081, 5.325803, 1.623639]}\nD: {'rotation matrix': [[0.9999931647889151, 0.002636603414451914, -0.0022507832348392875], [-0.002639482011584797, 0.9999963226575643, -0.0013481976836917885], [0.002247569136725343, 0.001353829445398431, 0.9999964854536705]], 'translation vector': [-0.00651758527214108, -0.0013521488456044173, 0.0015986017122768814]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_161_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_161_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_161_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_161_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.848714, 0.230926, -0.475771], [0.528497, 0.337392, -0.77901], [-0.019373, -0.912601, -0.408393]], 'translation vector': [1.792868, 5.329395, 1.618046]}\nB: {'rotation matrix': [[-0.84982, 0.23191, -0.473312], [0.526645, 0.337444, -0.780241], [-0.021229, -0.912332, -0.408901]], 'translation vector': [1.792819, 5.327111, 1.618396]}\nC: {'rotation matrix': [[-0.847697, 0.231565, -0.477271], [0.530179, 0.339488, -0.776955], [-0.017888, -0.911661, -0.410554]], 'translation vector': [1.79081, 5.325803, 1.623639]}\nD: {'rotation matrix': [[0.9999931647889151, 0.002636603414451914, -0.0022507832348392875], [-0.002639482011584797, 0.9999963226575643, -0.0013481976836917885], [0.002247569136725343, 0.001353829445398431, 0.9999964854536705]], 'translation vector': [-0.00651758527214108, -0.0013521488456044173, 0.0015986017122768814]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999401570739126, -0.0046057655246039214, 0.009916971929525087], [0.004585227566153826, 0.9999870493451988, 0.0021285393012493415], [-0.009927494824793211, -0.0020825415412263123, 0.9999492671696912]], 'translation vector': [-0.008397334377028054, -0.00983275627665292, -0.005241897912080518]}\nB: {'rotation matrix': [[0.979087, -0.093288, 0.180791], [-0.203431, -0.440264, 0.874519], [-0.001987, -0.893009, -0.450035]], 'translation vector': [1.973386, 0.601511, 1.693802]}\nC: {'rotation matrix': [[0.980067, -0.092864, 0.175631], [-0.198651, -0.445785, 0.872819], [-0.002761, -0.89031, -0.455347]], 'translation vector': [1.967233, 0.628282, 1.699375]}\nD: {'rotation matrix': [[0.978621, -0.096572, 0.18159], [-0.205599, -0.435758, 0.876267], [-0.005494, -0.894868, -0.446298]], 'translation vector': [1.993303, 0.583546, 1.694907]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_162_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_162_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_162_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_162_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999401570739126, -0.0046057655246039214, 0.009916971929525087], [0.004585227566153826, 0.9999870493451988, 0.0021285393012493415], [-0.009927494824793211, -0.0020825415412263123, 0.9999492671696912]], 'translation vector': [-0.008397334377028054, -0.00983275627665292, -0.005241897912080518]}\nB: {'rotation matrix': [[0.979087, -0.093288, 0.180791], [-0.203431, -0.440264, 0.874519], [-0.001987, -0.893009, -0.450035]], 'translation vector': [1.973386, 0.601511, 1.693802]}\nC: {'rotation matrix': [[0.980067, -0.092864, 0.175631], [-0.198651, -0.445785, 0.872819], [-0.002761, -0.89031, -0.455347]], 'translation vector': [1.967233, 0.628282, 1.699375]}\nD: {'rotation matrix': [[0.978621, -0.096572, 0.18159], [-0.205599, -0.435758, 0.876267], [-0.005494, -0.894868, -0.446298]], 'translation vector': [1.993303, 0.583546, 1.694907]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.170548, -0.038579, 0.984594], [-0.984686, -0.02999, -0.171739], [0.036154, -0.998805, -0.032873]], 'translation vector': [3.062037, 2.44503, 1.503093]}\nB: {'rotation matrix': [[-0.179914, -0.049674, 0.982427], [-0.983099, -0.025325, -0.181318], [0.033887, -0.998444, -0.044279]], 'translation vector': [3.062168, 2.448737, 1.498158]}\nC: {'rotation matrix': [[-0.175595, -0.045495, 0.983411], [-0.983814, -0.028148, -0.176969], [0.035732, -0.998568, -0.039815]], 'translation vector': [3.062089, 2.446902, 1.500845]}\nD: {'rotation matrix': [[0.9999851894661843, 0.0005818244206442844, -0.005337253553840107], [-0.0006352685106689393, 0.9999500541572905, -0.009997050540678364], [0.005330302626435351, 0.010000486202961777, 0.9999353803458123]], 'translation vector': [0.013561096331675682, -0.004517035589624685, -0.0041143791607543]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_163_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_163_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_163_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_163_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.170548, -0.038579, 0.984594], [-0.984686, -0.02999, -0.171739], [0.036154, -0.998805, -0.032873]], 'translation vector': [3.062037, 2.44503, 1.503093]}\nB: {'rotation matrix': [[-0.179914, -0.049674, 0.982427], [-0.983099, -0.025325, -0.181318], [0.033887, -0.998444, -0.044279]], 'translation vector': [3.062168, 2.448737, 1.498158]}\nC: {'rotation matrix': [[-0.175595, -0.045495, 0.983411], [-0.983814, -0.028148, -0.176969], [0.035732, -0.998568, -0.039815]], 'translation vector': [3.062089, 2.446902, 1.500845]}\nD: {'rotation matrix': [[0.9999851894661843, 0.0005818244206442844, -0.005337253553840107], [-0.0006352685106689393, 0.9999500541572905, -0.009997050540678364], [0.005330302626435351, 0.010000486202961777, 0.9999353803458123]], 'translation vector': [0.013561096331675682, -0.004517035589624685, -0.0041143791607543]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.879731, -0.18442, 0.43825], [-0.471339, -0.216982, 0.854844], [-0.062558, -0.958597, -0.27781]], 'translation vector': [1.012821, 1.289106, 1.470609]}\nB: {'rotation matrix': [[0.877414, -0.186393, 0.442044], [-0.476234, -0.227312, 0.84943], [-0.057846, -0.955818, -0.288213]], 'translation vector': [1.033287, 1.302455, 1.466311]}\nC: {'rotation matrix': [[0.999991817168325, 0.002932358485082305, -0.002640226065149888], [-0.0029080462382595554, 0.9999549882789169, 0.009034252562416854], [0.0026665546966547606, -0.009026772459937056, 0.9999556430330881]], 'translation vector': [0.005120345387922942, -0.0015772155749180783, 0.009569803755594242]}\nD: {'rotation matrix': [[0.878466, -0.185862, 0.440175], [-0.473882, -0.221089, 0.852382], [-0.061107, -0.957379, -0.282296]], 'translation vector': [1.023458, 1.295782, 1.469602]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_164_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_164_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_164_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_164_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.879731, -0.18442, 0.43825], [-0.471339, -0.216982, 0.854844], [-0.062558, -0.958597, -0.27781]], 'translation vector': [1.012821, 1.289106, 1.470609]}\nB: {'rotation matrix': [[0.877414, -0.186393, 0.442044], [-0.476234, -0.227312, 0.84943], [-0.057846, -0.955818, -0.288213]], 'translation vector': [1.033287, 1.302455, 1.466311]}\nC: {'rotation matrix': [[0.999991817168325, 0.002932358485082305, -0.002640226065149888], [-0.0029080462382595554, 0.9999549882789169, 0.009034252562416854], [0.0026665546966547606, -0.009026772459937056, 0.9999556430330881]], 'translation vector': [0.005120345387922942, -0.0015772155749180783, 0.009569803755594242]}\nD: {'rotation matrix': [[0.878466, -0.185862, 0.440175], [-0.473882, -0.221089, 0.852382], [-0.061107, -0.957379, -0.282296]], 'translation vector': [1.023458, 1.295782, 1.469602]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.306256, 0.214312, -0.927512], [0.951867, -0.081783, 0.295401], [-0.012547, -0.973336, -0.229043]], 'translation vector': [3.740017, 1.664374, 1.453227]}\nB: {'rotation matrix': [[0.304194, 0.215718, -0.927864], [0.952563, -0.078625, 0.294012], [-0.009529, -0.973285, -0.229402]], 'translation vector': [3.747529, 1.6658, 1.453625]}\nC: {'rotation matrix': [[0.9998537516460376, 0.013008935130727867, -0.011104836010819949], [-0.013009085999586367, 0.9999155647701683, 0.00015274967336381323], [0.011105421497037882, -8.408899546142835e-06, 0.9999381123179228]], 'translation vector': [0.00936290533716333, 0.003069967953460928, -0.010760020039624951]}\nD: {'rotation matrix': [[0.309341, 0.212762, -0.926844], [0.950827, -0.084926, 0.297851], [-0.015342, -0.973406, -0.228571]], 'translation vector': [3.731516, 1.660707, 1.454311]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_165_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_165_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_165_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_165_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.306256, 0.214312, -0.927512], [0.951867, -0.081783, 0.295401], [-0.012547, -0.973336, -0.229043]], 'translation vector': [3.740017, 1.664374, 1.453227]}\nB: {'rotation matrix': [[0.304194, 0.215718, -0.927864], [0.952563, -0.078625, 0.294012], [-0.009529, -0.973285, -0.229402]], 'translation vector': [3.747529, 1.6658, 1.453625]}\nC: {'rotation matrix': [[0.9998537516460376, 0.013008935130727867, -0.011104836010819949], [-0.013009085999586367, 0.9999155647701683, 0.00015274967336381323], [0.011105421497037882, -8.408899546142835e-06, 0.9999381123179228]], 'translation vector': [0.00936290533716333, 0.003069967953460928, -0.010760020039624951]}\nD: {'rotation matrix': [[0.309341, 0.212762, -0.926844], [0.950827, -0.084926, 0.297851], [-0.015342, -0.973406, -0.228571]], 'translation vector': [3.731516, 1.660707, 1.454311]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.226506, -0.721874, 0.653906], [-0.969583, -0.103174, 0.221955], [-0.092757, -0.68429, -0.723287]], 'translation vector': [2.104302, 2.429349, 1.38499]}\nB: {'rotation matrix': [[0.223513, -0.721637, 0.655197], [-0.970242, -0.100499, 0.220298], [-0.093129, -0.684938, -0.722625]], 'translation vector': [2.105446, 2.427759, 1.384995]}\nC: {'rotation matrix': [[0.22885, -0.719341, 0.655878], [-0.96905, -0.104271, 0.223761], [-0.092571, -0.686787, -0.72094]], 'translation vector': [2.102429, 2.429695, 1.385047]}\nD: {'rotation matrix': [[0.9999909282503874, 0.0026675007233110163, 0.003038726855301391], [-0.0026681297586413737, 0.999996473984971, 2.7861207662840692e-05], [-0.0030386998838646344, -3.5943923369286046e-05, 0.9999953589686927]], 'translation vector': [0.002364456746013932, -0.0010737235666011813, -0.00037719790048196256]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_166_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_166_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_166_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_166_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.226506, -0.721874, 0.653906], [-0.969583, -0.103174, 0.221955], [-0.092757, -0.68429, -0.723287]], 'translation vector': [2.104302, 2.429349, 1.38499]}\nB: {'rotation matrix': [[0.223513, -0.721637, 0.655197], [-0.970242, -0.100499, 0.220298], [-0.093129, -0.684938, -0.722625]], 'translation vector': [2.105446, 2.427759, 1.384995]}\nC: {'rotation matrix': [[0.22885, -0.719341, 0.655878], [-0.96905, -0.104271, 0.223761], [-0.092571, -0.686787, -0.72094]], 'translation vector': [2.102429, 2.429695, 1.385047]}\nD: {'rotation matrix': [[0.9999909282503874, 0.0026675007233110163, 0.003038726855301391], [-0.0026681297586413737, 0.999996473984971, 2.7861207662840692e-05], [-0.0030386998838646344, -3.5943923369286046e-05, 0.9999953589686927]], 'translation vector': [0.002364456746013932, -0.0010737235666011813, -0.00037719790048196256]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.711418, -0.467017, 0.525147], [-0.700604, 0.529926, -0.477841], [-0.055129, -0.707865, -0.704193]], 'translation vector': [2.529564, 4.393072, 1.526695]}\nB: {'rotation matrix': [[0.9999966648975326, -0.0024113488419032422, -0.0008172418163523504], [0.0024114327512952905, 0.9999969977952643, 0.0006432367723817748], [0.0008165042296219522, -0.0006466099152102373, 0.9999990767691678]], 'translation vector': [-0.006437670952323948, -0.005367763877482813, 0.00013241538331776326]}\nC: {'rotation matrix': [[-0.711906, -0.467075, 0.524433], [-0.700166, 0.529878, -0.478536], [-0.054374, -0.707863, -0.704254]], 'translation vector': [2.530244, 4.39346, 1.526741]}\nD: {'rotation matrix': [[-0.711605, -0.467107, 0.524814], [-0.700478, 0.529425, -0.47858], [-0.054301, -0.708181, -0.70394]], 'translation vector': [2.529967, 4.393585, 1.525543]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_167_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_167_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_167_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_167_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.711418, -0.467017, 0.525147], [-0.700604, 0.529926, -0.477841], [-0.055129, -0.707865, -0.704193]], 'translation vector': [2.529564, 4.393072, 1.526695]}\nB: {'rotation matrix': [[0.9999966648975326, -0.0024113488419032422, -0.0008172418163523504], [0.0024114327512952905, 0.9999969977952643, 0.0006432367723817748], [0.0008165042296219522, -0.0006466099152102373, 0.9999990767691678]], 'translation vector': [-0.006437670952323948, -0.005367763877482813, 0.00013241538331776326]}\nC: {'rotation matrix': [[-0.711906, -0.467075, 0.524433], [-0.700166, 0.529878, -0.478536], [-0.054374, -0.707863, -0.704254]], 'translation vector': [2.530244, 4.39346, 1.526741]}\nD: {'rotation matrix': [[-0.711605, -0.467107, 0.524814], [-0.700478, 0.529425, -0.47858], [-0.054301, -0.708181, -0.70394]], 'translation vector': [2.529967, 4.393585, 1.525543]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.61412, -0.406634, 0.676392], [-0.788751, 0.286898, -0.543656], [0.027014, -0.867374, -0.496922]], 'translation vector': [1.884445, 2.364432, 1.389567]}\nB: {'rotation matrix': [[0.9999800934681147, 0.006249341845585854, -0.0009789493583592457], [-0.006247228158252656, 0.9999797870897016, 0.0016201321535261565], [0.0009895210241494333, -0.0016143397944822075, 0.9999982198013821]], 'translation vector': [-0.0069672096971418185, 0.0007707556249330061, 0.0022308491982188094]}\nC: {'rotation matrix': [[-0.614991, -0.407345, 0.675171], [-0.788025, 0.286731, -0.544795], [0.028327, -0.867096, -0.497335]], 'translation vector': [1.885989, 2.365962, 1.389016]}\nD: {'rotation matrix': [[-0.615135, -0.40626, 0.675693], [-0.787872, 0.284761, -0.546048], [0.029426, -0.868253, -0.495248]], 'translation vector': [1.88807, 2.366622, 1.388041]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_168_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_168_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_168_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_168_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.61412, -0.406634, 0.676392], [-0.788751, 0.286898, -0.543656], [0.027014, -0.867374, -0.496922]], 'translation vector': [1.884445, 2.364432, 1.389567]}\nB: {'rotation matrix': [[0.9999800934681147, 0.006249341845585854, -0.0009789493583592457], [-0.006247228158252656, 0.9999797870897016, 0.0016201321535261565], [0.0009895210241494333, -0.0016143397944822075, 0.9999982198013821]], 'translation vector': [-0.0069672096971418185, 0.0007707556249330061, 0.0022308491982188094]}\nC: {'rotation matrix': [[-0.614991, -0.407345, 0.675171], [-0.788025, 0.286731, -0.544795], [0.028327, -0.867096, -0.497335]], 'translation vector': [1.885989, 2.365962, 1.389016]}\nD: {'rotation matrix': [[-0.615135, -0.40626, 0.675693], [-0.787872, 0.284761, -0.546048], [0.029426, -0.868253, -0.495248]], 'translation vector': [1.88807, 2.366622, 1.388041]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999912398243648, -0.003460941321399837, -0.0022122658857095193], [0.003455741372162686, 0.9999910905464573, -0.0022383864983849893], [0.0022193515549835062, 0.0022307044509074837, 0.999995073124285]], 'translation vector': [-0.001957679288641462, -0.0024920290268601875, -0.000907578453192226]}\nB: {'rotation matrix': [[0.67484, -0.325973, 0.662067], [-0.73754, -0.328352, 0.590102], [0.025034, -0.886525, -0.462003]], 'translation vector': [2.869569, 2.417867, 1.545271]}\nC: {'rotation matrix': [[0.67798, -0.325694, 0.658989], [-0.734824, -0.323965, 0.595886], [0.019413, -0.88824, -0.45897]], 'translation vector': [2.868894, 2.415756, 1.54509]}\nD: {'rotation matrix': [[0.682626, -0.324357, 0.654839], [-0.73069, -0.316101, 0.605122], [0.010719, -0.891556, -0.452783]], 'translation vector': [2.86653, 2.411599, 1.544608]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_169_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_169_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_169_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_169_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999912398243648, -0.003460941321399837, -0.0022122658857095193], [0.003455741372162686, 0.9999910905464573, -0.0022383864983849893], [0.0022193515549835062, 0.0022307044509074837, 0.999995073124285]], 'translation vector': [-0.001957679288641462, -0.0024920290268601875, -0.000907578453192226]}\nB: {'rotation matrix': [[0.67484, -0.325973, 0.662067], [-0.73754, -0.328352, 0.590102], [0.025034, -0.886525, -0.462003]], 'translation vector': [2.869569, 2.417867, 1.545271]}\nC: {'rotation matrix': [[0.67798, -0.325694, 0.658989], [-0.734824, -0.323965, 0.595886], [0.019413, -0.88824, -0.45897]], 'translation vector': [2.868894, 2.415756, 1.54509]}\nD: {'rotation matrix': [[0.682626, -0.324357, 0.654839], [-0.73069, -0.316101, 0.605122], [0.010719, -0.891556, -0.452783]], 'translation vector': [2.86653, 2.411599, 1.544608]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.999502, 0.00746, 0.030678], [-0.028996, 0.167572, -0.985433], [-0.012492, -0.985832, -0.167272]], 'translation vector': [6.682728, 5.426456, 1.759702]}\nB: {'rotation matrix': [[-0.999516, 0.005588, 0.030613], [-0.029207, 0.171126, -0.984816], [-0.010741, -0.985233, -0.17088]], 'translation vector': [6.687027, 5.423337, 1.762554]}\nC: {'rotation matrix': [[-0.999427, 0.005452, 0.0334], [-0.031967, 0.171859, -0.984603], [-0.011109, -0.985106, -0.171586]], 'translation vector': [6.682628, 5.424977, 1.756356]}\nD: {'rotation matrix': [[0.9999959031714618, 0.0006283915085459037, 0.0029514431388234703], [-0.0006213832896176772, 0.9999971711296258, -0.0021323447476004893], [-0.0029538525457832297, 0.002130012321803009, 0.9999931612377413]], 'translation vector': [-0.006207505850969852, 0.015496225089857818, -0.006213786764486251]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_170_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_170_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_170_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_170_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.999502, 0.00746, 0.030678], [-0.028996, 0.167572, -0.985433], [-0.012492, -0.985832, -0.167272]], 'translation vector': [6.682728, 5.426456, 1.759702]}\nB: {'rotation matrix': [[-0.999516, 0.005588, 0.030613], [-0.029207, 0.171126, -0.984816], [-0.010741, -0.985233, -0.17088]], 'translation vector': [6.687027, 5.423337, 1.762554]}\nC: {'rotation matrix': [[-0.999427, 0.005452, 0.0334], [-0.031967, 0.171859, -0.984603], [-0.011109, -0.985106, -0.171586]], 'translation vector': [6.682628, 5.424977, 1.756356]}\nD: {'rotation matrix': [[0.9999959031714618, 0.0006283915085459037, 0.0029514431388234703], [-0.0006213832896176772, 0.9999971711296258, -0.0021323447476004893], [-0.0029538525457832297, 0.002130012321803009, 0.9999931612377413]], 'translation vector': [-0.006207505850969852, 0.015496225089857818, -0.006213786764486251]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.106477, -0.473799, 0.874173], [-0.992913, 0.097339, -0.068183], [-0.052786, -0.875237, -0.480805]], 'translation vector': [4.553204, 3.149855, 1.246823]}\nB: {'rotation matrix': [[-0.115243, -0.46998, 0.875122], [-0.991961, 0.100818, -0.076485], [-0.052282, -0.876901, -0.47782]], 'translation vector': [4.553743, 3.152171, 1.246409]}\nC: {'rotation matrix': [[0.999828950585133, 0.0011190525701963586, -0.018454829607259946], [-0.001416468295998283, 0.9998678811169985, -0.016145155593255522], [0.018434708990961175, 0.016168544891222898, 0.9996994385524549]], 'translation vector': [0.002077144261483088, 0.01271199054625649, -0.0002353155159546816]}\nD: {'rotation matrix': [[-0.120252, -0.468652, 0.87516], [-0.991418, 0.102239, -0.081478], [-0.051291, -0.877447, -0.476924]], 'translation vector': [4.555783, 3.154248, 1.246329]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_171_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_171_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_171_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_171_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.106477, -0.473799, 0.874173], [-0.992913, 0.097339, -0.068183], [-0.052786, -0.875237, -0.480805]], 'translation vector': [4.553204, 3.149855, 1.246823]}\nB: {'rotation matrix': [[-0.115243, -0.46998, 0.875122], [-0.991961, 0.100818, -0.076485], [-0.052282, -0.876901, -0.47782]], 'translation vector': [4.553743, 3.152171, 1.246409]}\nC: {'rotation matrix': [[0.999828950585133, 0.0011190525701963586, -0.018454829607259946], [-0.001416468295998283, 0.9998678811169985, -0.016145155593255522], [0.018434708990961175, 0.016168544891222898, 0.9996994385524549]], 'translation vector': [0.002077144261483088, 0.01271199054625649, -0.0002353155159546816]}\nD: {'rotation matrix': [[-0.120252, -0.468652, 0.87516], [-0.991418, 0.102239, -0.081478], [-0.051291, -0.877447, -0.476924]], 'translation vector': [4.555783, 3.154248, 1.246329]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.927045, 0.223636, -0.300956], [0.364198, 0.346229, -0.864573], [-0.08915, -0.911105, -0.402418]], 'translation vector': [7.648557, 2.747808, 1.440051]}\nB: {'rotation matrix': [[0.9999939386175598, 0.0009488285827030999, 0.0032872470438982896], [-0.0009337892397183689, 0.9999879134504162, -0.004824212466184848], [-0.0032917475553783573, 0.004820430801235839, 0.9999833362205853]], 'translation vector': [-0.000493455857793812, -0.002698981007177137, 0.0012657934234763246]}\nC: {'rotation matrix': [[-0.9261, 0.223085, -0.304257], [0.366658, 0.34218, -0.865144], [-0.08889, -0.912768, -0.398689]], 'translation vector': [7.650569, 2.747621, 1.441708]}\nD: {'rotation matrix': [[-0.927432, 0.22366, -0.299744], [0.363522, 0.350792, -0.863016], [-0.087874, -0.909352, -0.406641]], 'translation vector': [7.650677, 2.747929, 1.439487]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_172_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_172_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_172_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_172_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.927045, 0.223636, -0.300956], [0.364198, 0.346229, -0.864573], [-0.08915, -0.911105, -0.402418]], 'translation vector': [7.648557, 2.747808, 1.440051]}\nB: {'rotation matrix': [[0.9999939386175598, 0.0009488285827030999, 0.0032872470438982896], [-0.0009337892397183689, 0.9999879134504162, -0.004824212466184848], [-0.0032917475553783573, 0.004820430801235839, 0.9999833362205853]], 'translation vector': [-0.000493455857793812, -0.002698981007177137, 0.0012657934234763246]}\nC: {'rotation matrix': [[-0.9261, 0.223085, -0.304257], [0.366658, 0.34218, -0.865144], [-0.08889, -0.912768, -0.398689]], 'translation vector': [7.650569, 2.747621, 1.441708]}\nD: {'rotation matrix': [[-0.927432, 0.22366, -0.299744], [0.363522, 0.350792, -0.863016], [-0.087874, -0.909352, -0.406641]], 'translation vector': [7.650677, 2.747929, 1.439487]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.598936, 0.355502, -0.717562], [0.800003, -0.305531, 0.516379], [-0.035664, -0.883329, -0.467396]], 'translation vector': [5.964795, 1.444893, 1.32602]}\nB: {'rotation matrix': [[0.600188, 0.357296, -0.715622], [0.799089, -0.307102, 0.516861], [-0.035096, -0.882059, -0.46983]], 'translation vector': [5.950611, 1.450679, 1.325211]}\nC: {'rotation matrix': [[0.9999898156973296, -0.003952939496578174, 0.0024431521094752146], [0.003942856258073881, 0.9999843026055344, 0.004096575088743528], [-0.0024598279169757275, -0.004085831486072852, 0.9999883270401599]], 'translation vector': [-0.0051631563465806, -0.010227260523923531, 0.01366119668418353]}\nD: {'rotation matrix': [[0.593595, 0.358896, -0.720305], [0.803944, -0.304849, 0.510628], [-0.036322, -0.882191, -0.469489]], 'translation vector': [5.978991, 1.441471, 1.326102]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_173_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_173_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_173_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_173_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.598936, 0.355502, -0.717562], [0.800003, -0.305531, 0.516379], [-0.035664, -0.883329, -0.467396]], 'translation vector': [5.964795, 1.444893, 1.32602]}\nB: {'rotation matrix': [[0.600188, 0.357296, -0.715622], [0.799089, -0.307102, 0.516861], [-0.035096, -0.882059, -0.46983]], 'translation vector': [5.950611, 1.450679, 1.325211]}\nC: {'rotation matrix': [[0.9999898156973296, -0.003952939496578174, 0.0024431521094752146], [0.003942856258073881, 0.9999843026055344, 0.004096575088743528], [-0.0024598279169757275, -0.004085831486072852, 0.9999883270401599]], 'translation vector': [-0.0051631563465806, -0.010227260523923531, 0.01366119668418353]}\nD: {'rotation matrix': [[0.593595, 0.358896, -0.720305], [0.803944, -0.304849, 0.510628], [-0.036322, -0.882191, -0.469489]], 'translation vector': [5.978991, 1.441471, 1.326102]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.983068, 0.048576, -0.176687], [0.181735, -0.381917, 0.906152], [-0.023462, -0.922919, -0.384278]], 'translation vector': [2.212073, 3.484547, 1.465708]}\nB: {'rotation matrix': [[0.982686, 0.047982, -0.178958], [0.183606, -0.38172, 0.905858], [-0.024847, -0.923032, -0.38392]], 'translation vector': [2.212621, 3.48432, 1.466163]}\nC: {'rotation matrix': [[0.983078, 0.050132, -0.176192], [0.181887, -0.381466, 0.906312], [-0.021776, -0.923023, -0.384129]], 'translation vector': [2.213536, 3.486831, 1.465259]}\nD: {'rotation matrix': [[0.9999889301916728, 0.0015926412893515843, 0.004480728220099604], [-0.001621568987345314, 0.9999775972099906, 0.006504872079739442], [-0.004470952780905997, -0.0065123882198827605, 0.9999681868018854]], 'translation vector': [-0.002104900488902217, -0.0034679151915213424, 0.0012659901948626207]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_174_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_174_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_174_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_174_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.983068, 0.048576, -0.176687], [0.181735, -0.381917, 0.906152], [-0.023462, -0.922919, -0.384278]], 'translation vector': [2.212073, 3.484547, 1.465708]}\nB: {'rotation matrix': [[0.982686, 0.047982, -0.178958], [0.183606, -0.38172, 0.905858], [-0.024847, -0.923032, -0.38392]], 'translation vector': [2.212621, 3.48432, 1.466163]}\nC: {'rotation matrix': [[0.983078, 0.050132, -0.176192], [0.181887, -0.381466, 0.906312], [-0.021776, -0.923023, -0.384129]], 'translation vector': [2.213536, 3.486831, 1.465259]}\nD: {'rotation matrix': [[0.9999889301916728, 0.0015926412893515843, 0.004480728220099604], [-0.001621568987345314, 0.9999775972099906, 0.006504872079739442], [-0.004470952780905997, -0.0065123882198827605, 0.9999681868018854]], 'translation vector': [-0.002104900488902217, -0.0034679151915213424, 0.0012659901948626207]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999643490106183, 0.005007713450141965, -0.006820932250517381], [-0.004994218544548531, 0.9999851347119364, 0.0020857596614642536], [0.0068306095153302035, -0.0020519868188517945, 0.9999742349784666]], 'translation vector': [-0.002699516524657053, 0.0005464771955957654, -0.0009563459127281959]}\nB: {'rotation matrix': [[0.549558, 0.430394, -0.716064], [0.833614, -0.2256, 0.504176], [0.05545, -0.873994, -0.482762]], 'translation vector': [3.109701, 1.26111, 1.347453]}\nC: {'rotation matrix': [[0.545357, 0.429527, -0.719787], [0.836166, -0.218941, 0.502882], [0.05841, -0.876111, -0.478557]], 'translation vector': [3.10956, 1.258833, 1.347276]}\nD: {'rotation matrix': [[0.548441, 0.430496, -0.716859], [0.834301, -0.22414, 0.503689], [0.056159, -0.874319, -0.482091]], 'translation vector': [3.109132, 1.259955, 1.347698]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_175_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_175_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_175_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_175_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999643490106183, 0.005007713450141965, -0.006820932250517381], [-0.004994218544548531, 0.9999851347119364, 0.0020857596614642536], [0.0068306095153302035, -0.0020519868188517945, 0.9999742349784666]], 'translation vector': [-0.002699516524657053, 0.0005464771955957654, -0.0009563459127281959]}\nB: {'rotation matrix': [[0.549558, 0.430394, -0.716064], [0.833614, -0.2256, 0.504176], [0.05545, -0.873994, -0.482762]], 'translation vector': [3.109701, 1.26111, 1.347453]}\nC: {'rotation matrix': [[0.545357, 0.429527, -0.719787], [0.836166, -0.218941, 0.502882], [0.05841, -0.876111, -0.478557]], 'translation vector': [3.10956, 1.258833, 1.347276]}\nD: {'rotation matrix': [[0.548441, 0.430496, -0.716859], [0.834301, -0.22414, 0.503689], [0.056159, -0.874319, -0.482091]], 'translation vector': [3.109132, 1.259955, 1.347698]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.9999664473886144, -4.097872539031611e-05, -0.00815329503789991], [4.450303663576986e-05, 1.0000000556925084, 0.0005279321342564721], [0.008153564277134814, -0.0005283064017830043, 0.99996718864845]], 'translation vector': [0.0006960777394775519, 0.0030013724924222718, -0.001027289568414247]}\nB: {'rotation matrix': [[-0.825443, 0.242757, -0.509621], [0.56437, 0.373207, -0.736345], [0.011442, -0.895425, -0.445066]], 'translation vector': [4.848658, 2.610627, 1.449985]}\nC: {'rotation matrix': [[-0.825701, 0.242217, -0.50946], [0.563992, 0.37286, -0.73681], [0.01149, -0.895716, -0.444479]], 'translation vector': [4.848603, 2.611202, 1.449781]}\nD: {'rotation matrix': [[-0.825281, 0.242861, -0.509834], [0.5646, 0.373686, -0.735925], [0.011791, -0.895197, -0.445515]], 'translation vector': [4.848519, 2.6109, 1.44995]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_176_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_176_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_176_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_176_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.9999664473886144, -4.097872539031611e-05, -0.00815329503789991], [4.450303663576986e-05, 1.0000000556925084, 0.0005279321342564721], [0.008153564277134814, -0.0005283064017830043, 0.99996718864845]], 'translation vector': [0.0006960777394775519, 0.0030013724924222718, -0.001027289568414247]}\nB: {'rotation matrix': [[-0.825443, 0.242757, -0.509621], [0.56437, 0.373207, -0.736345], [0.011442, -0.895425, -0.445066]], 'translation vector': [4.848658, 2.610627, 1.449985]}\nC: {'rotation matrix': [[-0.825701, 0.242217, -0.50946], [0.563992, 0.37286, -0.73681], [0.01149, -0.895716, -0.444479]], 'translation vector': [4.848603, 2.611202, 1.449781]}\nD: {'rotation matrix': [[-0.825281, 0.242861, -0.509834], [0.5646, 0.373686, -0.735925], [0.011791, -0.895197, -0.445515]], 'translation vector': [4.848519, 2.6109, 1.44995]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.820406, -0.123018, 0.558391], [-0.564425, -0.330389, 0.756484], [0.091425, -0.935794, -0.340487]], 'translation vector': [1.795617, 2.461673, 1.379824]}\nB: {'rotation matrix': [[0.820181, -0.122779, 0.558774], [-0.564668, -0.330702, 0.756166], [0.091946, -0.935714, -0.340565]], 'translation vector': [1.795446, 2.463577, 1.379349]}\nC: {'rotation matrix': [[0.9999965570678159, 0.002414373935934805, -0.0013910970690502594], [-0.0024092149366839086, 0.9999893972371463, 0.004094256181755705], [0.0014001974871553952, -0.0040912377586492955, 0.9999902946910975]], 'translation vector': [0.0015616232476808878, 0.0015124074702654866, -0.0024993421767338653]}\nD: {'rotation matrix': [[0.81953, -0.122236, 0.559847], [-0.565374, -0.331709, 0.755196], [0.093395, -0.935429, -0.340955]], 'translation vector': [1.794011, 2.466618, 1.378419]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_177_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_177_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_177_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_177_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.820406, -0.123018, 0.558391], [-0.564425, -0.330389, 0.756484], [0.091425, -0.935794, -0.340487]], 'translation vector': [1.795617, 2.461673, 1.379824]}\nB: {'rotation matrix': [[0.820181, -0.122779, 0.558774], [-0.564668, -0.330702, 0.756166], [0.091946, -0.935714, -0.340565]], 'translation vector': [1.795446, 2.463577, 1.379349]}\nC: {'rotation matrix': [[0.9999965570678159, 0.002414373935934805, -0.0013910970690502594], [-0.0024092149366839086, 0.9999893972371463, 0.004094256181755705], [0.0014001974871553952, -0.0040912377586492955, 0.9999902946910975]], 'translation vector': [0.0015616232476808878, 0.0015124074702654866, -0.0024993421767338653]}\nD: {'rotation matrix': [[0.81953, -0.122236, 0.559847], [-0.565374, -0.331709, 0.755196], [0.093395, -0.935429, -0.340955]], 'translation vector': [1.794011, 2.466618, 1.378419]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.802386, 0.058378, -0.593943], [0.596799, 0.07378, -0.798992], [-0.002822, -0.995564, -0.09404]], 'translation vector': [2.583445, 4.00863, 1.432702]}\nB: {'rotation matrix': [[-0.80243, 0.05764, -0.593956], [0.596739, 0.072442, -0.799159], [-0.003036, -0.995706, -0.092526]], 'translation vector': [2.583423, 4.00901, 1.432499]}\nC: {'rotation matrix': [[-0.802147, 0.057229, -0.594377], [0.597116, 0.07095, -0.799012], [-0.003555, -0.995837, -0.091085]], 'translation vector': [2.584156, 4.008181, 1.433043]}\nD: {'rotation matrix': [[0.9999994783035012, 0.00041051927851740725, -0.0005862593604151125], [-0.00040931866521127987, 0.9999994989245719, 0.001478878293238192], [0.0005866244600769685, -0.00147872503352613, 0.9999988017009569]], 'translation vector': [0.00041364848275698973, -0.004321265289284559, -0.00018345117394513721]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_178_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_178_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_178_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_178_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.802386, 0.058378, -0.593943], [0.596799, 0.07378, -0.798992], [-0.002822, -0.995564, -0.09404]], 'translation vector': [2.583445, 4.00863, 1.432702]}\nB: {'rotation matrix': [[-0.80243, 0.05764, -0.593956], [0.596739, 0.072442, -0.799159], [-0.003036, -0.995706, -0.092526]], 'translation vector': [2.583423, 4.00901, 1.432499]}\nC: {'rotation matrix': [[-0.802147, 0.057229, -0.594377], [0.597116, 0.07095, -0.799012], [-0.003555, -0.995837, -0.091085]], 'translation vector': [2.584156, 4.008181, 1.433043]}\nD: {'rotation matrix': [[0.9999994783035012, 0.00041051927851740725, -0.0005862593604151125], [-0.00040931866521127987, 0.9999994989245719, 0.001478878293238192], [0.0005866244600769685, -0.00147872503352613, 0.9999988017009569]], 'translation vector': [0.00041364848275698973, -0.004321265289284559, -0.00018345117394513721]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.992585, -0.064418, 0.103079], [-0.120896, -0.435169, 0.892195], [-0.012617, -0.898041, -0.43973]], 'translation vector': [3.286474, 2.568909, 1.509796]}\nB: {'rotation matrix': [[0.992385, -0.067834, 0.102811], [-0.122233, -0.439432, 0.889921], [-0.015188, -0.895711, -0.444377]], 'translation vector': [3.289696, 2.56831, 1.509591]}\nC: {'rotation matrix': [[0.9999807964258803, 0.0038860910275422844, -0.004806691946462499], [-0.003909951367268014, 0.9999796651555257, -0.005033098084012006], [0.004787399082777756, 0.005051536745205018, 0.999976147031262]], 'translation vector': [-0.001554233624264434, -0.002644430194159053, -0.0006288644424583545]}\nD: {'rotation matrix': [[0.992758, -0.062357, 0.102681], [-0.119576, -0.430727, 0.894525], [-0.011552, -0.900325, -0.435064]], 'translation vector': [3.283188, 2.568117, 1.510042]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_179_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_179_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_179_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_179_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.992585, -0.064418, 0.103079], [-0.120896, -0.435169, 0.892195], [-0.012617, -0.898041, -0.43973]], 'translation vector': [3.286474, 2.568909, 1.509796]}\nB: {'rotation matrix': [[0.992385, -0.067834, 0.102811], [-0.122233, -0.439432, 0.889921], [-0.015188, -0.895711, -0.444377]], 'translation vector': [3.289696, 2.56831, 1.509591]}\nC: {'rotation matrix': [[0.9999807964258803, 0.0038860910275422844, -0.004806691946462499], [-0.003909951367268014, 0.9999796651555257, -0.005033098084012006], [0.004787399082777756, 0.005051536745205018, 0.999976147031262]], 'translation vector': [-0.001554233624264434, -0.002644430194159053, -0.0006288644424583545]}\nD: {'rotation matrix': [[0.992758, -0.062357, 0.102681], [-0.119576, -0.430727, 0.894525], [-0.011552, -0.900325, -0.435064]], 'translation vector': [3.283188, 2.568117, 1.510042]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.422089, -0.487348, 0.764417], [-0.906483, 0.237506, -0.349114], [-0.011414, -0.840287, -0.542021]], 'translation vector': [1.410195, 1.210537, 1.389714]}\nB: {'rotation matrix': [[-0.429509, -0.487246, 0.760337], [-0.903019, 0.239935, -0.356353], [-0.0088, -0.839656, -0.543047]], 'translation vector': [1.408282, 1.210133, 1.390728]}\nC: {'rotation matrix': [[0.9999401042348363, 0.0004722608940826321, -0.010894896595028812], [-0.0004891614331012371, 0.9999986952391333, -0.0015551949611865112], [0.010893168247730851, 0.0015589513371194136, 0.9999394642924967]], 'translation vector': [-0.002554071705175298, -0.000426511698759402, -0.0005788025297613075]}\nD: {'rotation matrix': [[-0.426247, -0.487547, 0.761978], [-0.904552, 0.238954, -0.353109], [-0.00992, -0.839761, -0.542865]], 'translation vector': [1.409365, 1.209862, 1.390977]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_180_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_180_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_180_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_180_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.422089, -0.487348, 0.764417], [-0.906483, 0.237506, -0.349114], [-0.011414, -0.840287, -0.542021]], 'translation vector': [1.410195, 1.210537, 1.389714]}\nB: {'rotation matrix': [[-0.429509, -0.487246, 0.760337], [-0.903019, 0.239935, -0.356353], [-0.0088, -0.839656, -0.543047]], 'translation vector': [1.408282, 1.210133, 1.390728]}\nC: {'rotation matrix': [[0.9999401042348363, 0.0004722608940826321, -0.010894896595028812], [-0.0004891614331012371, 0.9999986952391333, -0.0015551949611865112], [0.010893168247730851, 0.0015589513371194136, 0.9999394642924967]], 'translation vector': [-0.002554071705175298, -0.000426511698759402, -0.0005788025297613075]}\nD: {'rotation matrix': [[-0.426247, -0.487547, 0.761978], [-0.904552, 0.238954, -0.353109], [-0.00992, -0.839761, -0.542865]], 'translation vector': [1.409365, 1.209862, 1.390977]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.471816, -0.325425, 0.819444], [-0.872235, -0.30807, 0.379868], [0.128827, -0.893975, -0.429199]], 'translation vector': [4.769558, 1.138603, 1.289356]}\nB: {'rotation matrix': [[0.9997418114978017, 0.011895326282976197, -0.019384512225947795], [-0.0117477819809795, 0.9999024052361882, 0.007665229365566708], [0.019474149960248398, -0.007435647706830853, 0.99978314162453]], 'translation vector': [0.0024302909823366026, 0.0025910713671302155, 0.004134808496160325]}\nC: {'rotation matrix': [[0.463265, -0.315518, 0.828151], [-0.87672, -0.299624, 0.376281], [0.129411, -0.900374, -0.415426]], 'translation vector': [4.764074, 1.139958, 1.290116]}\nD: {'rotation matrix': [[0.466198, -0.319071, 0.825139], [-0.875193, -0.302567, 0.377479], [0.129217, -0.898136, -0.420304]], 'translation vector': [4.766454, 1.138272, 1.288707]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_181_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_181_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_181_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_181_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.471816, -0.325425, 0.819444], [-0.872235, -0.30807, 0.379868], [0.128827, -0.893975, -0.429199]], 'translation vector': [4.769558, 1.138603, 1.289356]}\nB: {'rotation matrix': [[0.9997418114978017, 0.011895326282976197, -0.019384512225947795], [-0.0117477819809795, 0.9999024052361882, 0.007665229365566708], [0.019474149960248398, -0.007435647706830853, 0.99978314162453]], 'translation vector': [0.0024302909823366026, 0.0025910713671302155, 0.004134808496160325]}\nC: {'rotation matrix': [[0.463265, -0.315518, 0.828151], [-0.87672, -0.299624, 0.376281], [0.129411, -0.900374, -0.415426]], 'translation vector': [4.764074, 1.139958, 1.290116]}\nD: {'rotation matrix': [[0.466198, -0.319071, 0.825139], [-0.875193, -0.302567, 0.377479], [0.129217, -0.898136, -0.420304]], 'translation vector': [4.766454, 1.138272, 1.288707]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.782202, 0.158811, -0.602444], [0.623011, 0.192986, -0.758033], [-0.004121, -0.968264, -0.249895]], 'translation vector': [5.112607, 3.166242, 1.386639]}\nB: {'rotation matrix': [[-0.778966, 0.157543, -0.606954], [0.627051, 0.189047, -0.75569], [-0.00431, -0.969248, -0.246049]], 'translation vector': [5.115294, 3.157473, 1.383296]}\nC: {'rotation matrix': [[-0.779462, 0.157557, -0.606313], [0.62644, 0.190695, -0.755783], [-0.003458, -0.968923, -0.247339]], 'translation vector': [5.116126, 3.162086, 1.384797]}\nD: {'rotation matrix': [[0.9999744617727225, -0.0001956738250342968, -0.007090775323277213], [0.00023566199396404743, 0.9999840487337286, 0.005570125228072977], [0.007090207625955035, -0.005572126334409686, 0.9999593181215198]], 'translation vector': [0.001060093909475146, -0.0011413036070948707, -0.0054511706559239315]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_182_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_182_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_182_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_182_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.782202, 0.158811, -0.602444], [0.623011, 0.192986, -0.758033], [-0.004121, -0.968264, -0.249895]], 'translation vector': [5.112607, 3.166242, 1.386639]}\nB: {'rotation matrix': [[-0.778966, 0.157543, -0.606954], [0.627051, 0.189047, -0.75569], [-0.00431, -0.969248, -0.246049]], 'translation vector': [5.115294, 3.157473, 1.383296]}\nC: {'rotation matrix': [[-0.779462, 0.157557, -0.606313], [0.62644, 0.190695, -0.755783], [-0.003458, -0.968923, -0.247339]], 'translation vector': [5.116126, 3.162086, 1.384797]}\nD: {'rotation matrix': [[0.9999744617727225, -0.0001956738250342968, -0.007090775323277213], [0.00023566199396404743, 0.9999840487337286, 0.005570125228072977], [0.007090207625955035, -0.005572126334409686, 0.9999593181215198]], 'translation vector': [0.001060093909475146, -0.0011413036070948707, -0.0054511706559239315]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.883351, 0.250777, -0.395983], [0.468408, -0.502792, 0.726495], [-0.016909, -0.827231, -0.561607]], 'translation vector': [3.460753, 1.393703, 1.261616]}\nB: {'rotation matrix': [[0.9999974556820361, 0.0020951454008892073, 0.0005388626757519509], [-0.0020947728022776887, 0.9999969776383335, -0.0008772711173625334], [-0.0005408474142489815, 0.0008761512281933679, 0.9999997549182328]], 'translation vector': [0.002023718546634523, -0.000327483901387704, 0.000534647049254211]}\nC: {'rotation matrix': [[0.882846, 0.250522, -0.397268], [0.469328, -0.502506, 0.726099], [-0.017726, -0.827482, -0.561212]], 'translation vector': [3.46034, 1.393395, 1.261018]}\nD: {'rotation matrix': [[0.883505, 0.250774, -0.395641], [0.468134, -0.50232, 0.726998], [-0.016426, -0.827519, -0.561198]], 'translation vector': [3.461493, 1.393772, 1.262191]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_183_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_183_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_183_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_183_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.883351, 0.250777, -0.395983], [0.468408, -0.502792, 0.726495], [-0.016909, -0.827231, -0.561607]], 'translation vector': [3.460753, 1.393703, 1.261616]}\nB: {'rotation matrix': [[0.9999974556820361, 0.0020951454008892073, 0.0005388626757519509], [-0.0020947728022776887, 0.9999969776383335, -0.0008772711173625334], [-0.0005408474142489815, 0.0008761512281933679, 0.9999997549182328]], 'translation vector': [0.002023718546634523, -0.000327483901387704, 0.000534647049254211]}\nC: {'rotation matrix': [[0.882846, 0.250522, -0.397268], [0.469328, -0.502506, 0.726099], [-0.017726, -0.827482, -0.561212]], 'translation vector': [3.46034, 1.393395, 1.261018]}\nD: {'rotation matrix': [[0.883505, 0.250774, -0.395641], [0.468134, -0.50232, 0.726998], [-0.016426, -0.827519, -0.561198]], 'translation vector': [3.461493, 1.393772, 1.262191]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.082095, -0.690035, 0.719105], [-0.996618, 0.054259, -0.061711], [0.003565, -0.72174, -0.692155]], 'translation vector': [1.142854, 0.964299, 1.384999]}\nB: {'rotation matrix': [[-0.082522, -0.690473, 0.718636], [-0.99657, 0.052619, -0.063881], [0.006294, -0.721442, -0.692446]], 'translation vector': [1.142415, 0.962891, 1.383926]}\nC: {'rotation matrix': [[-0.081714, -0.689876, 0.719301], [-0.996653, 0.054911, -0.060558], [0.00228, -0.721842, -0.692054]], 'translation vector': [1.143872, 0.96595, 1.386324]}\nD: {'rotation matrix': [[0.9999981813065101, -0.0008741599533984917, -0.0018370380112936338], [0.0008726557083801512, 0.9999996139578026, -0.0006456924823724538], [0.0018388517886205992, 0.0006438367932182827, 0.999997821691748]], 'translation vector': [-0.0006029244698710912, 0.002574260386327465, 0.00012150794273751986]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_184_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_184_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_184_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_184_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.082095, -0.690035, 0.719105], [-0.996618, 0.054259, -0.061711], [0.003565, -0.72174, -0.692155]], 'translation vector': [1.142854, 0.964299, 1.384999]}\nB: {'rotation matrix': [[-0.082522, -0.690473, 0.718636], [-0.99657, 0.052619, -0.063881], [0.006294, -0.721442, -0.692446]], 'translation vector': [1.142415, 0.962891, 1.383926]}\nC: {'rotation matrix': [[-0.081714, -0.689876, 0.719301], [-0.996653, 0.054911, -0.060558], [0.00228, -0.721842, -0.692054]], 'translation vector': [1.143872, 0.96595, 1.386324]}\nD: {'rotation matrix': [[0.9999981813065101, -0.0008741599533984917, -0.0018370380112936338], [0.0008726557083801512, 0.9999996139578026, -0.0006456924823724538], [0.0018388517886205992, 0.0006438367932182827, 0.999997821691748]], 'translation vector': [-0.0006029244698710912, 0.002574260386327465, 0.00012150794273751986]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.885696, -0.241091, 0.396758], [-0.463478, 0.409419, -0.785852], [0.027022, -0.879915, -0.474362]], 'translation vector': [3.284311, 2.742399, 1.352773]}\nB: {'rotation matrix': [[-0.887326, -0.240072, 0.393723], [-0.460299, 0.409476, -0.787689], [0.027883, -0.880168, -0.473844]], 'translation vector': [3.284908, 2.737404, 1.354156]}\nC: {'rotation matrix': [[0.9999731444122048, 0.0012103447086951903, -0.007132060128435475], [-0.0011432251198222655, 0.9999548360017081, 0.009399220003853712], [0.007143404275372449, -0.009390869439370925, 0.9999297253861809]], 'translation vector': [-0.004051670502601468, 0.003985677205533222, -0.007748124103307941]}\nD: {'rotation matrix': [[-0.88833, -0.238233, 0.392575], [-0.45841, 0.409739, -0.788653], [0.02703, -0.880545, -0.473192]], 'translation vector': [3.286612, 2.733624, 1.354709]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_185_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_185_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_185_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_185_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.885696, -0.241091, 0.396758], [-0.463478, 0.409419, -0.785852], [0.027022, -0.879915, -0.474362]], 'translation vector': [3.284311, 2.742399, 1.352773]}\nB: {'rotation matrix': [[-0.887326, -0.240072, 0.393723], [-0.460299, 0.409476, -0.787689], [0.027883, -0.880168, -0.473844]], 'translation vector': [3.284908, 2.737404, 1.354156]}\nC: {'rotation matrix': [[0.9999731444122048, 0.0012103447086951903, -0.007132060128435475], [-0.0011432251198222655, 0.9999548360017081, 0.009399220003853712], [0.007143404275372449, -0.009390869439370925, 0.9999297253861809]], 'translation vector': [-0.004051670502601468, 0.003985677205533222, -0.007748124103307941]}\nD: {'rotation matrix': [[-0.88833, -0.238233, 0.392575], [-0.45841, 0.409739, -0.788653], [0.02703, -0.880545, -0.473192]], 'translation vector': [3.286612, 2.733624, 1.354709]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.929629, 0.142082, -0.340003], [0.367757, 0.416138, -0.831615], [0.023331, -0.898132, -0.439106]], 'translation vector': [3.895597, 4.105544, 1.337128]}\nB: {'rotation matrix': [[0.9999978567692223, -0.002080974608083212, 0.0006913520673930054], [0.0020836393175395, 0.9999892180319079, -0.004134789557599625], [-0.0006820179303371059, 0.00413702467971305, 0.9999915554185601]], 'translation vector': [0.0017503586952924977, 0.001995171524919126, -0.0003721348284200232]}\nC: {'rotation matrix': [[-0.930698, 0.142163, -0.337032], [0.365142, 0.415816, -0.832928], [0.021732, -0.898269, -0.438909]], 'translation vector': [3.896934, 4.102128, 1.337288]}\nD: {'rotation matrix': [[-0.927672, 0.142632, -0.34508], [0.372556, 0.415512, -0.82979], [0.02503, -0.898335, -0.438597]], 'translation vector': [3.896674, 4.103256, 1.336071]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_186_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_186_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_186_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_186_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.929629, 0.142082, -0.340003], [0.367757, 0.416138, -0.831615], [0.023331, -0.898132, -0.439106]], 'translation vector': [3.895597, 4.105544, 1.337128]}\nB: {'rotation matrix': [[0.9999978567692223, -0.002080974608083212, 0.0006913520673930054], [0.0020836393175395, 0.9999892180319079, -0.004134789557599625], [-0.0006820179303371059, 0.00413702467971305, 0.9999915554185601]], 'translation vector': [0.0017503586952924977, 0.001995171524919126, -0.0003721348284200232]}\nC: {'rotation matrix': [[-0.930698, 0.142163, -0.337032], [0.365142, 0.415816, -0.832928], [0.021732, -0.898269, -0.438909]], 'translation vector': [3.896934, 4.102128, 1.337288]}\nD: {'rotation matrix': [[-0.927672, 0.142632, -0.34508], [0.372556, 0.415512, -0.82979], [0.02503, -0.898335, -0.438597]], 'translation vector': [3.896674, 4.103256, 1.336071]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.97654, 0.035326, -0.212419], [0.213134, -0.299258, 0.930064], [-0.030712, -0.953518, -0.299767]], 'translation vector': [2.83562, 1.415562, 1.663413]}\nB: {'rotation matrix': [[0.976528, 0.035613, -0.212425], [0.213211, -0.299739, 0.929891], [-0.030556, -0.953356, -0.300296]], 'translation vector': [2.836028, 1.415543, 1.663749]}\nC: {'rotation matrix': [[0.976477, 0.035047, -0.212752], [0.213359, -0.299571, 0.929912], [-0.031143, -0.95343, -0.300002]], 'translation vector': [2.836339, 1.415174, 1.663386]}\nD: {'rotation matrix': [[0.9999989734089719, -0.00043750334983729943, -0.0007413258006153675], [0.00043629581692446537, 0.9999991219954293, -0.0014728840089735186], [0.0007417507454709163, 0.0014731899576831099, 0.9999977641755871]], 'translation vector': [0.0005969954680278278, -0.001266102560834259, -0.001081742192190538]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_187_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_187_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_187_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_187_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.97654, 0.035326, -0.212419], [0.213134, -0.299258, 0.930064], [-0.030712, -0.953518, -0.299767]], 'translation vector': [2.83562, 1.415562, 1.663413]}\nB: {'rotation matrix': [[0.976528, 0.035613, -0.212425], [0.213211, -0.299739, 0.929891], [-0.030556, -0.953356, -0.300296]], 'translation vector': [2.836028, 1.415543, 1.663749]}\nC: {'rotation matrix': [[0.976477, 0.035047, -0.212752], [0.213359, -0.299571, 0.929912], [-0.031143, -0.95343, -0.300002]], 'translation vector': [2.836339, 1.415174, 1.663386]}\nD: {'rotation matrix': [[0.9999989734089719, -0.00043750334983729943, -0.0007413258006153675], [0.00043629581692446537, 0.9999991219954293, -0.0014728840089735186], [0.0007417507454709163, 0.0014731899576831099, 0.9999977641755871]], 'translation vector': [0.0005969954680278278, -0.001266102560834259, -0.001081742192190538]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.693693, -0.417889, 0.586651], [-0.719234, -0.35819, 0.595318], [-0.038645, -0.834907, -0.549033]], 'translation vector': [2.468094, 0.650908, 1.47083]}\nB: {'rotation matrix': [[0.694995, -0.417186, 0.58561], [-0.71783, -0.35584, 0.598413], [-0.041266, -0.836262, -0.546775]], 'translation vector': [2.468435, 0.652249, 1.472357]}\nC: {'rotation matrix': [[0.692825, -0.417185, 0.588176], [-0.720192, -0.359253, 0.593516], [-0.036302, -0.834802, -0.549352]], 'translation vector': [2.467356, 0.649437, 1.470088]}\nD: {'rotation matrix': [[0.9999933308482389, -0.003211931119773083, -0.0013319651843937527], [0.003213068050675667, 0.999995289918959, 0.0008982640947086999], [0.0013289957939952358, -0.000903126250102818, 0.9999984333444373]], 'translation vector': [0.0004300736920825887, -0.0014825221206589134, 0.0006853212942847797]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_188_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_188_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_188_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_188_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.693693, -0.417889, 0.586651], [-0.719234, -0.35819, 0.595318], [-0.038645, -0.834907, -0.549033]], 'translation vector': [2.468094, 0.650908, 1.47083]}\nB: {'rotation matrix': [[0.694995, -0.417186, 0.58561], [-0.71783, -0.35584, 0.598413], [-0.041266, -0.836262, -0.546775]], 'translation vector': [2.468435, 0.652249, 1.472357]}\nC: {'rotation matrix': [[0.692825, -0.417185, 0.588176], [-0.720192, -0.359253, 0.593516], [-0.036302, -0.834802, -0.549352]], 'translation vector': [2.467356, 0.649437, 1.470088]}\nD: {'rotation matrix': [[0.9999933308482389, -0.003211931119773083, -0.0013319651843937527], [0.003213068050675667, 0.999995289918959, 0.0008982640947086999], [0.0013289957939952358, -0.000903126250102818, 0.9999984333444373]], 'translation vector': [0.0004300736920825887, -0.0014825221206589134, 0.0006853212942847797]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.999996219803918, 0.0022950861591112606, 0.0016527156635626785], [-0.002306966520835195, 0.9999706555795355, 0.007290423829373102], [-0.0016351749151619617, -0.007294378220159148, 0.9999718191472952]], 'translation vector': [-0.0006106128955529755, 0.003113758643644493, 0.0009225265168792962]}\nB: {'rotation matrix': [[-0.815808, -0.262316, 0.51541], [-0.578233, 0.385672, -0.71896], [-0.010185, -0.884561, -0.466314]], 'translation vector': [2.767913, 1.370181, 1.363789]}\nC: {'rotation matrix': [[-0.81395, -0.261884, 0.518557], [-0.58082, 0.384609, -0.717443], [-0.011555, -0.885151, -0.46516]], 'translation vector': [2.76859, 1.370986, 1.364432]}\nD: {'rotation matrix': [[-0.813152, -0.262698, 0.519397], [-0.581967, 0.382082, -0.717863], [-0.009871, -0.886004, -0.463573]], 'translation vector': [2.770085, 1.372341, 1.364365]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_189_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_189_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_189_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_189_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.999996219803918, 0.0022950861591112606, 0.0016527156635626785], [-0.002306966520835195, 0.9999706555795355, 0.007290423829373102], [-0.0016351749151619617, -0.007294378220159148, 0.9999718191472952]], 'translation vector': [-0.0006106128955529755, 0.003113758643644493, 0.0009225265168792962]}\nB: {'rotation matrix': [[-0.815808, -0.262316, 0.51541], [-0.578233, 0.385672, -0.71896], [-0.010185, -0.884561, -0.466314]], 'translation vector': [2.767913, 1.370181, 1.363789]}\nC: {'rotation matrix': [[-0.81395, -0.261884, 0.518557], [-0.58082, 0.384609, -0.717443], [-0.011555, -0.885151, -0.46516]], 'translation vector': [2.76859, 1.370986, 1.364432]}\nD: {'rotation matrix': [[-0.813152, -0.262698, 0.519397], [-0.581967, 0.382082, -0.717863], [-0.009871, -0.886004, -0.463573]], 'translation vector': [2.770085, 1.372341, 1.364365]}"}, "output": {"output_text": "A"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.928393, -0.117955, 0.352381], [-0.371416, -0.324296, 0.86999], [0.011656, -0.938573, -0.344885]], 'translation vector': [5.42922, 4.041657, 1.370122]}\nB: {'rotation matrix': [[0.9999934814259427, -0.0026531579456658904, 0.00224130267955375], [0.0026602242897140562, 0.9999913138748001, -0.003078123639036722], [-0.0022329200630453808, 0.0030851593249457813, 0.9999929388785541]], 'translation vector': [0.005873456268956634, 0.01508492723074184, 0.0010277199164683282]}\nC: {'rotation matrix': [[0.928402, -0.120953, 0.351341], [-0.37149, -0.322693, 0.870554], [0.008079, -0.938743, -0.344522]], 'translation vector': [5.430759, 4.038916, 1.364124]}\nD: {'rotation matrix': [[0.929388, -0.122542, 0.348169], [-0.369059, -0.323333, 0.87135], [0.005798, -0.938317, -0.345726]], 'translation vector': [5.437048, 4.036695, 1.363649]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_190_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_190_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_190_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_190_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.928393, -0.117955, 0.352381], [-0.371416, -0.324296, 0.86999], [0.011656, -0.938573, -0.344885]], 'translation vector': [5.42922, 4.041657, 1.370122]}\nB: {'rotation matrix': [[0.9999934814259427, -0.0026531579456658904, 0.00224130267955375], [0.0026602242897140562, 0.9999913138748001, -0.003078123639036722], [-0.0022329200630453808, 0.0030851593249457813, 0.9999929388785541]], 'translation vector': [0.005873456268956634, 0.01508492723074184, 0.0010277199164683282]}\nC: {'rotation matrix': [[0.928402, -0.120953, 0.351341], [-0.37149, -0.322693, 0.870554], [0.008079, -0.938743, -0.344522]], 'translation vector': [5.430759, 4.038916, 1.364124]}\nD: {'rotation matrix': [[0.929388, -0.122542, 0.348169], [-0.369059, -0.323333, 0.87135], [0.005798, -0.938317, -0.345726]], 'translation vector': [5.437048, 4.036695, 1.363649]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.953183, -0.088424, 0.289177], [-0.302167, 0.24151, -0.922154], [0.011701, -0.966361, -0.256923]], 'translation vector': [1.211269, 4.890959, 1.556685]}\nB: {'rotation matrix': [[-0.956401, -0.093459, 0.276701], [-0.291436, 0.243606, -0.925052], [0.019048, -0.965361, -0.260222]], 'translation vector': [1.215449, 4.887503, 1.555227]}\nC: {'rotation matrix': [[-0.955218, -0.090693, 0.281661], [-0.295515, 0.243707, -0.92373], [0.015133, -0.965599, -0.259595]], 'translation vector': [1.213553, 4.889249, 1.555428]}\nD: {'rotation matrix': [[0.9999952251812577, 0.0022961074705375706, -0.0021161445756707063], [-0.002289678755021547, 0.9999926232978033, 0.0030864959240033065], [0.002122435340409546, -0.003080853599724993, 0.9999927920546235]], 'translation vector': [-0.0038335858537008605, -0.001641279240269633, 0.007141792646779166]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_191_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_191_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_191_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_191_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.953183, -0.088424, 0.289177], [-0.302167, 0.24151, -0.922154], [0.011701, -0.966361, -0.256923]], 'translation vector': [1.211269, 4.890959, 1.556685]}\nB: {'rotation matrix': [[-0.956401, -0.093459, 0.276701], [-0.291436, 0.243606, -0.925052], [0.019048, -0.965361, -0.260222]], 'translation vector': [1.215449, 4.887503, 1.555227]}\nC: {'rotation matrix': [[-0.955218, -0.090693, 0.281661], [-0.295515, 0.243707, -0.92373], [0.015133, -0.965599, -0.259595]], 'translation vector': [1.213553, 4.889249, 1.555428]}\nD: {'rotation matrix': [[0.9999952251812577, 0.0022961074705375706, -0.0021161445756707063], [-0.002289678755021547, 0.9999926232978033, 0.0030864959240033065], [0.002122435340409546, -0.003080853599724993, 0.9999927920546235]], 'translation vector': [-0.0038335858537008605, -0.001641279240269633, 0.007141792646779166]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.984658, -0.071177, 0.15932], [-0.173691, -0.312158, 0.934018], [-0.016748, -0.94736, -0.319732]], 'translation vector': [3.953827, 2.817107, 1.554211]}\nB: {'rotation matrix': [[0.984585, -0.072332, 0.159251], [-0.17407, -0.316233, 0.932575], [-0.017095, -0.94592, -0.323949]], 'translation vector': [3.956161, 2.818039, 1.553922]}\nC: {'rotation matrix': [[1.000000390586898, -0.00015899006682454229, -7.860499707006931e-05], [0.0001592053293816045, 0.9999988073723325, -0.001691416563679354], [7.911078695652774e-05, 0.0016920717225864005, 0.999998246065904]], 'translation vector': [-0.0031188610741375022, -0.006275319353865605, -0.0020119857094367255]}\nD: {'rotation matrix': [[0.985021, -0.07075, 0.157254], [-0.171705, -0.318523, 0.932234], [-0.015866, -0.945271, -0.325899]], 'translation vector': [3.958103, 2.817717, 1.548612]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_192_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_192_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_192_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_192_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.984658, -0.071177, 0.15932], [-0.173691, -0.312158, 0.934018], [-0.016748, -0.94736, -0.319732]], 'translation vector': [3.953827, 2.817107, 1.554211]}\nB: {'rotation matrix': [[0.984585, -0.072332, 0.159251], [-0.17407, -0.316233, 0.932575], [-0.017095, -0.94592, -0.323949]], 'translation vector': [3.956161, 2.818039, 1.553922]}\nC: {'rotation matrix': [[1.000000390586898, -0.00015899006682454229, -7.860499707006931e-05], [0.0001592053293816045, 0.9999988073723325, -0.001691416563679354], [7.911078695652774e-05, 0.0016920717225864005, 0.999998246065904]], 'translation vector': [-0.0031188610741375022, -0.006275319353865605, -0.0020119857094367255]}\nD: {'rotation matrix': [[0.985021, -0.07075, 0.157254], [-0.171705, -0.318523, 0.932234], [-0.015866, -0.945271, -0.325899]], 'translation vector': [3.958103, 2.817717, 1.548612]}"}, "output": {"output_text": "C"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.324348, -0.501243, 0.802218], [-0.945427, 0.143915, -0.292328], [0.031076, -0.853255, -0.520567]], 'translation vector': [-0.28287, 2.921737, 1.307859]}\nB: {'rotation matrix': [[0.9998206713039935, 0.01088419825826216, -0.015474672154137172], [-0.010778287023678227, 0.999918107104106, 0.00690122311170722], [0.015548906823126927, -0.006732006329303681, 0.9998567482634122]], 'translation vector': [-0.0009142965758486277, -0.0021867567072129113, 0.0020065217081752795]}\nC: {'rotation matrix': [[-0.336594, -0.496252, 0.800274], [-0.941144, 0.149432, -0.30318], [0.030867, -0.855222, -0.517342]], 'translation vector': [-0.283556, 2.919329, 1.307566]}\nD: {'rotation matrix': [[-0.33061, -0.49822, 0.801544], [-0.943277, 0.147056, -0.297664], [0.03043, -0.854489, -0.518578]], 'translation vector': [-0.283976, 2.918767, 1.308425]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_193_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_193_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_193_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_193_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.324348, -0.501243, 0.802218], [-0.945427, 0.143915, -0.292328], [0.031076, -0.853255, -0.520567]], 'translation vector': [-0.28287, 2.921737, 1.307859]}\nB: {'rotation matrix': [[0.9998206713039935, 0.01088419825826216, -0.015474672154137172], [-0.010778287023678227, 0.999918107104106, 0.00690122311170722], [0.015548906823126927, -0.006732006329303681, 0.9998567482634122]], 'translation vector': [-0.0009142965758486277, -0.0021867567072129113, 0.0020065217081752795]}\nC: {'rotation matrix': [[-0.336594, -0.496252, 0.800274], [-0.941144, 0.149432, -0.30318], [0.030867, -0.855222, -0.517342]], 'translation vector': [-0.283556, 2.919329, 1.307566]}\nD: {'rotation matrix': [[-0.33061, -0.49822, 0.801544], [-0.943277, 0.147056, -0.297664], [0.03043, -0.854489, -0.518578]], 'translation vector': [-0.283976, 2.918767, 1.308425]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.988022, -0.009517, -0.154018], [0.15411, 0.009774, 0.988005], [-0.007897, -0.999907, 0.011124]], 'translation vector': [3.954252, 2.675021, 1.588509]}\nB: {'rotation matrix': [[0.9999405001439704, 0.001517544746561925, 0.010769614189192206], [-0.0015446990948080185, 0.9999954653771669, 0.0025653888781814608], [-0.01076601396546154, -0.002581837929747806, 0.999938856444669]], 'translation vector': [-0.04482632526707597, 0.009643399205063075, 0.00020168741742709884]}\nC: {'rotation matrix': [[0.989616, -0.01086, -0.14333], [0.143408, 0.0068, 0.98964], [-0.009773, -0.999918, 0.008287]], 'translation vector': [3.942101, 2.673398, 1.591243]}\nD: {'rotation matrix': [[0.990689, -0.012911, -0.13553], [0.135653, 0.009179, 0.990714], [-0.011547, -0.999875, 0.010845]], 'translation vector': [3.935715, 2.670411, 1.599032]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_194_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_194_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_194_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_194_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.988022, -0.009517, -0.154018], [0.15411, 0.009774, 0.988005], [-0.007897, -0.999907, 0.011124]], 'translation vector': [3.954252, 2.675021, 1.588509]}\nB: {'rotation matrix': [[0.9999405001439704, 0.001517544746561925, 0.010769614189192206], [-0.0015446990948080185, 0.9999954653771669, 0.0025653888781814608], [-0.01076601396546154, -0.002581837929747806, 0.999938856444669]], 'translation vector': [-0.04482632526707597, 0.009643399205063075, 0.00020168741742709884]}\nC: {'rotation matrix': [[0.989616, -0.01086, -0.14333], [0.143408, 0.0068, 0.98964], [-0.009773, -0.999918, 0.008287]], 'translation vector': [3.942101, 2.673398, 1.591243]}\nD: {'rotation matrix': [[0.990689, -0.012911, -0.13553], [0.135653, 0.009179, 0.990714], [-0.011547, -0.999875, 0.010845]], 'translation vector': [3.935715, 2.670411, 1.599032]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.986946, -0.051965, 0.152438], [-0.150832, 0.630041, -0.761774], [-0.056457, -0.774822, -0.629654]], 'translation vector': [2.054614, 1.600808, 1.269291]}\nB: {'rotation matrix': [[-0.986874, -0.051472, 0.153072], [-0.151005, 0.630133, -0.761663], [-0.057252, -0.774779, -0.629634]], 'translation vector': [2.054307, 1.600529, 1.268919]}\nC: {'rotation matrix': [[-0.98698, -0.05266, 0.151977], [-0.150937, 0.629701, -0.762033], [-0.055572, -0.77505, -0.629451]], 'translation vector': [2.055977, 1.600957, 1.269368]}\nD: {'rotation matrix': [[0.9999963800999353, -0.00021451754451556594, -0.002594557385802436], [0.0002238699679127643, 0.9999932861643056, 0.0035784816404103737], [0.002594523779822574, -0.003578355478331594, 0.9999903397449379]], 'translation vector': [-0.000729278960620583, -0.000294753198244152, 0.0006269109678853635]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_195_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_195_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_195_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_195_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.986946, -0.051965, 0.152438], [-0.150832, 0.630041, -0.761774], [-0.056457, -0.774822, -0.629654]], 'translation vector': [2.054614, 1.600808, 1.269291]}\nB: {'rotation matrix': [[-0.986874, -0.051472, 0.153072], [-0.151005, 0.630133, -0.761663], [-0.057252, -0.774779, -0.629634]], 'translation vector': [2.054307, 1.600529, 1.268919]}\nC: {'rotation matrix': [[-0.98698, -0.05266, 0.151977], [-0.150937, 0.629701, -0.762033], [-0.055572, -0.77505, -0.629451]], 'translation vector': [2.055977, 1.600957, 1.269368]}\nD: {'rotation matrix': [[0.9999963800999353, -0.00021451754451556594, -0.002594557385802436], [0.0002238699679127643, 0.9999932861643056, 0.0035784816404103737], [0.002594523779822574, -0.003578355478331594, 0.9999903397449379]], 'translation vector': [-0.000729278960620583, -0.000294753198244152, 0.0006269109678853635]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.68897, 0.400817, -0.603877], [0.724521, 0.403569, -0.55875], [0.01975, -0.822483, -0.568447]], 'translation vector': [2.703838, 2.593028, 1.451995]}\nB: {'rotation matrix': [[0.9999867433099353, 0.0013037371773630478, -0.00505037420137311], [-0.0013327082277047075, 0.9999835004529126, -0.005699178965962697], [0.005042267928314986, 0.005705137184663826, 0.9999709125049986]], 'translation vector': [-0.003533739342698565, -0.0004956556588801009, 0.0008851453202214365]}\nC: {'rotation matrix': [[-0.687775, 0.405276, -0.60226], [0.725687, 0.405077, -0.55614], [0.018572, -0.819551, -0.572706]], 'translation vector': [2.702493, 2.593958, 1.452821]}\nD: {'rotation matrix': [[-0.6898, 0.39893, -0.604178], [0.723736, 0.402474, -0.560554], [0.019544, -0.823935, -0.566347]], 'translation vector': [2.703783, 2.591564, 1.452902]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_196_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_196_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_196_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_196_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.68897, 0.400817, -0.603877], [0.724521, 0.403569, -0.55875], [0.01975, -0.822483, -0.568447]], 'translation vector': [2.703838, 2.593028, 1.451995]}\nB: {'rotation matrix': [[0.9999867433099353, 0.0013037371773630478, -0.00505037420137311], [-0.0013327082277047075, 0.9999835004529126, -0.005699178965962697], [0.005042267928314986, 0.005705137184663826, 0.9999709125049986]], 'translation vector': [-0.003533739342698565, -0.0004956556588801009, 0.0008851453202214365]}\nC: {'rotation matrix': [[-0.687775, 0.405276, -0.60226], [0.725687, 0.405077, -0.55614], [0.018572, -0.819551, -0.572706]], 'translation vector': [2.702493, 2.593958, 1.452821]}\nD: {'rotation matrix': [[-0.6898, 0.39893, -0.604178], [0.723736, 0.402474, -0.560554], [0.019544, -0.823935, -0.566347]], 'translation vector': [2.703783, 2.591564, 1.452902]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[0.956857, -0.169845, 0.235747], [-0.290511, -0.544648, 0.786741], [-0.005224, -0.821286, -0.570492]], 'translation vector': [1.276382, 2.833935, 1.317457]}\nB: {'rotation matrix': [[0.9999970754093099, -0.00022106845961624886, 0.002343703924443124], [0.0002217235397933884, 1.000000114885585, -0.0003702254651105004], [-0.0023436320323324275, 0.00037212161436898277, 0.9999969566117395]], 'translation vector': [-0.0012761111666008684, -0.00028494477773222116, -0.0002961069063054378]}\nC: {'rotation matrix': [[0.956588, -0.169724, 0.236925], [-0.291413, -0.54533, 0.785935], [-0.00419, -0.820859, -0.571116]], 'translation vector': [1.27605, 2.834144, 1.316524]}\nD: {'rotation matrix': [[0.956511, -0.169195, 0.237615], [-0.291683, -0.546399, 0.785092], [-0.003001, -0.820257, -0.571988]], 'translation vector': [1.276076, 2.834318, 1.31658]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_197_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_197_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_197_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_197_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[0.956857, -0.169845, 0.235747], [-0.290511, -0.544648, 0.786741], [-0.005224, -0.821286, -0.570492]], 'translation vector': [1.276382, 2.833935, 1.317457]}\nB: {'rotation matrix': [[0.9999970754093099, -0.00022106845961624886, 0.002343703924443124], [0.0002217235397933884, 1.000000114885585, -0.0003702254651105004], [-0.0023436320323324275, 0.00037212161436898277, 0.9999969566117395]], 'translation vector': [-0.0012761111666008684, -0.00028494477773222116, -0.0002961069063054378]}\nC: {'rotation matrix': [[0.956588, -0.169724, 0.236925], [-0.291413, -0.54533, 0.785935], [-0.00419, -0.820859, -0.571116]], 'translation vector': [1.27605, 2.834144, 1.316524]}\nD: {'rotation matrix': [[0.956511, -0.169195, 0.237615], [-0.291683, -0.546399, 0.785092], [-0.003001, -0.820257, -0.571988]], 'translation vector': [1.276076, 2.834318, 1.31658]}"}, "output": {"output_text": "B"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.13245, -0.562539, 0.816092], [-0.991146, 0.067404, -0.114398], [0.009346, -0.824018, -0.566486]], 'translation vector': [2.413971, 4.448666, 1.362137]}\nB: {'rotation matrix': [[-0.128485, -0.560739, 0.817963], [-0.991659, 0.064113, -0.111818], [0.010258, -0.825507, -0.564299]], 'translation vector': [2.417159, 4.443525, 1.361777]}\nC: {'rotation matrix': [[-0.132037, -0.563719, 0.815345], [-0.991214, 0.068574, -0.113106], [0.007848, -0.823115, -0.567821]], 'translation vector': [2.411706, 4.446467, 1.360844]}\nD: {'rotation matrix': [[0.9999987411869987, 0.0004418575380488951, 0.0016406984025160954], [-0.0004493688554351619, 0.9999862647190665, 0.005219826446688158], [-0.0016383092715178728, -0.005221150249650812, 0.9999852785117939]], 'translation vector': [-0.005409867262581081, 0.0012422036096766398, -0.002713386665627704]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_198_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_198_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_198_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_198_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.13245, -0.562539, 0.816092], [-0.991146, 0.067404, -0.114398], [0.009346, -0.824018, -0.566486]], 'translation vector': [2.413971, 4.448666, 1.362137]}\nB: {'rotation matrix': [[-0.128485, -0.560739, 0.817963], [-0.991659, 0.064113, -0.111818], [0.010258, -0.825507, -0.564299]], 'translation vector': [2.417159, 4.443525, 1.361777]}\nC: {'rotation matrix': [[-0.132037, -0.563719, 0.815345], [-0.991214, 0.068574, -0.113106], [0.007848, -0.823115, -0.567821]], 'translation vector': [2.411706, 4.446467, 1.360844]}\nD: {'rotation matrix': [[0.9999987411869987, 0.0004418575380488951, 0.0016406984025160954], [-0.0004493688554351619, 0.9999862647190665, 0.005219826446688158], [-0.0016383092715178728, -0.005221150249650812, 0.9999852785117939]], 'translation vector': [-0.005409867262581081, 0.0012422036096766398, -0.002713386665627704]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "SCANNET_threed_pose_estimation", "options": "A: {'rotation matrix': [[-0.394987, 0.317496, -0.862079], [0.912593, 0.027696, -0.407931], [-0.10564, -0.947855, -0.300685]], 'translation vector': [4.882912, 2.963368, 1.402415]}\nB: {'rotation matrix': [[-0.393743, 0.318728, -0.862194], [0.912946, 0.026185, -0.40724], [-0.107222, -0.947484, -0.301292]], 'translation vector': [4.884082, 2.960136, 1.407949]}\nC: {'rotation matrix': [[-0.393984, 0.318317, -0.862236], [0.913424, 0.031343, -0.405802], [-0.102149, -0.947466, -0.303106]], 'translation vector': [4.883262, 2.96182, 1.402411]}\nD: {'rotation matrix': [[0.9999866432276233, 0.002858711999338773, -0.0043010740025884895], [-0.0027980802651053054, 0.9998995896653632, 0.013894745582855106], [0.004339725514880082, -0.013881253175420062, 0.9998943009326048]], 'translation vector': [-0.001200424251745491, -0.0035619824296807545, 0.0012814893270478578]}", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_199_0.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_199_1.png", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_199_2.jpg", "3D-spatial/threeD_Pose_Estimation/threeD_Pose_Estimation_199_3.png"], "question": "Given a pair of a RGB image and a depth image for scan 0, along with another pair of a RGB image and a depth image for scan 1, please estimate the relative camera pose from scan 0 to scan 1. The output should indicate the rotation matrix and the translation vector.", "context": "Your task is to estimate the relative camera pose between two scans. \nSelect from the following choices.\nA: {'rotation matrix': [[-0.394987, 0.317496, -0.862079], [0.912593, 0.027696, -0.407931], [-0.10564, -0.947855, -0.300685]], 'translation vector': [4.882912, 2.963368, 1.402415]}\nB: {'rotation matrix': [[-0.393743, 0.318728, -0.862194], [0.912946, 0.026185, -0.40724], [-0.107222, -0.947484, -0.301292]], 'translation vector': [4.884082, 2.960136, 1.407949]}\nC: {'rotation matrix': [[-0.393984, 0.318317, -0.862236], [0.913424, 0.031343, -0.405802], [-0.102149, -0.947466, -0.303106]], 'translation vector': [4.883262, 2.96182, 1.402411]}\nD: {'rotation matrix': [[0.9999866432276233, 0.002858711999338773, -0.0043010740025884895], [-0.0027980802651053054, 0.9998995896653632, 0.013894745582855106], [0.004339725514880082, -0.013881253175420062, 0.9998943009326048]], 'translation vector': [-0.001200424251745491, -0.0035619824296807545, 0.0012814893270478578]}"}, "output": {"output_text": "D"}, "task": "threeD_Pose_Estimation"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: uncertain\nC: no\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_0_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_0_11.png"], "question": "Are there any things?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: uncertain\nC: no\nD: maybe"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: walking\nB: lying down\nC: sitting\nD: standing", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_1_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_1_11.png"], "question": "What is the status of the pedestrian to the back right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: walking\nB: lying down\nC: sitting\nD: standing"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 2\nC: 3\nD: 0", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_2_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_2_11.png"], "question": "How many cars are to the front left of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 2\nC: 3\nD: 0"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 12\nC: 3\nD: 8", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_3_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_3_11.png"], "question": "How many moving things are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 12\nC: 3\nD: 8"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: idling\nC: broken down\nD: parked", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_4_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_4_11.png"], "question": "The truck to the back right of the bus is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: idling\nC: broken down\nD: parked"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: not sure\nC: yes\nD: unknown", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_5_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_5_11.png"], "question": "Are there any other pedestrians of the same status as the thing that is to the front of the bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: not sure\nC: yes\nD: unknown"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: new\nB: without rider\nC: for sale\nD: broken", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_6_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_6_11.png"], "question": "There is a motorcycle; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: new\nB: without rider\nC: for sale\nD: broken"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: sitting\nB: jumping\nC: stationary\nD: moving", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_7_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_7_11.png"], "question": "What status is the pedestrian that is to the front of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: sitting\nB: jumping\nC: stationary\nD: moving"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: on the road\nB: without rider\nC: being ridden by someone\nD: inside the truck", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_8_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_8_11.png"], "question": "What is the status of the motorcycle to the front of the parked truck?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: on the road\nB: without rider\nC: being ridden by someone\nD: inside the truck"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 7\nB: 5\nC: 3\nD: 10", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_9_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_9_11.png"], "question": "How many cars are to the back of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 7\nB: 5\nC: 3\nD: 10"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: with rider\nB: in repair\nC: being sold\nD: locked up", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_10_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_10_11.png"], "question": "The bicycle is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: with rider\nB: in repair\nC: being sold\nD: locked up"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 2\nB: 4\nC: 10\nD: 8", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_11_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_11_11.png"], "question": "How many other things are there of the same status as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 2\nB: 4\nC: 10\nD: 8"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: I don’t know\nC: no\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_12_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_12_11.png"], "question": "Is the status of the thing that is to the front left of the construction vehicle the same as the car to the back of the motorcycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: I don’t know\nC: no\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: waiting\nB: moving\nC: departing\nD: stopped", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_13_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_13_11.png"], "question": "There is a bus; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: waiting\nB: moving\nC: departing\nD: stopped"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 6\nB: 4\nC: 2\nD: 7", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_14_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_14_11.png"], "question": "There is a bicycle; how many moving things are to the back right of it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 6\nB: 4\nC: 2\nD: 7"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: no\nC: possibly\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_15_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_15_11.png"], "question": "Are any trucks visible?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: no\nC: possibly\nD: maybe"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 12\nC: 3\nD: 9", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_16_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_16_11.png"], "question": "What number of parked cars are to the front left of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 12\nC: 3\nD: 9"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: broken\nB: with rider\nC: new\nD: without rider", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_17_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_17_11.png"], "question": "The bicycle to the front of me is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: broken\nB: with rider\nC: new\nD: without rider"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: uncertain\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_18_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_18_11.png"], "question": "There is a truck that is to the back right of the parked thing; is its status the same as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: uncertain\nD: yes"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: unsure\nB: yes\nC: no\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_19_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_19_11.png"], "question": "There is a car that is to the front left of the motorcycle; is it the same status as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: unsure\nB: yes\nC: no\nD: maybe"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no, but there is a stationary bus\nB: yes, the bus is moving\nC: yes, there is a bus in the frame\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_20_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_20_11.png"], "question": "There is a with rider motorcycle; are there any moving buss to the back right of it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no, but there is a stationary bus\nB: yes, the bus is moving\nC: yes, there is a bus in the frame\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: train\nB: trees\nC: bike\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_21_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_21_11.png"], "question": "The thing that is both to the back of the bus and the front left of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: train\nB: trees\nC: bike\nD: car"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: uncertain\nC: maybe\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_22_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_22_11.png"], "question": "Are there any other things of the same status as the traffic cone to the front left of the with rider thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: uncertain\nC: maybe\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: uncertain\nB: no\nC: yes\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_23_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_23_11.png"], "question": "There is a truck that is to the back right of the bus; is it the same status as the car that is to the back right of the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: uncertain\nB: no\nC: yes\nD: maybe"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: sitting\nB: lying down\nC: moving\nD: standing", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_24_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_24_11.png"], "question": "What is the status of the pedestrian to the front of the parked thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: sitting\nB: lying down\nC: moving\nD: standing"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: not sure\nB: yes\nC: maybe\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_25_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_25_11.png"], "question": "Are there any other things that in the same status as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: not sure\nB: yes\nC: maybe\nD: no"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: tree\nB: bicycle\nC: lamp post\nD: pedestrian", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_26_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_26_11.png"], "question": "What is the thing that is to the front left of me and the back right of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: tree\nB: bicycle\nC: lamp post\nD: pedestrian"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: sold\nB: moving\nC: parked\nD: broken down", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_27_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_27_11.png"], "question": "What is the status of the trailer?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: sold\nB: moving\nC: parked\nD: broken down"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: with rider\nB: being serviced\nC: missing\nD: on stand", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_28_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_28_11.png"], "question": "What status is the motorcycle to the front left of the parked thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: with rider\nB: being serviced\nC: missing\nD: on stand"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: sometimes\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_29_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_29_11.png"], "question": "There is a bus; is its status the same as the bicycle to the back of the with rider motorcycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: sometimes\nD: yes"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: traffic light\nB: bus stop sign\nC: hydrant\nD: pedestrian", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_30_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_30_11.png"], "question": "What is the standing pedestrian that is to the front left of the stopped bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: traffic light\nB: bus stop sign\nC: hydrant\nD: pedestrian"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: motorcycle\nB: truck\nC: bicycle\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_31_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_31_11.png"], "question": "What is the thing that is both to the back right of the stopped bus and the front left of the parked truck?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: motorcycle\nB: truck\nC: bicycle\nD: car"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: uncertain\nC: no\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_32_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_32_11.png"], "question": "Are there any other construction vehicles of the same status as the car that is to the back of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: uncertain\nC: no\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: airplane\nB: train\nC: car\nD: motorcycle", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_33_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_33_11.png"], "question": "The with rider thing to the front left of the with rider bicycle is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: airplane\nB: train\nC: car\nD: motorcycle"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: pedestrian\nB: building\nC: bus\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_34_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_34_11.png"], "question": "What is the thing that is both to the back of the standing pedestrian and the front left of the parked thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: pedestrian\nB: building\nC: bus\nD: car"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: no\nC: unknown\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_35_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_35_11.png"], "question": "There is a car to the back right of the stopped bus; does it have the same status as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: no\nC: unknown\nD: maybe"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: stopped\nC: under maintenance\nD: idle", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_36_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_36_11.png"], "question": "What is the status of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: stopped\nC: under maintenance\nD: idle"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: no\nC: maybe\nD: sometimes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_37_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_37_11.png"], "question": "Are there any trailers to the front right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: no\nC: maybe\nD: sometimes"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: maybe\nC: no\nD: uncertain", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_38_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_38_11.png"], "question": "Is the status of the truck to the front left of the without rider motorcycle the same as the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: maybe\nC: no\nD: uncertain"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: parked\nB: reversing\nC: broken down\nD: moving", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_39_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_39_11.png"], "question": "What status is the car that is to the front of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: parked\nB: reversing\nC: broken down\nD: moving"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: maybe\nC: sometimes\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_40_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_40_11.png"], "question": "Are there any other pedestrians of the same status as the trailer?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: maybe\nC: sometimes\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: no\nC: maybe\nD: not sure", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_41_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_41_11.png"], "question": "Are there any other things that in the same status as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: no\nC: maybe\nD: not sure"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 1\nB: 0\nC: 3\nD: 2", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_42_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_42_11.png"], "question": "How many other motorcycles in the same status as the car that is to the back of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 1\nB: 0\nC: 3\nD: 2"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: not sure\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_43_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_43_11.png"], "question": "There is a parked truck; are there any moving pedestrians to the front left of it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: not sure\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: under maintenance\nB: moving\nC: parked\nD: stopped", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_44_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_44_11.png"], "question": "There is a truck to the front left of the stopped construction vehicle; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: under maintenance\nB: moving\nC: parked\nD: stopped"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bicycle\nB: car\nC: motorcycle\nD: tricycle", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_45_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_45_11.png"], "question": "There is a with rider thing that is to the front left of the bicycle; what is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bicycle\nB: car\nC: motorcycle\nD: tricycle"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: yes\nC: no\nD: I can't tell", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_46_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_46_11.png"], "question": "Are there any motorcycles to the front right of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: yes\nC: no\nD: I can't tell"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bus\nB: car\nC: bike\nD: train", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_47_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_47_11.png"], "question": "What is the stopped thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bus\nB: car\nC: bike\nD: train"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: pedestrian\nB: car\nC: bike\nD: tree", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_48_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_48_11.png"], "question": "The standing pedestrian that is to the front left of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: pedestrian\nB: car\nC: bike\nD: tree"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 2\nB: 5\nC: 3\nD: 1", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_49_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_49_11.png"], "question": "How many other things are in the same status as the bus that is to the back right of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 2\nB: 5\nC: 3\nD: 1"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: standing\nB: moving\nC: lying down\nD: sitting", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_50_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_50_11.png"], "question": "What is the status of the pedestrian that is to the front of the traffic cone?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: standing\nB: moving\nC: lying down\nD: sitting"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: yes\nD: unknown", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_51_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_51_11.png"], "question": "There is a motorcycle to the back right of the parked thing; does it have the same status as the bicycle that is to the back right of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: unknown"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: possibly\nC: no\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_52_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_52_11.png"], "question": "Are there any buss to the front right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: possibly\nC: no\nD: yes"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: parked\nB: moving\nC: under maintenance\nD: stopping", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_53_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_53_11.png"], "question": "The bus is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: parked\nB: moving\nC: under maintenance\nD: stopping"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: parked\nB: broken down\nC: moving\nD: stopped", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_54_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_54_11.png"], "question": "The bus that is to the front of me is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: parked\nB: broken down\nC: moving\nD: stopped"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bicycle\nB: car\nC: motorcycle\nD: pedestrian", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_55_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_55_11.png"], "question": "The thing that is both to the back of the stopped bus and the back right of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bicycle\nB: car\nC: motorcycle\nD: pedestrian"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: maybe\nC: uncertain\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_56_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_56_11.png"], "question": "There is a with rider thing; are there any parked cars to the back of it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: maybe\nC: uncertain\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: sometimes\nB: no\nC: yes\nD: uncertain", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_57_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_57_11.png"], "question": "Does the bicycle have the same status as the thing that is to the back right of the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: sometimes\nB: no\nC: yes\nD: uncertain"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: only on weekends\nB: no\nC: sometimes\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_58_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_58_11.png"], "question": "Are any without rider things visible?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: only on weekends\nB: no\nC: sometimes\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: unknown\nC: no\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_59_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_59_11.png"], "question": "Is the status of the bicycle the same as the truck that is to the back of the without rider motorcycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: unknown\nC: no\nD: yes"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: not applicable\nB: no\nC: uncertain\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_60_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_60_11.png"], "question": "Are there any other buss that in the same status as the motorcycle to the back right of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: not applicable\nB: no\nC: uncertain\nD: yes"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 9\nB: 5\nC: 12\nD: 7", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_61_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_61_11.png"], "question": "What number of cars are to the back right of the trailer?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 9\nB: 5\nC: 12\nD: 7"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 3\nB: 0\nC: 1\nD: 2", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_62_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_62_11.png"], "question": "What number of other things are there of the same status as the bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 3\nB: 0\nC: 1\nD: 2"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: with rider\nB: on the ground\nC: in repair\nD: broken", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_63_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_63_11.png"], "question": "What is the status of the bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: with rider\nB: on the ground\nC: in repair\nD: broken"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: unsure\nB: no\nC: maybe\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_64_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_64_11.png"], "question": "Are there any other bicycles of the same status as the car to the front left of the parked trailer?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: unsure\nB: no\nC: maybe\nD: yes"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: being repaired\nB: moving\nC: stopped\nD: parked", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_65_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_65_11.png"], "question": "The construction vehicle is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: being repaired\nB: moving\nC: stopped\nD: parked"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: building\nB: bicycle\nC: tree\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_66_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_66_11.png"], "question": "The thing that is both to the front left of the construction vehicle and the back of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: building\nB: bicycle\nC: tree\nD: car"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 4\nB: 2\nC: 0\nD: 1", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_67_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_67_11.png"], "question": "What number of moving cars are to the front left of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 4\nB: 2\nC: 0\nD: 1"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 9\nB: 5\nC: 12\nD: 7", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_68_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_68_11.png"], "question": "How many cars are to the back of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 9\nB: 5\nC: 12\nD: 7"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: sometimes\nB: no\nC: only during peak hours\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_69_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_69_11.png"], "question": "Are there any moving trailers?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: sometimes\nB: no\nC: only during peak hours\nD: yes"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 6\nB: 12\nC: 9\nD: 3", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_70_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_70_11.png"], "question": "What number of cars are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 6\nB: 12\nC: 9\nD: 3"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: unknown\nB: yes\nC: no\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_71_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_71_11.png"], "question": "Is the status of the car that is to the front of the trailer the same as the trailer?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: unknown\nB: yes\nC: no\nD: maybe"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: uncertain\nC: no\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_72_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_72_11.png"], "question": "There is a construction vehicle that is to the front left of the parked truck; does it have the same status as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: uncertain\nC: no\nD: maybe"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: stopped\nB: moving\nC: broken down\nD: cancelled", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_73_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_73_11.png"], "question": "What is the status of the bus to the back right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: stopped\nB: moving\nC: broken down\nD: cancelled"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 4\nB: 9\nC: 7\nD: 12", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_74_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_74_11.png"], "question": "How many other things in the same status as the thing that is to the front of the with rider bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 4\nB: 9\nC: 7\nD: 12"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: maybe\nC: yes\nD: unsure", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_75_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_75_11.png"], "question": "Are there any other buss of the same status as the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: maybe\nC: yes\nD: unsure"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: sometimes\nC: maybe\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_76_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_76_11.png"], "question": "There is a construction vehicle; is its status the same as the bus that is to the front of the truck?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: sometimes\nC: maybe\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: tree\nB: car\nC: bicycle\nD: bench", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_77_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_77_11.png"], "question": "The thing that is both to the back right of the trailer and the back right of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: tree\nB: car\nC: bicycle\nD: bench"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 9\nB: 7\nC: 5\nD: 12", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_78_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_78_11.png"], "question": "What number of other things in the same status as the car that is to the front left of the motorcycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 9\nB: 7\nC: 5\nD: 12"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 10\nB: 3\nC: 1\nD: 5", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_79_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_79_11.png"], "question": "What number of other things are in the same status as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 10\nB: 3\nC: 1\nD: 5"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bench\nB: pedestrian\nC: tree\nD: bicycle", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_80_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_80_11.png"], "question": "The thing that is both to the back right of the stopped bus and the back right of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bench\nB: pedestrian\nC: tree\nD: bicycle"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 3\nC: 7\nD: 2", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_81_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_81_11.png"], "question": "What number of other things are in the same status as the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 3\nC: 7\nD: 2"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 3\nB: 5\nC: 2\nD: 0", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_82_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_82_11.png"], "question": "What number of moving buss are to the front right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 3\nB: 5\nC: 2\nD: 0"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: motorcycle\nB: bicycle\nC: trolley\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_83_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_83_11.png"], "question": "The without rider thing that is to the front left of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: motorcycle\nB: bicycle\nC: trolley\nD: car"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: with rider\nB: in front of the bus\nC: without rider\nD: parked", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_84_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_84_11.png"], "question": "What status is the motorcycle to the back of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: with rider\nB: in front of the bus\nC: without rider\nD: parked"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: scooter\nB: rollerblades\nC: motorcycle\nD: bicycle", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_85_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_85_11.png"], "question": "The with rider thing to the back right of the moving bus is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: scooter\nB: rollerblades\nC: motorcycle\nD: bicycle"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bicycle\nB: dog\nC: pedestrian\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_86_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_86_11.png"], "question": "What is the moving thing that is both to the back right of the motorcycle and the front of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bicycle\nB: dog\nC: pedestrian\nD: car"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: uncertain\nB: maybe\nC: yes\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_87_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_87_11.png"], "question": "Is there another car that has the same status as the thing that is to the front left of the with rider thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: uncertain\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: without rider\nB: parked\nC: with rider\nD: damaged", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_88_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_88_11.png"], "question": "What status is the motorcycle to the back of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: without rider\nB: parked\nC: with rider\nD: damaged"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 7\nB: 3\nC: 6\nD: 4", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_89_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_89_11.png"], "question": "There is a stopped bus; what number of moving things are to the front left of it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 7\nB: 3\nC: 6\nD: 4"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: turning\nB: stopped\nC: broken down\nD: moving", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_90_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_90_11.png"], "question": "There is a truck to the back right of the moving truck; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: turning\nB: stopped\nC: broken down\nD: moving"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: maybe\nC: sometimes\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_91_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_91_11.png"], "question": "Are there any cars?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: maybe\nC: sometimes\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: stationary\nC: parked\nD: broken down", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_92_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_92_11.png"], "question": "There is a car to the back right of me; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: stationary\nC: parked\nD: broken down"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: unknown\nB: yes\nC: maybe\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_93_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_93_11.png"], "question": "There is a construction vehicle; does it have the same status as the car to the back right of the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: unknown\nB: yes\nC: maybe\nD: no"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: motorcycle\nB: bicycle\nC: truck\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_94_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_94_11.png"], "question": "What is the moving thing that is both to the back right of the bus and the front left of the with rider bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: motorcycle\nB: bicycle\nC: truck\nD: car"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: sometimes\nC: yes\nD: probably not", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_95_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_95_11.png"], "question": "Are there any barriers?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: sometimes\nC: yes\nD: probably not"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 7\nB: 10\nC: 3\nD: 5", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_96_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_96_11.png"], "question": "How many moving pedestrians are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 7\nB: 10\nC: 3\nD: 5"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: yes\nC: there is a rider without a car\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_97_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_97_11.png"], "question": "There is a with rider thing; are there any stopped cars to the back left of it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: yes\nC: there is a rider without a car\nD: maybe"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: not sure\nC: maybe\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_98_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_98_11.png"], "question": "Is there another car of the same status as the pedestrian to the front of the with rider thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: not sure\nC: maybe\nD: no"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: not sure\nB: no\nC: maybe\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_99_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_99_11.png"], "question": "Are there any moving buss?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: not sure\nB: no\nC: maybe\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: under maintenance\nC: being loaded\nD: parked", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_100_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_100_11.png"], "question": "The construction vehicle is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: under maintenance\nC: being loaded\nD: parked"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: delayed\nB: stopped\nC: broken down\nD: moving", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_101_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_101_11.png"], "question": "The bus is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: delayed\nB: stopped\nC: broken down\nD: moving"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: lost\nB: moving\nC: stopped\nD: waiting", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_102_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_102_11.png"], "question": "There is a pedestrian that is to the front left of the stopped bus; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: lost\nB: moving\nC: stopped\nD: waiting"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: maybe\nC: not sure\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_103_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_103_11.png"], "question": "There is a bus; is it the same status as the thing that is to the back of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: maybe\nC: not sure\nD: no"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: parked\nB: moving\nC: stopped\nD: overturned", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_104_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_104_11.png"], "question": "What is the status of the construction vehicle to the front of the bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: parked\nB: moving\nC: stopped\nD: overturned"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: sometimes\nC: no\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_105_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_105_11.png"], "question": "Are any things visible?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: sometimes\nC: no\nD: maybe"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: I don't know\nC: maybe\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_106_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_106_11.png"], "question": "Are there any things to the front of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: I don't know\nC: maybe\nD: no"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bicycle\nB: bus\nC: train\nD: plane", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_107_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_107_11.png"], "question": "There is a stopped thing that is to the front of me; what is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bicycle\nB: bus\nC: train\nD: plane"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: uncertain\nB: no\nC: yes\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_108_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_108_11.png"], "question": "Are there any other things that in the same status as the car to the front left of the barrier?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: uncertain\nB: no\nC: yes\nD: maybe"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: yes\nD: possibly", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_109_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_109_11.png"], "question": "Does the car that is to the front left of the moving truck have the same status as the motorcycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: possibly"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: under maintenance\nC: accelerating\nD: stopped", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_110_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_110_11.png"], "question": "The construction vehicle is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: under maintenance\nC: accelerating\nD: stopped"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: under maintenance\nC: delayed\nD: stopped", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_111_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_111_11.png"], "question": "The bus is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: under maintenance\nC: delayed\nD: stopped"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: yes\nD: uncertain", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_112_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_112_11.png"], "question": "There is a car to the front left of the bicycle; is its status the same as the truck to the back right of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: uncertain"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: yes\nC: no\nD: uncertain", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_113_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_113_11.png"], "question": "Is there another car that has the same status as the motorcycle to the back of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: yes\nC: no\nD: uncertain"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: maybe\nC: sometimes\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_114_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_114_11.png"], "question": "There is a car to the front of the construction vehicle; is its status the same as the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: maybe\nC: sometimes\nD: yes"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: yes\nC: only one other thing\nD: uncertain", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_115_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_115_11.png"], "question": "Are there any other things of the same status as the motorcycle that is to the front left of the parked thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: yes\nC: only one other thing\nD: uncertain"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: yes\nD: unknown", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_116_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_116_11.png"], "question": "Are there any cars to the back left of the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: unknown"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: broken down\nB: moving backward\nC: without rider\nD: with rider", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_117_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_117_11.png"], "question": "There is a motorcycle that is to the back of me; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: broken down\nB: moving backward\nC: without rider\nD: with rider"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 10\nB: 15\nC: 3\nD: 5", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_118_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_118_11.png"], "question": "What number of cars are to the front left of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 10\nB: 15\nC: 3\nD: 5"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: broken\nB: without rider\nC: missing\nD: with rider", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_119_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_119_11.png"], "question": "There is a thing that is to the front left of me; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: broken\nB: without rider\nC: missing\nD: with rider"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: sitting\nB: standing\nC: running\nD: walking", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_120_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_120_11.png"], "question": "What status is the pedestrian that is to the back right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: sitting\nB: standing\nC: running\nD: walking"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: not sure\nB: maybe\nC: yes\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_121_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_121_11.png"], "question": "Does the truck to the back of the bus have the same status as the construction vehicle that is to the back right of the with rider thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: not sure\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: tree\nB: mailbox\nC: sidewalk\nD: traffic cone", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_122_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_122_11.png"], "question": "The thing that is to the back of the moving car and the front left of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: tree\nB: mailbox\nC: sidewalk\nD: traffic cone"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: unknown\nB: no\nC: yes\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_123_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_123_11.png"], "question": "Do the thing that is to the front of the stopped car and the pedestrian that is to the front left of the stopped car have the same status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: unknown\nB: no\nC: yes\nD: maybe"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: unknown\nB: no\nC: maybe\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_124_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_124_11.png"], "question": "Are there any other pedestrians of the same status as the bus that is to the front left of the stopped bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: unknown\nB: no\nC: maybe\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: broken down\nB: being repaired\nC: without rider\nD: with rider", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_125_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_125_11.png"], "question": "What is the status of the motorcycle that is to the front left of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: broken down\nB: being repaired\nC: without rider\nD: with rider"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: departing\nC: stopped\nD: arriving", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_126_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_126_11.png"], "question": "What is the status of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: departing\nC: stopped\nD: arriving"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: there are people\nB: yes\nC: a car\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_127_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_127_11.png"], "question": "Are there any moving things to the back left of the with rider bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: there are people\nB: yes\nC: a car\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: absent\nB: moving\nC: dangerous\nD: stationary", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_128_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_128_11.png"], "question": "There is a pedestrian; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: absent\nB: moving\nC: dangerous\nD: stationary"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: some\nB: yes\nC: maybe\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_129_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_129_11.png"], "question": "There is a moving truck; are there any trucks to the front of it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: some\nB: yes\nC: maybe\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: train\nB: bicycle\nC: car\nD: pedestrian", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_130_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_130_11.png"], "question": "The stopped thing to the back of the stopped bus is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: train\nB: bicycle\nC: car\nD: pedestrian"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: not sure\nC: cannot tell\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_131_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_131_11.png"], "question": "Are any with rider motorcycles visible?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: not sure\nC: cannot tell\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 6\nB: 4\nC: 3\nD: 2", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_132_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_132_11.png"], "question": "How many things are to the front of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 6\nB: 4\nC: 3\nD: 2"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 2\nC: 4\nD: 7", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_133_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_133_11.png"], "question": "What number of moving things are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 2\nC: 4\nD: 7"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: yes\nC: not sure\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_134_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_134_11.png"], "question": "Are there any barriers?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: yes\nC: not sure\nD: maybe"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: yes\nC: not sure\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_135_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_135_11.png"], "question": "Is the status of the truck that is to the front left of the moving car the same as the trailer?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: yes\nC: not sure\nD: no"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: pedestrian\nB: tree\nC: bicycle\nD: traffic light", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_136_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_136_11.png"], "question": "What is the thing that is both to the back right of the moving bus and the back right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: pedestrian\nB: tree\nC: bicycle\nD: traffic light"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: pedestrian\nB: bicycle\nC: tree\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_137_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_137_11.png"], "question": "The moving thing that is both to the back of me and the front of the with rider motorcycle is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: pedestrian\nB: bicycle\nC: tree\nD: car"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: maybe\nC: not sure\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_138_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_138_11.png"], "question": "There is a bus to the front of the parked construction vehicle; is it the same status as the thing that is to the back of the moving truck?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: maybe\nC: not sure\nD: yes"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: truck\nB: car\nC: scooter\nD: bus", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_139_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_139_11.png"], "question": "What is the stopped thing that is both to the front left of the with rider motorcycle and the back of the with rider bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: truck\nB: car\nC: scooter\nD: bus"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: I don’t know\nC: no\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_140_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_140_11.png"], "question": "There is a construction vehicle to the back right of the bus; is it the same status as the motorcycle that is to the back of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: I don’t know\nC: no\nD: maybe"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: maybe\nC: yes\nD: not sure", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_141_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_141_11.png"], "question": "Are there any other things that in the same status as the pedestrian to the back right of the stopped bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: maybe\nC: yes\nD: not sure"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: barrier\nB: fire hydrant\nC: tree\nD: light pole", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_142_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_142_11.png"], "question": "The thing that is to the back right of the moving bus is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: barrier\nB: fire hydrant\nC: tree\nD: light pole"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 3\nB: 5\nC: 9\nD: 12", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_143_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_143_11.png"], "question": "How many moving things are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 3\nB: 5\nC: 9\nD: 12"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: car\nB: tree\nC: bench\nD: bicycle", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_144_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_144_11.png"], "question": "What is the moving thing that is to the front left of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: car\nB: tree\nC: bench\nD: bicycle"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 7\nB: 12\nC: 5\nD: 9", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_145_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_145_11.png"], "question": "What number of other things in the same status as the car that is to the back right of the parked thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 7\nB: 12\nC: 5\nD: 9"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: banana\nB: car\nC: running water\nD: flying bird", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_146_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_146_11.png"], "question": "There is a stopped thing; what is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: banana\nB: car\nC: running water\nD: flying bird"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: maybe\nC: cannot determine\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_147_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_147_11.png"], "question": "There is a motorcycle; does it have the same status as the car that is to the front left of the with rider thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: maybe\nC: cannot determine\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bicycle\nB: pedestrian\nC: crosswalk\nD: traffic light", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_148_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_148_11.png"], "question": "There is a standing pedestrian to the front left of me; what is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bicycle\nB: pedestrian\nC: crosswalk\nD: traffic light"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 7\nC: 3\nD: 10", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_149_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_149_11.png"], "question": "What number of motorcycles are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 7\nC: 3\nD: 10"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: yes\nC: uncertain\nD: probably", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_150_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_150_11.png"], "question": "Is there another construction vehicle of the same status as the truck that is to the front of the moving truck?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: yes\nC: uncertain\nD: probably"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: maybe\nC: no\nD: sometimes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_151_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_151_11.png"], "question": "Are there any moving buss?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: maybe\nC: no\nD: sometimes"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: lane divider\nB: barrier\nC: tree\nD: cone", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_152_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_152_11.png"], "question": "The thing that is to the back right of me and the back right of the construction vehicle is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: lane divider\nB: barrier\nC: tree\nD: cone"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: stationary\nB: disappearing\nC: transforming\nD: moving", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_153_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_153_11.png"], "question": "There is a thing that is to the front left of me; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: stationary\nB: disappearing\nC: transforming\nD: moving"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: no\nC: not sure\nD: maybe", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_154_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_154_11.png"], "question": "There is a bus to the front left of the stopped bus; is it the same status as the motorcycle to the back of the moving bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: no\nC: not sure\nD: maybe"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: no\nC: maybe\nD: uncertain", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_155_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_155_11.png"], "question": "There is a truck; is it the same status as the car to the back right of the stopped truck?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: no\nC: maybe\nD: uncertain"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: traffic cone\nB: tree\nC: hydrant\nD: bench", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_156_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_156_11.png"], "question": "What is the thing that is both to the back right of the parked construction vehicle and the front left of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: traffic cone\nB: tree\nC: hydrant\nD: bench"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: stopped\nB: departed\nC: moving\nD: full", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_157_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_157_11.png"], "question": "What is the status of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: stopped\nB: departed\nC: moving\nD: full"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: pedestrian\nB: trash can\nC: tree\nD: motorcycle", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_158_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_158_11.png"], "question": "The moving thing that is to the front left of the moving bus and the back of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: pedestrian\nB: trash can\nC: tree\nD: motorcycle"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bicycle\nB: bench\nC: car\nD: tree", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_159_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_159_11.png"], "question": "The thing that is to the back of me and the front left of the parked construction vehicle is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bicycle\nB: bench\nC: car\nD: tree"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: parked\nB: moving\nC: accelerating\nD: stopped", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_160_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_160_11.png"], "question": "What is the status of the car that is to the back of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: parked\nB: moving\nC: accelerating\nD: stopped"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: turning\nB: disappearing\nC: stopped\nD: moving", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_161_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_161_11.png"], "question": "There is a bus that is to the front of me; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: turning\nB: disappearing\nC: stopped\nD: moving"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 3\nC: 8\nD: 0", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_162_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_162_11.png"], "question": "There is a bus; how many things are to the front right of it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 3\nC: 8\nD: 0"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: stopped\nB: waiting for passengers\nC: moving\nD: broken down", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_163_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_163_11.png"], "question": "What is the status of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: stopped\nB: waiting for passengers\nC: moving\nD: broken down"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: sometimes\nC: no\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_164_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_164_11.png"], "question": "Are there any not standing pedestrians?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: sometimes\nC: no\nD: yes"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: unable to determine\nC: no\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_165_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_165_11.png"], "question": "Is the status of the truck that is to the front left of the moving car the same as the trailer?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: unable to determine\nC: no\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: car\nB: bicycle\nC: dog\nD: pedestrian", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_166_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_166_11.png"], "question": "What is the moving thing to the back right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: car\nB: bicycle\nC: dog\nD: pedestrian"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: yes\nD: uncertain", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_167_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_167_11.png"], "question": "Is there another bus that has the same status as the car that is to the front left of the stopped construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: uncertain"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving swiftly\nB: stopped\nC: being repaired\nD: broken down", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_168_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_168_11.png"], "question": "The bus that is to the back right of the moving bus is in what status?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving swiftly\nB: stopped\nC: being repaired\nD: broken down"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 10\nC: 7\nD: 3", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_169_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_169_11.png"], "question": "What number of trucks are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 10\nC: 7\nD: 3"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bicycle\nB: tree\nC: car\nD: building", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_170_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_170_11.png"], "question": "The moving thing that is to the front of the construction vehicle and the front left of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bicycle\nB: tree\nC: car\nD: building"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: car\nB: traffic light\nC: bicycle\nD: pedestrian", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_171_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_171_11.png"], "question": "What is the thing that is both to the front left of the stopped bus and the back of the with rider thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: car\nB: traffic light\nC: bicycle\nD: pedestrian"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: missing\nC: broken down\nD: parked", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_172_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_172_11.png"], "question": "What status is the truck to the back right of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: missing\nC: broken down\nD: parked"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: locked up\nB: in transit\nC: damaged\nD: with rider", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_173_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_173_11.png"], "question": "What is the status of the bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: locked up\nB: in transit\nC: damaged\nD: with rider"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 1\nB: 5\nC: 10\nD: 3", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_174_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_174_11.png"], "question": "What number of stopped trucks are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 1\nB: 5\nC: 10\nD: 3"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: sometimes\nB: maybe\nC: yes\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_175_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_175_11.png"], "question": "Are there any moving buss to the back left of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: sometimes\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: moving\nB: broken down\nC: under maintenance\nD: parked", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_176_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_176_11.png"], "question": "What is the status of the bus that is to the front left of the stopped bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: moving\nB: broken down\nC: under maintenance\nD: parked"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bus\nB: bicycle\nC: car\nD: train", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_177_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_177_11.png"], "question": "The moving thing that is both to the back right of the with rider motorcycle and the front of me is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bus\nB: bicycle\nC: car\nD: train"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: boat\nB: bicycle\nC: house\nD: car", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_178_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_178_11.png"], "question": "There is a parked thing that is to the back of me; what is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: boat\nB: bicycle\nC: house\nD: car"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: possibly\nB: no\nC: maybe\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_179_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_179_11.png"], "question": "Are there any not standing pedestrians?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: possibly\nB: no\nC: maybe\nD: yes"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: yes\nC: maybe\nD: uncertain", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_180_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_180_11.png"], "question": "Are any stopped trucks visible?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: yes\nC: maybe\nD: uncertain"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 3\nB: 4\nC: 2\nD: 1", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_181_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_181_11.png"], "question": "What number of things are to the back right of the motorcycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 3\nB: 4\nC: 2\nD: 1"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 7\nB: 5\nC: 2\nD: 3", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_182_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_182_11.png"], "question": "How many other things in the same status as the thing to the front left of the pedestrian?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 7\nB: 5\nC: 2\nD: 3"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: stopped\nB: disappeared\nC: moving\nD: broken down", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_183_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_183_11.png"], "question": "What status is the bus that is to the front of the parked thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: stopped\nB: disappeared\nC: moving\nD: broken down"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bus\nB: bicycle\nC: pedestrian\nD: traffic light", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_184_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_184_11.png"], "question": "The thing that is to the front left of me and the front of the with rider motorcycle is what?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bus\nB: bicycle\nC: pedestrian\nD: traffic light"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: maybe\nC: I do not know\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_185_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_185_11.png"], "question": "Are there any other cars that in the same status as the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: maybe\nC: I do not know\nD: yes"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: car\nB: tree\nC: bicycle\nD: bus", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_186_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_186_11.png"], "question": "There is a stopped thing that is to the front of me; what is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: car\nB: tree\nC: bicycle\nD: bus"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 3\nC: 9\nD: 7", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_187_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_187_11.png"], "question": "What number of other things are there of the same status as the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 3\nC: 9\nD: 7"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: yes\nB: unknown\nC: maybe\nD: no", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_188_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_188_11.png"], "question": "Are there any stopped things to the back right of the trailer?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: yes\nB: unknown\nC: maybe\nD: no"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bus\nB: tree\nC: car\nD: bicycle", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_189_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_189_11.png"], "question": "What is the stopped thing that is to the front left of the with rider motorcycle and the back right of me?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bus\nB: tree\nC: car\nD: bicycle"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 3\nB: 5\nC: 10\nD: 7", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_190_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_190_11.png"], "question": "How many standing pedestrians are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 3\nB: 5\nC: 10\nD: 7"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: broken down\nB: stationary\nC: under repair\nD: moving", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_191_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_191_11.png"], "question": "There is a bus; what status is it?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: broken down\nB: stationary\nC: under repair\nD: moving"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: possibly\nB: unknown\nC: no\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_192_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_192_11.png"], "question": "Does the thing to the front left of the construction vehicle have the same status as the construction vehicle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: possibly\nB: unknown\nC: no\nD: yes"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: stopped\nB: broken down\nC: moving\nD: delayed", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_193_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_193_11.png"], "question": "What is the status of the bus?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: stopped\nB: broken down\nC: moving\nD: delayed"}, "output": {"output_text": "C"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: bicycle\nB: car\nC: airplane\nD: bus", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_194_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_194_11.png"], "question": "What is the stopped thing?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: bicycle\nB: car\nC: airplane\nD: bus"}, "output": {"output_text": "D"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: no\nB: only when moving\nC: sometimes\nD: yes", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_195_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_195_11.png"], "question": "There is a truck to the front of the stopped construction vehicle; does it have the same status as the bicycle?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: no\nB: only when moving\nC: sometimes\nD: yes"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 5\nB: 2\nC: 10\nD: 8", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_196_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_196_11.png"], "question": "How many moving cars are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 5\nB: 2\nC: 10\nD: 8"}, "output": {"output_text": "A"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 3\nB: 5\nC: 50\nD: 12", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_197_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_197_11.png"], "question": "How many other things are in the same status as the truck?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 3\nB: 5\nC: 50\nD: 12"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: 7\nB: 1\nC: 3\nD: 5", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_198_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_198_11.png"], "question": "What number of with rider things are there?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: 7\nB: 1\nC: 3\nD: 5"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "NuScenes_threeD_question_answering", "options": "A: maybe\nB: no\nC: yes\nD: possibly", "visual_input_component": "LiDAR image and natural image", "input": {"input_image_path": ["3D-spatial/threeD_question_answering/threeD_question_answering_199_0.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_1.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_2.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_3.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_4.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_5.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_6.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_7.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_8.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_9.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_10.png", "3D-spatial/threeD_question_answering/threeD_question_answering_199_11.png"], "question": "There is a car to the front of the parked construction vehicle; is its status the same as the construction vehicle to the front of the moving truck?", "context": "Your task is : Given inputs of the 3D information for a scene and a question about the 3D scene (real life), the model aims to output the correct answer. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: possibly"}, "output": {"output_text": "B"}, "task": "threeD_question_answering"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_0_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_0_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_0_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_0_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_0_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_0_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_0_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_0_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.424269, -0.366439, 0.828081], [-0.894198, -0.025281, 0.446957], [-0.142848, -0.930098, -0.338395]] and translation vector: [2.638367, 6.760901, 1.41712], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.432512, -0.37625, 0.819371], [-0.890339, -0.034872, 0.45396], [-0.14223, -0.925862, -0.350073]] and translation vector: [2.640049, 6.763855, 1.420073], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.438239, -0.392392, 0.808687], [-0.889665, -0.061011, 0.452519], [-0.128226, -0.917772, -0.375835]] and translation vector: [2.630422, 6.772062, 1.413381]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_1_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_1_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_1_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_1_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_1_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_1_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_1_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_1_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.156961, 0.257294, -0.953501], [0.986843, 0.002956, -0.161652], [-0.038773, -0.966329, -0.254373]] and translation vector: [1.838324, 1.205476, 1.480452], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.155829, 0.255617, -0.954137], [0.987039, 0.002796, -0.160453], [-0.038347, -0.966774, -0.252739]] and translation vector: [1.83996, 1.205416, 1.474648], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.143517, 0.25546, -0.956108], [0.988424, -0.011031, -0.151315], [-0.049202, -0.966757, -0.25092]] and translation vector: [1.851541, 1.18465, 1.4701]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_2_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_2_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_2_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_2_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_2_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_2_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_2_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_2_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.255252, -0.433184, 0.864406], [-0.966562, 0.137073, -0.216725], [-0.024605, -0.890821, -0.453687]] and translation vector: [1.468232, 3.881342, 1.432686], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.253329, -0.437174, 0.862962], [-0.967015, 0.138948, -0.213484], [-0.026577, -0.888579, -0.457953]] and translation vector: [1.469363, 3.879031, 1.438972], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.261321, -0.422366, 0.867939], [-0.964773, 0.142608, -0.221079], [-0.030398, -0.895137, -0.444754]] and translation vector: [1.471272, 3.88079, 1.429099]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_3_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_3_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_3_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_3_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_3_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_3_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_3_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_3_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.99336, -0.011945, -0.114427], [0.103059, -0.349694, 0.931178], [-0.051137, -0.936788, -0.346141]] and translation vector: [2.948285, 4.432959, 1.460427], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.99314, -0.016022, -0.115825], [0.102925, -0.35027, 0.930977], [-0.055486, -0.936512, -0.346218]] and translation vector: [2.949102, 4.433566, 1.463483], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.994232, -0.017087, -0.105881], [0.09324, -0.350155, 0.93204], [-0.053001, -0.936536, -0.346542]] and translation vector: [2.955784, 4.441682, 1.459117]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_4_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_4_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_4_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_4_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_4_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_4_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_4_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_4_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.30056, -0.511506, 0.805], [-0.953151, 0.130866, -0.272721], [0.034151, -0.849256, -0.526876]] and translation vector: [-0.281614, 2.924112, 1.306122], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.318531, -0.50267, 0.803655], [-0.947336, 0.139247, -0.288383], [0.033055, -0.85319, -0.520551]] and translation vector: [-0.284617, 2.924129, 1.305331], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.357195, -0.491936, 0.793984], [-0.933829, 0.17044, -0.314507], [0.019391, -0.853785, -0.520264]] and translation vector: [-0.283755, 2.908583, 1.310995]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_5_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_5_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_5_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_5_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_5_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_5_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_5_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_5_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.042655, 0.409797, -0.911179], [0.998036, -0.024411, -0.0577], [-0.045888, -0.91185, -0.40795]] and translation vector: [2.423933, 1.356295, 3.282493], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.032887, 0.418885, -0.907444], [0.998611, -0.023628, -0.047098], [-0.041169, -0.907732, -0.417526]] and translation vector: [2.425306, 1.358764, 3.278826], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.041885, 0.387609, -0.920872], [0.998138, -0.024683, -0.055789], [-0.044354, -0.921493, -0.385853]] and translation vector: [2.418078, 1.34298, 3.29873]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_6_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_6_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_6_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_6_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_6_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_6_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_6_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_6_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.241978, -0.427128, 0.871211], [-0.963615, 0.210861, -0.164264], [-0.113543, -0.879261, -0.462611]] and translation vector: [2.164319, 10.11033, 1.716674], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.23973, -0.426819, 0.871983], [-0.964754, 0.205144, -0.16482], [-0.108534, -0.880762, -0.460955]] and translation vector: [2.164643, 10.108889, 1.726434], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.169937, -0.42419, 0.889485], [-0.982379, 0.144175, -0.118927], [-0.077795, -0.894023, -0.441217]] and translation vector: [2.137954, 10.094281, 1.733226]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_7_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_7_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_7_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_7_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_7_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_7_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_7_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_7_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.753053, 0.123809, -0.646206], [0.619922, -0.462608, 0.633791], [-0.220471, -0.877875, -0.42512]] and translation vector: [4.259223, 3.769218, 1.505729], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.760823, 0.125761, -0.636658], [0.611756, -0.466381, 0.638939], [-0.216572, -0.875599, -0.431768]] and translation vector: [4.257898, 3.775608, 1.505422], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.792722, 0.129689, -0.595629], [0.575941, -0.479462, 0.662124], [-0.199711, -0.867927, -0.454772]] and translation vector: [4.245731, 3.788037, 1.507869]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_8_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_8_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_8_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_8_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_8_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_8_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_8_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_8_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.40936, -0.486807, 0.77165], [-0.912164, 0.236459, -0.334729], [-0.019515, -0.840896, -0.540844]] and translation vector: [1.412713, 1.214489, 1.390939], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.417972, -0.487805, 0.766384], [-0.908352, 0.237425, -0.344277], [-0.014019, -0.840045, -0.542336]] and translation vector: [1.411881, 1.212071, 1.390231], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.442659, -0.487865, 0.752356], [-0.896674, 0.245809, -0.368176], [-0.005316, -0.837595, -0.546266]] and translation vector: [1.400211, 1.203382, 1.386707]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_9_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_9_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_9_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_9_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_9_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_9_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_9_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_9_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.999403, 0.004498, 0.03425], [-0.034232, -0.004158, 0.999405], [0.004638, -0.999981, -0.004001]] and translation vector: [2.393484, 5.775056, 1.371464], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.998454, -0.001139, 0.055575], [-0.055569, 0.004857, 0.998443], [-0.001408, -0.999988, 0.004786]] and translation vector: [2.356134, 5.774678, 1.367739], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.989764, 0.002175, 0.142698], [-0.142529, 0.066115, 0.98758], [-0.007287, -0.99781, 0.065748]] and translation vector: [2.255451, 5.785594, 1.33032]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_10_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_10_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_10_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_10_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_10_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_10_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_10_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_10_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.133825, -0.39571, 0.908573], [-0.990975, -0.046263, 0.125813], [-0.007752, -0.91721, -0.398329]] and translation vector: [4.990516, 4.227292, 1.32289], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.168071, -0.388121, 0.906153], [-0.985699, -0.054747, 0.159375], [-0.012247, -0.919981, -0.391772]] and translation vector: [4.987841, 4.19209, 1.32312], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.233014, -0.364692, 0.901501], [-0.972471, -0.085505, 0.216767], [-0.00197, -0.927194, -0.374577]] and translation vector: [4.985941, 4.092797, 1.324644]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_11_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_11_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_11_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_11_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_11_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_11_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_11_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_11_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.993306, 0.029023, -0.111812], [0.110831, -0.512349, 0.851596], [-0.032571, -0.858287, -0.512136]] and translation vector: [2.482234, 1.391135, 1.348064], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.992702, 0.031717, -0.116349], [0.116167, -0.510508, 0.85199], [-0.032374, -0.859288, -0.510467]] and translation vector: [2.48213, 1.388715, 1.34704], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.989452, 0.033499, -0.140936], [0.139029, -0.492892, 0.858911], [-0.040694, -0.869445, -0.49235]] and translation vector: [2.480608, 1.381749, 1.351104]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_12_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_12_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_12_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_12_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_12_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_12_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_12_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_12_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.473704, -0.275929, 0.836342], [-0.879436, -0.198746, 0.432542], [0.046868, -0.940406, -0.336809]] and translation vector: [2.984934, 2.048073, 1.446683], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.466625, -0.271085, 0.841888], [-0.8831, -0.195475, 0.426525], [0.048943, -0.942498, -0.330608]] and translation vector: [2.979092, 2.049407, 1.446378], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.457049, -0.259072, 0.850875], [-0.888339, -0.18058, 0.422191], [0.044273, -0.948827, -0.312678]] and translation vector: [2.973803, 2.044357, 1.455601]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_13_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_13_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_13_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_13_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_13_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_13_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_13_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_13_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.996429, -0.081152, -0.023325], [-0.01119, 0.400709, -0.916137], [0.083693, -0.912604, -0.400187]] and translation vector: [7.365378, 2.610504, 1.343957], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.997089, -0.075007, -0.013671], [-0.016913, 0.392439, -0.919623], [0.074343, -0.916715, -0.392565]] and translation vector: [7.36531, 2.61944, 1.344548], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.997405, -0.064807, -0.031376], [0.004675, 0.376559, -0.926381], [0.071851, -0.924123, -0.375279]] and translation vector: [7.389543, 2.653858, 1.358479]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_14_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_14_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_14_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_14_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_14_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_14_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_14_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_14_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.994136, 0.036629, -0.101745], [0.107123, -0.462198, 0.880283], [-0.014782, -0.88602, -0.463411]] and translation vector: [3.8191, 1.340951, 1.354002], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.994264, 0.034625, -0.101195], [0.105882, -0.452335, 0.885541], [-0.015112, -0.891176, -0.453407]] and translation vector: [3.821174, 1.339834, 1.359098], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.998446, 0.039334, -0.039482], [0.052098, -0.407104, 0.911895], [0.019796, -0.912535, -0.408521]] and translation vector: [3.821787, 1.333543, 1.372052]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_15_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_15_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_15_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_15_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_15_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_15_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_15_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_15_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.844798, -0.442354, 0.301064], [-0.534849, 0.714819, -0.450523], [-0.015916, -0.541624, -0.84047]] and translation vector: [3.085932, 7.995926, 1.934485], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.833593, -0.457276, 0.309873], [-0.552243, 0.702368, -0.449118], [-0.012274, -0.545507, -0.838017]] and translation vector: [3.091993, 8.002051, 1.93396], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.810018, -0.472367, 0.347478], [-0.58602, 0.673547, -0.450461], [-0.02126, -0.56851, -0.822401]] and translation vector: [3.083665, 8.001425, 1.939036]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_16_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_16_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_16_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_16_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_16_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_16_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_16_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_16_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.937403, 0.174354, -0.301457], [0.34768, 0.517889, -0.781607], [0.019845, -0.837491, -0.54609]] and translation vector: [1.513881, 1.499843, 1.388066], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.93698, 0.17766, -0.300842], [0.348874, 0.522274, -0.77815], [0.018876, -0.834067, -0.551341]] and translation vector: [1.515168, 1.503997, 1.385631], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.940806, 0.177334, -0.288855], [0.338804, 0.516688, -0.786286], [0.009813, -0.837607, -0.546185]] and translation vector: [1.517717, 1.515309, 1.387193]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_17_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_17_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_17_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_17_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_17_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_17_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_17_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_17_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.964843, 0.186346, -0.185345], [0.252505, 0.461537, -0.850426], [-0.07293, -0.867329, -0.492364]] and translation vector: [3.779865, 2.337391, 1.461827], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.966867, 0.182729, -0.178267], [0.244986, 0.467845, -0.849178], [-0.071768, -0.864715, -0.49711]] and translation vector: [3.779708, 2.335608, 1.46105], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.975115, 0.169172, -0.14329], [0.209929, 0.496761, -0.842115], [-0.071282, -0.85124, -0.519913]] and translation vector: [3.784041, 2.330569, 1.454727]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_18_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_18_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_18_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_18_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_18_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_18_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_18_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_18_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.399387, 0.327689, -0.856218], [0.9115, 0.041819, -0.409169], [-0.098274, -0.94386, -0.315391]] and translation vector: [4.88233, 2.963563, 1.403722], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.394763, 0.316878, -0.86241], [0.913367, 0.033579, -0.40575], [-0.099614, -0.947872, -0.302681]] and translation vector: [4.88409, 2.965299, 1.400614], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.386874, 0.309114, -0.868779], [0.915474, 0.015736, -0.402069], [-0.110614, -0.950895, -0.289074]] and translation vector: [4.883719, 2.961581, 1.413125]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_19_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_19_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_19_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_19_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_19_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_19_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_19_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_19_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.830629, 0.239867, -0.502514], [0.556756, 0.37214, -0.742654], [0.008867, -0.896647, -0.442658]] and translation vector: [4.849209, 2.614689, 1.447477], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.826514, 0.239564, -0.509396], [0.562778, 0.371773, -0.738286], [0.012512, -0.89688, -0.442097]] and translation vector: [4.848542, 2.612423, 1.449706], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.822193, 0.245879, -0.513364], [0.569134, 0.369775, -0.734406], [0.009254, -0.895997, -0.443965]] and translation vector: [4.848, 2.609138, 1.450893]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_20_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_20_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_20_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_20_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_20_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_20_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_20_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_20_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.815869, 0.244354, -0.524069], [0.578211, -0.336271, 0.743367], [0.005416, -0.909513, -0.415641]] and translation vector: [2.358014, 1.230078, 1.369842], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.817563, 0.244526, -0.521342], [0.575764, -0.332513, 0.746947], [0.009295, -0.910847, -0.41264]] and translation vector: [2.355037, 1.229076, 1.372478], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.827304, 0.233324, -0.511006], [0.561698, -0.330711, 0.758371], [0.007951, -0.914434, -0.404656]] and translation vector: [2.3528, 1.226651, 1.376959]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_21_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_21_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_21_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_21_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_21_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_21_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_21_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_21_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.880278, -0.246293, 0.405524], [-0.473973, 0.417832, -0.775091], [0.021459, -0.874503, -0.484545]] and translation vector: [3.281806, 2.754624, 1.352781], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.883446, -0.241464, 0.401521], [-0.467927, 0.41107, -0.782347], [0.023856, -0.879043, -0.476146]] and translation vector: [3.2823, 2.745028, 1.352692], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.889317, -0.237291, 0.390907], [-0.456246, 0.402627, -0.793556], [0.030913, -0.884073, -0.466326]] and translation vector: [3.299646, 2.724283, 1.356988]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_22_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_22_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_22_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_22_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_22_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_22_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_22_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_22_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.752388, 0.33007, -0.570058], [0.655329, 0.287372, -0.698542], [-0.066749, -0.89915, -0.43252]] and translation vector: [3.814293, 2.583141, 1.394159], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.750374, 0.330815, -0.572276], [0.657793, 0.28836, -0.695813], [-0.065164, -0.89856, -0.433986]] and translation vector: [3.802971, 2.57897, 1.383742], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.774913, 0.365169, -0.515909], [0.625622, 0.32685, -0.708355], [-0.090045, -0.871677, -0.481738]] and translation vector: [3.702851, 2.52357, 1.379531]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_23_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_23_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_23_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_23_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_23_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_23_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_23_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_23_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.764638, 0.028658, -0.643823], [0.64431, -0.055554, 0.762744], [-0.013909, -0.998044, -0.060944]] and translation vector: [3.061982, 3.98913, 1.495508], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.765028, 0.027801, -0.643396], [0.643825, -0.056098, 0.763114], [-0.014878, -0.998038, -0.060816]] and translation vector: [3.064652, 3.991985, 1.487138], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.769869, 0.028995, -0.637544], [0.638044, -0.057257, 0.767869], [-0.01424, -0.997939, -0.06258]] and translation vector: [3.059477, 3.994236, 1.491082]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_24_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_24_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_24_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_24_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_24_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_24_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_24_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_24_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.436119, -0.427186, 0.79203], [-0.89981, 0.218659, -0.377532], [-0.011909, -0.877326, -0.479747]] and translation vector: [1.992302, 3.72193, 1.553249], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.436462, -0.426736, 0.792084], [-0.899636, 0.219226, -0.377618], [-0.012502, -0.877403, -0.47959]] and translation vector: [1.991236, 3.722176, 1.553282], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.436236, -0.428201, 0.791418], [-0.899775, 0.217489, -0.37829], [-0.010141, -0.877122, -0.480161]] and translation vector: [1.989599, 3.72313, 1.552786]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_25_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_25_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_25_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_25_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_25_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_25_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_25_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_25_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.985254, -0.134646, 0.105573], [-0.142287, -0.302097, 0.942599], [-0.095024, -0.94372, -0.3168]] and translation vector: [1.134605, 1.549487, 1.505245], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.985752, -0.13049, 0.106142], [-0.141062, -0.297585, 0.944216], [-0.091624, -0.945736, -0.311752]] and translation vector: [1.131707, 1.551058, 1.506377], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.987724, -0.11535, 0.105339], [-0.134913, -0.289999, 0.94747], [-0.078743, -0.95005, -0.302001]] and translation vector: [1.113611, 1.565945, 1.522577]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_26_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_26_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_26_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_26_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_26_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_26_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_26_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_26_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.567127, -0.123224, 0.81436], [-0.823556, -0.071568, 0.562702], [-0.011056, -0.989795, -0.14207]] and translation vector: [0.249561, 0.967409, 1.634127], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.566682, -0.123694, 0.814599], [-0.82386, -0.07149, 0.562268], [-0.011313, -0.989742, -0.142418]] and translation vector: [0.249762, 0.967631, 1.633273], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.570813, -0.115531, 0.812912], [-0.82106, -0.073224, 0.566127], [-0.005881, -0.990601, -0.136655]] and translation vector: [0.269192, 0.984284, 1.63838]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_27_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_27_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_27_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_27_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_27_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_27_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_27_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_27_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.08083, -0.463089, 0.882618], [-0.994842, 0.091929, -0.042874], [-0.061284, -0.881531, -0.468131]] and translation vector: [4.543997, 3.147744, 1.235262], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.097623, -0.477164, 0.873375], [-0.993778, 0.094019, -0.059714], [-0.05362, -0.873771, -0.483373]] and translation vector: [4.550471, 3.148599, 1.246367], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.130487, -0.461277, 0.877608], [-0.991003, 0.087264, -0.101481], [-0.029773, -0.882954, -0.468514]] and translation vector: [4.556965, 3.161462, 1.2534]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_28_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_28_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_28_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_28_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_28_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_28_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_28_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_28_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.117057, -0.769276, 0.628102], [-0.987232, -0.021336, 0.157855], [-0.108033, -0.638561, -0.761951]] and translation vector: [1.032686, 1.226834, 2.186959], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.111522, -0.769903, 0.628341], [-0.98843, -0.020525, 0.150284], [-0.102807, -0.637831, -0.763284]] and translation vector: [1.037875, 1.232625, 2.186027], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.047902, -0.766247, 0.640758], [-0.996596, 0.006426, 0.082189], [-0.067095, -0.642514, -0.763331]] and translation vector: [1.085053, 1.269848, 2.178721]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_29_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_29_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_29_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_29_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_29_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_29_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_29_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_29_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.824719, -0.175736, 0.537546], [-0.564369, 0.316962, -0.762249], [-0.036427, -0.932015, -0.360584]] and translation vector: [4.397487, 4.054199, 1.411764], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.821778, -0.181799, 0.540028], [-0.568729, 0.319986, -0.757731], [-0.035047, -0.929816, -0.366351]] and translation vector: [4.391561, 4.044915, 1.406417], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.814573, -0.211319, 0.540199], [-0.579135, 0.348873, -0.736811], [-0.032758, -0.913034, -0.406565]] and translation vector: [4.415594, 3.989866, 1.391957]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_30_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_30_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_30_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_30_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_30_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_30_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_30_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_30_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.14018, 0.443083, -0.885453], [0.989985, -0.07783, 0.117782], [-0.016727, -0.893096, -0.449556]] and translation vector: [3.549726, 0.935059, 1.485921], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.140682, 0.443565, -0.885132], [0.989931, -0.077142, 0.11868], [-0.015638, -0.892916, -0.449951]] and translation vector: [3.549777, 0.934132, 1.483108], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.137256, 0.445178, -0.88486], [0.99043, -0.074707, 0.116046], [-0.014444, -0.89232, -0.451172]] and translation vector: [3.545579, 0.936731, 1.483973]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_31_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_31_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_31_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_31_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_31_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_31_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_31_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_31_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.59597, 0.482312, -0.642025], [0.802979, -0.35126, 0.4815], [0.006716, -0.802491, -0.596626]] and translation vector: [3.449961, 1.112515, 1.412234], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.596047, 0.483799, -0.640833], [0.802896, -0.349913, 0.482617], [0.009254, -0.802184, -0.597005]] and translation vector: [3.451157, 1.111087, 1.411899], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.59137, 0.494753, -0.636789], [0.806303, -0.350525, 0.476453], [0.012516, -0.795205, -0.606211]] and translation vector: [3.452706, 1.109482, 1.412867]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_32_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_32_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_32_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_32_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_32_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_32_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_32_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_32_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.112591, -0.547395, 0.829266], [-0.992672, 0.098819, -0.069547], [-0.043877, -0.83102, -0.55451]] and translation vector: [1.18498, 1.814175, 1.496605], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.111637, -0.546351, 0.830083], [-0.992679, 0.100057, -0.067648], [-0.046096, -0.831558, -0.553521]] and translation vector: [1.186424, 1.810214, 1.495373], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.122401, -0.542747, 0.83093], [-0.991535, 0.103412, -0.078512], [-0.043316, -0.833506, -0.55081]] and translation vector: [1.193691, 1.805185, 1.501094]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_33_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_33_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_33_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_33_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_33_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_33_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_33_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_33_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.606497, 0.359513, -0.709163], [0.793947, -0.321582, 0.515978], [-0.042553, -0.875977, -0.480473]] and translation vector: [5.898605, 1.464963, 1.329018], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.603336, 0.358994, -0.712116], [0.79647, -0.316333, 0.515334], [-0.040264, -0.878098, -0.476783]] and translation vector: [5.91512, 1.4588, 1.326343], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.586247, 0.38946, -0.710377], [0.809914, -0.302115, 0.502759], [-0.018811, -0.870085, -0.492543]] and translation vector: [6.035654, 1.433116, 1.31748]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_34_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_34_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_34_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_34_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_34_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_34_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_34_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_34_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.590232, -0.352789, 0.726062], [-0.807221, -0.252962, 0.533296], [-0.004475, -0.900861, -0.434086]] and translation vector: [2.518124, 2.463328, 1.346668], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.586587, -0.358769, 0.726086], [-0.809845, -0.250747, 0.530356], [-0.008212, -0.899117, -0.437632]] and translation vector: [2.520116, 2.462175, 1.344964], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.595628, -0.375207, 0.710244], [-0.80316, -0.264233, 0.533961], [-0.012675, -0.888482, -0.458736]] and translation vector: [2.525984, 2.461792, 1.333971]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_35_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_35_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_35_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_35_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_35_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_35_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_35_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_35_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.934582, -0.143102, 0.325696], [-0.355737, 0.383069, -0.852473], [-0.002774, -0.912568, -0.408916]] and translation vector: [2.694367, 2.483235, 1.465763], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.935747, -0.141154, 0.323191], [-0.352667, 0.379116, -0.85551], [-0.001768, -0.91452, -0.404537]] and translation vector: [2.694351, 2.483417, 1.465522], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.94215, -0.147808, 0.300842], [-0.33486, 0.375166, -0.864361], [0.014894, -0.915098, -0.402958]] and translation vector: [2.702719, 2.477868, 1.47257]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_36_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_36_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_36_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_36_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_36_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_36_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_36_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_36_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.684823, -0.326379, 0.651532], [-0.728707, -0.304485, 0.613413], [-0.001823, -0.894855, -0.446353]] and translation vector: [2.86358, 2.414664, 1.549631], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.684506, -0.325468, 0.652321], [-0.729004, -0.308374, 0.611113], [0.002261, -0.893855, -0.448351]] and translation vector: [2.864701, 2.413023, 1.547001], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.67888, -0.327994, 0.656918], [-0.733931, -0.329441, 0.593981], [0.021593, -0.885375, -0.464376]] and translation vector: [2.877256, 2.417151, 1.541322]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_37_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_37_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_37_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_37_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_37_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_37_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_37_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_37_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.882784, 0.25224, -0.396318], [0.469583, -0.498211, 0.728888], [-0.013595, -0.829554, -0.55826]] and translation vector: [3.463734, 1.394934, 1.262723], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.883097, 0.250738, -0.396574], [0.468931, -0.499833, 0.728197], [-0.015634, -0.829034, -0.558979]] and translation vector: [3.462241, 1.393432, 1.262782], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.878878, 0.250773, -0.405817], [0.476653, -0.496234, 0.725641], [-0.019409, -0.831183, -0.55566]] and translation vector: [3.458656, 1.394662, 1.254618]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_38_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_38_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_38_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_38_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_38_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_38_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_38_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_38_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.752445, 0.275595, -0.598225], [0.657828, -0.35994, 0.661593], [-0.032994, -0.891342, -0.452129]] and translation vector: [2.633805, 2.70906, 1.31733], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.746128, 0.269733, -0.608718], [0.664676, -0.35493, 0.657443], [-0.038718, -0.895136, -0.444108]] and translation vector: [2.667176, 2.689206, 1.310347], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.736878, 0.253582, -0.626664], [0.67323, -0.359496, 0.646161], [-0.061428, -0.89803, -0.435624]] and translation vector: [2.744361, 2.610373, 1.319779]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_39_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_39_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_39_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_39_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_39_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_39_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_39_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_39_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.386761, -0.304254, 0.870543], [-0.920043, 0.191539, -0.34181], [-0.062746, -0.933136, -0.354007]] and translation vector: [2.082368, 4.008438, 1.845888], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.387201, -0.298257, 0.872421], [-0.919947, 0.188025, -0.344013], [-0.061432, -0.935783, -0.347183]] and translation vector: [2.08001, 4.010775, 1.842824], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.376594, -0.325714, 0.867229], [-0.924884, 0.185353, -0.332016], [-0.052601, -0.927122, -0.371051]] and translation vector: [2.082613, 4.009402, 1.837637]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_40_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_40_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_40_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_40_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_40_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_40_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_40_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_40_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.484778, 0.389748, -0.782998], [0.874059, -0.248441, 0.417491], [-0.031813, -0.886777, -0.461102]] and translation vector: [2.948564, 2.712566, 1.480667], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.484062, 0.388161, -0.784229], [0.874419, -0.248162, 0.416902], [-0.03279, -0.887551, -0.459542]] and translation vector: [2.949191, 2.711738, 1.477649], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.467232, 0.39177, -0.792597], [0.88347, -0.241629, 0.401368], [-0.034271, -0.887768, -0.459014]] and translation vector: [2.947397, 2.72527, 1.480424]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_41_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_41_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_41_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_41_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_41_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_41_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_41_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_41_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.493838, -0.420518, 0.76111], [-0.864926, -0.147366, 0.479777], [-0.089593, -0.895236, -0.436493]] and translation vector: [0.736944, 2.108944, 1.402726], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.487676, -0.423405, 0.763479], [-0.869284, -0.154634, 0.469504], [-0.080731, -0.892646, -0.443471]] and translation vector: [0.733117, 2.095654, 1.39687], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.480924, -0.423346, 0.767783], [-0.872629, -0.146192, 0.465989], [-0.085031, -0.894095, -0.439732]] and translation vector: [0.701425, 2.057617, 1.397946]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_42_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_42_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_42_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_42_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_42_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_42_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_42_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_42_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.874867, -0.0675, 0.479638], [-0.482919, 0.197999, -0.852987], [-0.037391, -0.977875, -0.205819]] and translation vector: [2.397274, 1.722858, 1.486845], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.874077, -0.063653, 0.4816], [-0.484123, 0.196153, -0.852731], [-0.040189, -0.978505, -0.202269]] and translation vector: [2.402604, 1.721845, 1.489477], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.887879, -0.058916, 0.456289], [-0.458188, 0.203011, -0.865362], [-0.041648, -0.977402, -0.207244]] and translation vector: [2.446714, 1.689918, 1.489633]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_43_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_43_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_43_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_43_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_43_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_43_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_43_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_43_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.993805, -0.057016, 0.095394], [-0.110597, -0.423109, 0.899304], [-0.010913, -0.904283, -0.426794]] and translation vector: [3.282054, 2.568905, 1.512321], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.993106, -0.061381, 0.099861], [-0.116562, -0.427194, 0.896615], [-0.012375, -0.902074, -0.431404]] and translation vector: [3.283498, 2.568158, 1.509645], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.991697, -0.07473, 0.104657], [-0.127453, -0.462749, 0.877279], [-0.017129, -0.883334, -0.468431]] and translation vector: [3.294037, 2.566846, 1.501968]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_44_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_44_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_44_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_44_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_44_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_44_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_44_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_44_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.672393, -0.274439, 0.687438], [-0.739855, -0.221079, 0.635404], [-0.022402, -0.935846, -0.351697]] and translation vector: [3.802358, 2.110255, 1.494557], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.672432, -0.275262, 0.687071], [-0.739825, -0.222066, 0.635095], [-0.022242, -0.93537, -0.35297]] and translation vector: [3.806542, 2.108163, 1.497405], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.662943, -0.279413, 0.694575], [-0.748414, -0.223073, 0.624593], [-0.019579, -0.933899, -0.357001]] and translation vector: [3.809607, 2.112622, 1.492454]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_45_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_45_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_45_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_45_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_45_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_45_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_45_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_45_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.187285, -0.627824, 0.755488], [-0.982305, 0.118515, -0.145025], [0.001514, -0.76928, -0.63891]] and translation vector: [1.001752, 1.17634, 1.437838], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.187139, -0.630563, 0.75324], [-0.982328, 0.117514, -0.14568], [0.003345, -0.767191, -0.64141]] and translation vector: [1.00191, 1.178201, 1.437088], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.182531, -0.636948, 0.748986], [-0.983189, 0.114531, -0.142208], [0.004797, -0.762352, -0.647145]] and translation vector: [1.004145, 1.176443, 1.437678]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_46_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_46_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_46_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_46_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_46_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_46_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_46_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_46_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.977181, 0.077241, -0.197866], [0.211774, -0.426158, 0.879512], [-0.016388, -0.901345, -0.432791]] and translation vector: [0.977323, 0.877303, 1.40232], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.979446, 0.063797, -0.19135], [0.200663, -0.404476, 0.892263], [-0.020472, -0.912321, -0.408965]] and translation vector: [0.961423, 0.875672, 1.418643], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.983838, 0.068482, -0.165447], [0.178902, -0.337078, 0.924323], [0.007531, -0.938983, -0.343882]] and translation vector: [0.935081, 0.882589, 1.453845]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_47_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_47_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_47_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_47_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_47_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_47_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_47_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_47_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.205964, -0.505778, 0.837716], [-0.978495, 0.11627, -0.170378], [-0.011228, -0.854792, -0.518849]] and translation vector: [2.901534, 4.292832, 1.280844], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.204012, -0.504726, 0.838827], [-0.978841, 0.118998, -0.166463], [-0.0158, -0.855039, -0.518324]] and translation vector: [2.909629, 4.290413, 1.285823], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.169049, -0.47943, 0.861144], [-0.985403, 0.100042, -0.137744], [-0.020112, -0.871859, -0.489344]] and translation vector: [2.918062, 4.255744, 1.296137]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_48_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_48_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_48_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_48_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_48_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_48_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_48_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_48_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.810147, -0.229725, 0.539341], [-0.586224, 0.314131, -0.746769], [0.002128, -0.921167, -0.389162]] and translation vector: [3.108561, 2.950706, 1.466118], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.798041, -0.241673, 0.552019], [-0.602539, 0.306626, -0.736836], [0.00881, -0.920638, -0.390318]] and translation vector: [3.094201, 2.939754, 1.46817], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.730942, -0.298846, 0.613526], [-0.681648, 0.276413, -0.677461], [0.03287, -0.913393, -0.40575]] and translation vector: [3.008661, 2.892656, 1.463078]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_49_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_49_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_49_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_49_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_49_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_49_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_49_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_49_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.983299, 0.047874, -0.175588], [0.180439, -0.382417, 0.9062], [-0.023764, -0.922749, -0.384668]] and translation vector: [2.208684, 3.483128, 1.468268], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.982577, 0.045136, -0.18029], [0.183889, -0.376806, 0.907856], [-0.026957, -0.925192, -0.378541]] and translation vector: [2.211137, 3.481059, 1.465482], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.983986, 0.057121, -0.168843], [0.177826, -0.379389, 0.907988], [-0.012192, -0.923472, -0.383472]] and translation vector: [2.214237, 3.490379, 1.461581]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_50_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_50_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_50_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_50_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_50_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_50_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_50_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_50_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.053762, 0.423971, -0.904079], [0.99709, -0.071809, 0.025618], [-0.05406, -0.902825, -0.426597]] and translation vector: [3.696534, 7.381392, 1.65485], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.059051, 0.424044, -0.903714], [0.996629, -0.076693, 0.029136], [-0.056954, -0.902388, -0.427143]] and translation vector: [3.693501, 7.384472, 1.654036], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.076295, 0.430516, -0.899353], [0.995602, -0.082082, 0.045168], [-0.054375, -0.898843, -0.434884]] and translation vector: [3.686877, 7.38459, 1.650219]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_51_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_51_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_51_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_51_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_51_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_51_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_51_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_51_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.079656, -0.319192, 0.944337], [-0.994012, 0.096527, -0.051219], [-0.074805, -0.942762, -0.324969]] and translation vector: [4.3352, 2.935251, 1.464921], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.08136, -0.319768, 0.943996], [-0.993796, 0.098086, -0.052427], [-0.075828, -0.942405, -0.325765]] and translation vector: [4.335558, 2.933583, 1.460394], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.082648, -0.359045, 0.929654], [-0.993327, 0.104973, -0.047767], [-0.080438, -0.927398, -0.365325]] and translation vector: [4.342546, 2.934833, 1.439448]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_52_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_52_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_52_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_52_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_52_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_52_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_52_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_52_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.299058, 0.37418, -0.877812], [0.95368, -0.085842, 0.288314], [0.032528, -0.923375, -0.38252]] and translation vector: [3.908031, 4.993837, 1.41318], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.301871, 0.365699, -0.880419], [0.952911, -0.087746, 0.290279], [0.028901, -0.926588, -0.374966]] and translation vector: [3.903484, 4.991583, 1.422828], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.301255, 0.344295, -0.889217], [0.952977, -0.076566, 0.293211], [0.032867, -0.935734, -0.351171]] and translation vector: [3.913385, 4.973511, 1.425571]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_53_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_53_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_53_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_53_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_53_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_53_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_53_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_53_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.769532, -0.429513, 0.472588], [-0.615738, -0.302759, 0.727464], [-0.169375, -0.850797, -0.49745]] and translation vector: [2.184386, 2.253813, 1.283805], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.76638, -0.428136, 0.478917], [-0.620171, -0.298738, 0.725357], [-0.167481, -0.85291, -0.494464]] and translation vector: [2.185226, 2.257666, 1.286817], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.752434, -0.422477, 0.505328], [-0.641308, -0.294924, 0.708339], [-0.150223, -0.857049, -0.492848]] and translation vector: [2.203988, 2.240772, 1.285116]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_54_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_54_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_54_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_54_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_54_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_54_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_54_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_54_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.255196, -0.436856, 0.862573], [-0.966393, 0.143834, -0.213066], [-0.030988, -0.887958, -0.45888]] and translation vector: [1.734999, 0.744851, 1.432124], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.254375, -0.435236, 0.863634], [-0.966628, 0.142475, -0.21291], [-0.03038, -0.888972, -0.456953]] and translation vector: [1.735377, 0.747301, 1.433656], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.252592, -0.430397, 0.866577], [-0.967061, 0.14143, -0.211638], [-0.031471, -0.891491, -0.451944]] and translation vector: [1.738514, 0.752667, 1.434948]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_55_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_55_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_55_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_55_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_55_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_55_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_55_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_55_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.721847, -0.019511, -0.691778], [0.690918, -0.036893, 0.721991], [-0.039608, -0.999129, -0.013151]] and translation vector: [1.871862, 0.815296, 1.594356], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.723033, -0.022358, -0.690452], [0.689637, -0.034974, 0.723311], [-0.04032, -0.999138, -0.009869]] and translation vector: [1.872181, 0.815734, 1.596287], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.722407, -0.014829, -0.691309], [0.690381, -0.040572, 0.722307], [-0.038759, -0.999067, -0.019072]] and translation vector: [1.866769, 0.812653, 1.587453]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_56_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_56_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_56_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_56_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_56_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_56_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_56_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_56_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.15851, 0.420096, -0.893529], [0.981106, -0.034663, -0.190342], [-0.110934, -0.906817, -0.406664]] and translation vector: [4.004256, 0.910349, 2.578562], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.153085, 0.419732, -0.894645], [0.982322, -0.034068, -0.184071], [-0.107739, -0.907009, -0.407097]] and translation vector: [4.005316, 0.908549, 2.574668], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.128813, 0.432758, -0.89226], [0.986418, -0.036555, -0.160137], [-0.101917, -0.900769, -0.422171]] and translation vector: [4.005799, 0.894308, 2.560097]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_57_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_57_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_57_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_57_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_57_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_57_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_57_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_57_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.974605, -0.106498, 0.196986], [-0.223762, -0.428932, 0.875185], [-0.008712, -0.897037, -0.44187]] and translation vector: [2.006689, 0.552817, 1.711334], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.976991, -0.101609, 0.187523], [-0.213093, -0.42809, 0.878254], [-0.008962, -0.898006, -0.439892]] and translation vector: [2.014877, 0.551422, 1.700123], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.983342, -0.080889, 0.162776], [-0.181747, -0.450774, 0.87394], [0.002683, -0.888966, -0.457967]] and translation vector: [1.906067, 0.734394, 1.70234]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_58_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_58_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_58_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_58_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_58_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_58_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_58_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_58_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.677945, 0.409221, -0.610679], [0.735109, 0.38004, -0.561413], [0.00234, -0.829523, -0.558468]] and translation vector: [3.092599, 2.044437, 1.437429], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.678782, 0.408186, -0.610442], [0.734335, 0.380383, -0.562193], [0.002723, -0.829875, -0.557943]] and translation vector: [3.0892, 2.043949, 1.440375], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.676872, 0.407734, -0.61286], [0.736083, 0.380637, -0.559729], [0.005057, -0.829981, -0.557769]] and translation vector: [3.08962, 2.045413, 1.436176]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_59_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_59_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_59_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_59_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_59_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_59_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_59_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_59_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.207705, 0.494542, -0.843971], [0.97739, -0.069996, 0.199524], [0.039599, -0.866331, -0.497898]] and translation vector: [4.53083, 2.291093, 1.52739], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.209269, 0.494574, -0.843566], [0.977066, -0.071037, 0.200739], [0.039356, -0.866228, -0.498097]] and translation vector: [4.529976, 2.291335, 1.526507], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.196766, 0.49564, -0.845946], [0.979799, -0.067948, 0.18809], [0.035744, -0.865866, -0.498997]] and translation vector: [4.530453, 2.296434, 1.524226]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_60_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_60_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_60_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_60_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_60_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_60_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_60_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_60_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.956223, -0.170898, 0.237554], [-0.292595, -0.544035, 0.786393], [-0.005155, -0.821474, -0.570223]] and translation vector: [1.275326, 2.834272, 1.3185], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.956815, -0.170774, 0.235249], [-0.290631, -0.544392, 0.786875], [-0.00631, -0.821263, -0.570514]] and translation vector: [1.276568, 2.833979, 1.318089], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.956011, -0.167954, 0.240486], [-0.293328, -0.545359, 0.785202], [-0.000727, -0.821203, -0.570635]] and translation vector: [1.277841, 2.834386, 1.31762]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_61_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_61_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_61_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_61_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_61_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_61_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_61_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_61_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.928108, -0.125197, 0.35063], [-0.371823, 0.3599, -0.855699], [-0.019061, -0.924553, -0.380577]] and translation vector: [5.296664, 4.137775, 1.856988], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.930637, -0.119308, 0.34595], [-0.365378, 0.355543, -0.860284], [-0.020361, -0.927014, -0.374474]] and translation vector: [5.29653, 4.126579, 1.856014], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.952426, -0.118849, 0.280641], [-0.304767, 0.367704, -0.878584], [0.001226, -0.922317, -0.386432]] and translation vector: [5.320154, 4.099401, 1.857875]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_62_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_62_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_62_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_62_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_62_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_62_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_62_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_62_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.86482, -0.183466, 0.467362], [-0.501092, -0.256948, 0.826368], [-0.031523, -0.948851, -0.314147]] and translation vector: [3.012278, 2.022242, 1.442339], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.863867, -0.189194, 0.466839], [-0.502557, -0.260784, 0.824274], [-0.034203, -0.946677, -0.320364]] and translation vector: [3.015002, 2.018446, 1.436262], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.859994, -0.189108, 0.473971], [-0.509792, -0.276775, 0.81456], [-0.022856, -0.942143, -0.33443]] and translation vector: [3.018664, 2.017763, 1.427395]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_63_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_63_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_63_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_63_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_63_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_63_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_63_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_63_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.951558, 0.16536, -0.259218], [0.307283, -0.481983, 0.820531], [0.010744, -0.860436, -0.509446]] and translation vector: [2.919862, 3.428013, 1.521081], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.951326, 0.167996, -0.258374], [0.307875, -0.4803, 0.821295], [0.013877, -0.860866, -0.508643]] and translation vector: [2.920042, 3.428186, 1.518811], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.948369, 0.180855, -0.260555], [0.316485, -0.485614, 0.814872], [0.020845, -0.85526, -0.517779]] and translation vector: [2.906806, 3.429147, 1.512746]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_64_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_64_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_64_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_64_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_64_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_64_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_64_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_64_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.802837, 0.056561, -0.593509], [0.596192, 0.071654, -0.799638], [-0.002701, -0.995825, -0.091248]] and translation vector: [2.583219, 4.008804, 1.439254], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.802466, 0.056012, -0.594063], [0.59669, 0.070227, -0.799393], [-0.003056, -0.995957, -0.089777]] and translation vector: [2.583684, 4.008714, 1.434935], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.802651, 0.061565, -0.593263], [0.596422, 0.0734, -0.799308], [-0.005664, -0.995401, -0.095633]] and translation vector: [2.580812, 4.010173, 1.435745]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_65_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_65_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_65_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_65_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_65_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_65_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_65_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_65_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.355681, -0.20797, 0.911175], [-0.934036, 0.113197, -0.338769], [-0.032689, -0.971563, -0.234514]] and translation vector: [0.539195, 4.841905, 1.636959], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.354881, -0.205091, 0.912139], [-0.934375, 0.110848, -0.338608], [-0.031664, -0.972446, -0.230969]] and translation vector: [0.533365, 4.84225, 1.627512], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.357394, -0.22244, 0.907078], [-0.933778, 0.10396, -0.34242], [-0.018132, -0.969388, -0.244864]] and translation vector: [0.528036, 4.836335, 1.624936]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_66_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_66_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_66_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_66_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_66_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_66_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_66_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_66_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.566304, -0.590941, 0.574533], [-0.823945, 0.423135, -0.376925], [-0.020365, -0.686838, -0.726526]] and translation vector: [2.143516, 1.760119, 1.343188], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.561614, -0.596242, 0.57366], [-0.827171, 0.420904, -0.372329], [-0.019457, -0.683619, -0.729579]] and translation vector: [2.147258, 1.761594, 1.344016], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.547252, -0.609389, 0.573725], [-0.836861, 0.409368, -0.363431], [-0.013394, -0.679017, -0.734001]] and translation vector: [2.154856, 1.762344, 1.343807]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_67_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_67_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_67_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_67_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_67_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_67_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_67_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_67_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.848489, -0.131122, 0.512712], [-0.527579, 0.133483, -0.838954], [0.041567, -0.982339, -0.182436]] and translation vector: [2.702568, 1.718074, 1.602473], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.851363, -0.128939, 0.508484], [-0.523333, 0.142037, -0.840207], [0.036112, -0.981428, -0.188403]] and translation vector: [2.706553, 1.721294, 1.602035], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.862925, -0.138489, 0.485985], [-0.504369, 0.176659, -0.845224], [0.031201, -0.974481, -0.222293]] and translation vector: [2.716626, 1.723908, 1.586826]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_68_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_68_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_68_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_68_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_68_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_68_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_68_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_68_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.205292, 0.226186, -0.952205], [0.97316, -0.150555, 0.174048], [-0.103992, -0.962379, -0.251024]] and translation vector: [4.876985, 2.837537, 1.671042], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.210488, 0.22021, -0.952472], [0.971775, -0.153305, 0.17931], [-0.106533, -0.96333, -0.246263]] and translation vector: [4.87733, 2.840179, 1.675237], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.247756, 0.187443, -0.950517], [0.962582, -0.158806, 0.219585], [-0.109788, -0.969353, -0.219774]] and translation vector: [4.877867, 2.827038, 1.675608]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_69_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_69_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_69_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_69_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_69_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_69_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_69_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_69_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.52463, -0.231347, 0.819293], [-0.850589, 0.102279, -0.515789], [0.03553, -0.96748, -0.25044]] and translation vector: [5.897326, 2.792535, 1.553822], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.52763, -0.228151, 0.818263], [-0.84888, 0.105585, -0.517933], [0.03177, -0.967884, -0.249382]] and translation vector: [5.897463, 2.790525, 1.551499], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.541576, -0.222735, 0.810608], [-0.840076, 0.107703, -0.53167], [0.031116, -0.968911, -0.245444]] and translation vector: [5.894893, 2.788883, 1.558074]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_70_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_70_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_70_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_70_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_70_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_70_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_70_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_70_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.988959, -0.006087, -0.148062], [0.148117, 0.009943, 0.98892], [-0.004548, -0.999932, 0.010735]] and translation vector: [3.911582, 2.672538, 1.565046], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.987297, -0.007995, -0.158684], [0.158774, 0.012251, 0.987239], [-0.005949, -0.999893, 0.013365]] and translation vector: [3.955948, 2.679338, 1.574419], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.992697, -0.03521, -0.115384], [0.116446, 0.029785, 0.99275], [-0.031518, -0.998936, 0.033668]] and translation vector: [3.907376, 2.643518, 1.623414]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_71_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_71_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_71_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_71_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_71_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_71_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_71_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_71_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.999494, 0.005595, 0.031322], [-0.029883, 0.172936, -0.98448], [-0.010925, -0.984917, -0.172681]] and translation vector: [6.687301, 5.436423, 1.742894], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.999393, 0.00615, 0.034285], [-0.032681, 0.175053, -0.984017], [-0.012053, -0.98454, -0.174746]] and translation vector: [6.681215, 5.427393, 1.75699], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.999512, 0.015203, 0.027277], [-0.02448, 0.160854, -0.986675], [-0.019388, -0.986861, -0.160403]] and translation vector: [6.678608, 5.424335, 1.758175]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_72_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_72_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_72_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_72_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_72_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_72_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_72_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_72_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.991592, 0.052224, -0.118397], [0.1292, -0.348306, 0.928435], [0.007248, -0.935925, -0.352124]] and translation vector: [2.177373, 2.142725, 1.46728], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.992093, 0.047571, -0.11614], [0.125441, -0.346386, 0.929667], [0.003996, -0.936885, -0.349615]] and translation vector: [2.181058, 2.142908, 1.465582], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.99009, 0.041581, -0.13414], [0.14016, -0.352521, 0.925248], [-0.008815, -0.93488, -0.354856]] and translation vector: [2.196626, 2.148474, 1.466161]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_73_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_73_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_73_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_73_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_73_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_73_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_73_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_73_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.132001, -0.567775, 0.812532], [-0.991224, 0.069667, -0.112349], [0.007182, -0.820231, -0.571988]] and translation vector: [2.407685, 4.450429, 1.359714], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.130918, -0.563466, 0.8157], [-0.991376, 0.069526, -0.111087], [0.005882, -0.823209, -0.567709]] and translation vector: [2.40989, 4.444678, 1.359228], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.104614, -0.562754, 0.819978], [-0.994308, 0.042438, -0.097729], [0.020199, -0.825534, -0.563991]] and translation vector: [2.433079, 4.433616, 1.362504]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_74_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_74_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_74_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_74_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_74_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_74_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_74_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_74_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.877021, 0.121711, -0.464779], [0.46491, 0.459041, -0.75706], [0.12121, -0.880038, -0.459173]] and translation vector: [3.922419, 3.230202, 1.747047], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.876473, 0.11975, -0.466322], [0.465798, 0.455895, -0.758415], [0.121773, -0.881941, -0.455359]] and translation vector: [3.923546, 3.227255, 1.740959], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.862892, 0.148989, -0.482928], [0.494148, 0.449135, -0.744376], [0.105996, -0.880954, -0.461178]] and translation vector: [3.903725, 3.133858, 1.745573]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_75_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_75_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_75_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_75_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_75_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_75_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_75_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_75_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.515401, -0.339121, 0.786994], [-0.847541, -0.337435, 0.40965], [0.126638, -0.878143, -0.461333]] and translation vector: [4.776819, 1.138867, 1.280463], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.495978, -0.33911, 0.799381], [-0.859276, -0.324304, 0.395565], [0.125103, -0.88308, -0.452237]] and translation vector: [4.773187, 1.14016, 1.284317], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.481026, -0.30789, 0.820864], [-0.867671, -0.301264, 0.395457], [0.125539, -0.902465, -0.412064]] and translation vector: [4.757284, 1.147171, 1.295988]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_76_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_76_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_76_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_76_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_76_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_76_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_76_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_76_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.623567, 0.536294, -0.568817], [0.781209, -0.455034, 0.427384], [-0.029628, -0.710867, -0.702702]] and translation vector: [1.790477, 1.816361, 1.229059], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.636074, 0.528408, -0.562313], [0.771074, -0.462894, 0.437235], [-0.029252, -0.711698, -0.701876]] and translation vector: [1.794875, 1.819226, 1.230937], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.674924, 0.4822, -0.558534], [0.737532, -0.464309, 0.49037], [-0.022876, -0.7429, -0.669012]] and translation vector: [1.813084, 1.825686, 1.243736]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_77_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_77_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_77_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_77_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_77_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_77_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_77_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_77_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.999847, -0.004634, 0.01689], [-0.017397, -0.374134, 0.927211], [0.002023, -0.927363, -0.374157]] and translation vector: [3.310194, 3.16458, 1.506432], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.999774, -0.010896, 0.018284], [-0.021018, -0.369724, 0.928904], [-0.003361, -0.929078, -0.369869]] and translation vector: [3.316631, 3.168954, 1.519748], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.999711, -0.01062, 0.02156], [-0.023945, -0.363153, 0.931422], [-0.002062, -0.931669, -0.363302]] and translation vector: [3.313389, 3.184942, 1.522696]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_78_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_78_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_78_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_78_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_78_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_78_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_78_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_78_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.573165, 0.475287, -0.667521], [0.819422, -0.337921, 0.462988], [-0.005517, -0.81235, -0.583144]] and translation vector: [4.230747, 1.597944, 1.425469], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.580595, 0.472456, -0.663095], [0.814187, -0.339873, 0.470729], [-0.002969, -0.813186, -0.581996]] and translation vector: [4.228813, 1.597838, 1.42741], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.590926, 0.466068, -0.658474], [0.806725, -0.340048, 0.483283], [0.00133, -0.816791, -0.576932]] and translation vector: [4.230728, 1.601094, 1.427952]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_79_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_79_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_79_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_79_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_79_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_79_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_79_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_79_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.246516, -0.470365, 0.847341], [-0.959136, 0.006886, 0.282862], [-0.138884, -0.882445, -0.449446]] and translation vector: [3.043058, 2.955299, 1.551102], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.243276, -0.470143, 0.8484], [-0.960213, 0.006937, 0.279182], [-0.13714, -0.882563, -0.44975]] and translation vector: [3.042024, 2.954946, 1.550413], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.220837, -0.468715, 0.855299], [-0.967151, 0.007957, 0.254077], [-0.125896, -0.883313, -0.451561]] and translation vector: [3.035462, 2.949861, 1.549809]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_80_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_80_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_80_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_80_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_80_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_80_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_80_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_80_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.998134, -0.025826, -0.055325], [0.04389, 0.326427, -0.944203], [0.042444, -0.94487, -0.324684]] and translation vector: [2.355182, 2.984659, 1.395898], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.998605, -0.022906, -0.047579], [0.037628, 0.323493, -0.945482], [0.037048, -0.945953, -0.32218]] and translation vector: [2.345251, 2.98743, 1.391141], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.998425, -0.028903, -0.048087], [0.035665, 0.334665, -0.941662], [0.04331, -0.941894, -0.333107]] and translation vector: [2.317253, 2.991597, 1.388493]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_81_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_81_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_81_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_81_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_81_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_81_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_81_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_81_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.176261, -0.039155, 0.983564], [-0.983722, -0.028492, -0.177423], [0.03497, -0.998827, -0.033496]] and translation vector: [3.054739, 2.437738, 1.503838], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.18153, -0.048874, 0.98217], [-0.982778, -0.026092, -0.182941], [0.034567, -0.998464, -0.043296]] and translation vector: [3.061021, 2.450195, 1.498681], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.163045, -0.034334, 0.986021], [-0.986093, -0.02694, -0.163995], [0.032194, -0.999047, -0.029464]] and translation vector: [3.066704, 2.437577, 1.507359]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_82_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_82_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_82_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_82_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_82_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_82_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_82_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_82_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.853196, -0.330732, 0.403328], [-0.517406, -0.438892, 0.734619], [-0.065945, -0.835458, -0.545584]] and translation vector: [2.734716, 6.775187, 1.412962], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.853022, -0.336855, 0.398601], [-0.516617, -0.436898, 0.736361], [-0.0739, -0.834056, -0.546708]] and translation vector: [2.728871, 6.767794, 1.411126], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.851443, -0.340578, 0.398812], [-0.519517, -0.44372, 0.730216], [-0.071735, -0.828927, -0.554738]] and translation vector: [2.722152, 6.743406, 1.39829]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_83_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_83_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_83_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_83_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_83_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_83_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_83_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_83_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.954506, 0.05554, -0.292973], [0.288831, -0.41644, 0.862064], [-0.074127, -0.907465, -0.413536]] and translation vector: [2.66447, 1.005586, 1.476015], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.956668, 0.052296, -0.286448], [0.280824, -0.425753, 0.860158], [-0.076973, -0.903327, -0.42199]] and translation vector: [2.657996, 1.004761, 1.470821], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.966986, 0.054866, -0.248854], [0.248498, -0.419376, 0.873139], [-0.056458, -0.906153, -0.419165]] and translation vector: [2.617702, 1.004602, 1.502791]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_84_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_84_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_84_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_84_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_84_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_84_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_84_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_84_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.804945, -0.278842, 0.523748], [-0.593014, 0.407765, -0.694307], [-0.019964, -0.869468, -0.493585]] and translation vector: [4.871809, 2.494869, 1.402737], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.804444, -0.274614, 0.526742], [-0.593612, 0.404842, -0.695506], [-0.022252, -0.872176, -0.488687]] and translation vector: [4.863627, 2.491699, 1.400121], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.82218, -0.26485, 0.503859], [-0.568804, 0.416386, -0.709285], [-0.021946, -0.869757, -0.492992]] and translation vector: [4.864128, 2.487759, 1.4037]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_85_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_85_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_85_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_85_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_85_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_85_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_85_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_85_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.330673, -0.328207, 0.884837], [-0.942686, -0.070458, 0.326157], [-0.044703, -0.941975, -0.332694]] and translation vector: [3.753276, 4.481459, 1.345242], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.306694, -0.326667, 0.893995], [-0.950878, -0.063631, 0.302957], [-0.04208, -0.942995, -0.330136]] and translation vector: [3.754864, 4.497246, 1.34429], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.246991, -0.34493, 0.905549], [-0.96808, -0.046739, 0.246244], [-0.042613, -0.937464, -0.345464]] and translation vector: [3.754345, 4.564482, 1.352383]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_86_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_86_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_86_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_86_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_86_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_86_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_86_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_86_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.119369, -0.433868, 0.893034], [-0.990549, 0.113242, -0.077387], [-0.067553, -0.893832, -0.443285]] and translation vector: [3.407035, 4.679209, 1.397058], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.120544, -0.432859, 0.893366], [-0.990306, 0.115004, -0.077902], [-0.06902, -0.894096, -0.442526]] and translation vector: [3.401289, 4.681283, 1.397495], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.162977, -0.454909, 0.875498], [-0.983038, 0.15052, -0.104785], [-0.084112, -0.877725, -0.471725]] and translation vector: [3.342063, 4.674428, 1.399173]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_87_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_87_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_87_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_87_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_87_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_87_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_87_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_87_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.767458, -0.265442, 0.583565], [-0.640543, 0.35536, -0.680752], [-0.026676, -0.896248, -0.442751]] and translation vector: [3.343537, 3.697402, 1.375352], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.780866, -0.263741, 0.566294], [-0.624403, 0.357431, -0.694525], [-0.019236, -0.895926, -0.443786]] and translation vector: [3.344022, 3.709659, 1.376654], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.822146, -0.253316, 0.509811], [-0.569276, 0.364542, -0.736908], [0.000823, -0.896069, -0.443913]] and translation vector: [3.329204, 3.745763, 1.383552]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_88_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_88_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_88_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_88_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_88_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_88_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_88_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_88_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.612656, -0.411508, 0.674769], [-0.789543, 0.280105, -0.546043], [0.035694, -0.867296, -0.496511]] and translation vector: [1.897828, 2.372103, 1.388776], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.615876, -0.406578, 0.674826], [-0.787242, 0.284147, -0.547275], [0.03076, -0.868305, -0.495075]] and translation vector: [1.892345, 2.36762, 1.390764], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.607068, -0.416916, 0.676498], [-0.79419, 0.289362, -0.534352], [0.027027, -0.861656, -0.506773]] and translation vector: [1.87873, 2.3614, 1.391886]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_89_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_89_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_89_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_89_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_89_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_89_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_89_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_89_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.221984, 0.421429, -0.879273], [0.97466, 0.121427, -0.187867], [0.027595, -0.898695, -0.437705]] and translation vector: [3.155292, 0.483793, 1.35371], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.224547, 0.416482, -0.880978], [0.973822, 0.128715, -0.187361], [0.035363, -0.899986, -0.434482]] and translation vector: [3.157119, 0.483672, 1.354178], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.215665, 0.423756, -0.879727], [0.975658, 0.130183, -0.176474], [0.039743, -0.896373, -0.441517]] and translation vector: [3.155366, 0.486351, 1.353433]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_90_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_90_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_90_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_90_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_90_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_90_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_90_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_90_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.955421, 0.119616, -0.269932], [0.295248, 0.388339, -0.872939], [0.000408, -0.91372, -0.406343]] and translation vector: [2.65583, 2.981598, 1.368648], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.951595, 0.120375, -0.282803], [0.307283, 0.392547, -0.866882], [0.006663, -0.91182, -0.410535]] and translation vector: [2.655525, 2.981353, 1.361859], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.943467, 0.154725, -0.293138], [0.331247, 0.407989, -0.850776], [-0.01204, -0.89978, -0.436177]] and translation vector: [2.636264, 2.98502, 1.345518]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_91_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_91_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_91_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_91_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_91_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_91_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_91_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_91_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.908726, 0.150598, -0.389277], [0.406624, 0.108936, -0.907078], [-0.094198, -0.982575, -0.16023]] and translation vector: [8.822721, 3.830595, 1.476402], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.908663, 0.151907, -0.388916], [0.40641, 0.108245, -0.907256], [-0.09572, -0.98245, -0.160095]] and translation vector: [8.818814, 3.832555, 1.475788], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.906287, 0.145588, -0.396797], [0.413103, 0.106574, -0.904427], [-0.089385, -0.983589, -0.156729]] and translation vector: [8.811844, 3.835278, 1.478992]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_92_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_92_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_92_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_92_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_92_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_92_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_92_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_92_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.895509, 0.17248, -0.410263], [0.444823, 0.375965, -0.812886], [0.014038, -0.91044, -0.413402]] and translation vector: [2.818061, 5.409916, 1.54775], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.895274, 0.172164, -0.410907], [0.445264, 0.376844, -0.812237], [0.01501, -0.910136, -0.414037]] and translation vector: [2.819061, 5.407142, 1.548651], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.894314, 0.169155, -0.414233], [0.446992, 0.379174, -0.810201], [0.020016, -0.909733, -0.414712]] and translation vector: [2.82614, 5.405447, 1.545731]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_93_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_93_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_93_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_93_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_93_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_93_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_93_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_93_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.852441, 0.228219, -0.470383], [0.522431, 0.337001, -0.78326], [-0.020235, -0.913426, -0.406502]] and translation vector: [1.798405, 5.320803, 1.619482], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.850776, 0.231102, -0.471988], [0.52508, 0.336676, -0.781627], [-0.021728, -0.91282, -0.407783]] and translation vector: [1.793927, 5.32593, 1.618758], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.843319, 0.217806, -0.491298], [0.537393, 0.333805, -0.774456], [-0.004683, -0.917134, -0.398552]] and translation vector: [1.789976, 5.331068, 1.629155]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_94_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_94_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_94_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_94_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_94_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_94_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_94_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_94_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.443363, -0.325026, 0.835337], [-0.895367, 0.117125, -0.429651], [0.041809, -0.938424, -0.342946]] and translation vector: [2.190343, 3.392878, 1.594635], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.439336, -0.32163, 0.838772], [-0.897253, 0.111545, -0.427195], [0.043838, -0.940272, -0.337589]] and translation vector: [2.183471, 3.393708, 1.586874], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.44052, -0.339041, 0.83126], [-0.896776, 0.123224, -0.424981], [0.041655, -0.932667, -0.358326]] and translation vector: [2.168168, 3.37614, 1.57519]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_95_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_95_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_95_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_95_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_95_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_95_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_95_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_95_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.236277, -0.452541, 0.859872], [-0.970097, 0.160455, -0.182119], [-0.055554, -0.877189, -0.47692]] and translation vector: [1.575898, 1.961144, 1.314442], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.238966, -0.451212, 0.859828], [-0.9694, 0.162109, -0.184349], [-0.056205, -0.87757, -0.476143]] and translation vector: [1.575219, 1.960128, 1.313122], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.271686, -0.463311, 0.843522], [-0.960992, 0.177771, -0.211879], [-0.051788, -0.868182, -0.493536]] and translation vector: [1.583445, 1.96149, 1.313418]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_96_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_96_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_96_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_96_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_96_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_96_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_96_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_96_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.931668, 0.072515, -0.356001], [0.362912, -0.231685, 0.902561], [-0.017031, -0.970084, -0.24217]] and translation vector: [5.886859, 3.543659, 1.354971], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.931979, 0.073028, -0.355079], [0.362119, -0.233112, 0.902513], [-0.016864, -0.969704, -0.2437]] and translation vector: [5.882501, 3.543666, 1.354317], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.932369, 0.086637, -0.350973], [0.36142, -0.244825, 0.899687], [-0.007981, -0.965689, -0.259579]] and translation vector: [5.853946, 3.560033, 1.352092]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_97_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_97_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_97_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_97_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_97_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_97_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_97_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_97_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.688084, 0.423256, -0.589401], [0.725514, -0.415863, 0.54835], [-0.013017, -0.80493, -0.593227]] and translation vector: [3.968163, 0.8771, 1.421607], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.688048, 0.420794, -0.591205], [0.725576, -0.411726, 0.551381], [-0.011397, -0.80834, -0.588605]] and translation vector: [3.964529, 0.870938, 1.417962], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.665465, 0.44657, -0.598107], [0.746417, -0.402654, 0.529841], [-0.004219, -0.799027, -0.60128]] and translation vector: [3.954065, 0.866652, 1.420457]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_98_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_98_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_98_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_98_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_98_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_98_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_98_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_98_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.45377, -0.425062, 0.783208], [-0.891046, 0.227634, -0.392708], [-0.01136, -0.876074, -0.482043]] and translation vector: [2.25004, 3.862298, 1.519108], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.453547, -0.422981, 0.784463], [-0.891155, 0.226808, -0.392938], [-0.011717, -0.877294, -0.47981]] and translation vector: [2.249275, 3.861866, 1.519019], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.445149, -0.42745, 0.786847], [-0.895457, 0.212955, -0.390907], [-0.00047, -0.878599, -0.47756]] and translation vector: [2.244179, 3.86012, 1.517719]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_99_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_99_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_99_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_99_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_99_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_99_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_99_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_99_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.778266, 0.076502, -0.623257], [0.626532, 0.028295, -0.778882], [-0.041951, -0.996668, -0.069952]] and translation vector: [4.354075, 2.27787, 1.510689], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.774603, 0.078895, -0.627508], [0.631084, 0.031306, -0.775082], [-0.041505, -0.996391, -0.074039]] and translation vector: [4.353431, 2.276987, 1.507071], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.765589, 0.09814, -0.635801], [0.642341, 0.061836, -0.76392], [-0.035656, -0.99325, -0.110381]] and translation vector: [4.348542, 2.268086, 1.503072]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_100_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_100_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_100_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_100_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_100_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_100_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_100_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_100_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.997112, 0.02462, 0.071841], [-0.04661, 0.548461, -0.834876], [-0.059957, -0.835814, -0.545729]] and translation vector: [4.834615, 3.436689, 1.398379], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.998397, 0.025746, 0.050402], [-0.028149, 0.546702, -0.836854], [-0.0491, -0.836932, -0.545101]] and translation vector: [4.839047, 3.434593, 1.400064], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.999077, -0.037699, 0.020609], [-0.036788, 0.502836, -0.863599], [0.022194, -0.863559, -0.503759]] and translation vector: [4.856574, 3.440762, 1.395837]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_101_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_101_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_101_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_101_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_101_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_101_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_101_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_101_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.924593, 0.219455, -0.311397], [0.371095, 0.334047, -0.86643], [-0.086121, -0.916653, -0.390296]] and translation vector: [7.650298, 2.745242, 1.444521], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.925403, 0.221817, -0.30729], [0.368562, 0.337876, -0.866026], [-0.088274, -0.914679, -0.394425]] and translation vector: [7.650829, 2.747432, 1.442508], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.931334, 0.218695, -0.291187], [0.355288, 0.37018, -0.858334], [-0.079922, -0.902851, -0.422461]] and translation vector: [7.652313, 2.75096, 1.431448]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_102_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_102_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_102_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_102_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_102_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_102_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_102_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_102_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.927869, -0.125596, 0.351119], [-0.372891, -0.32108, 0.870551], [0.003399, -0.938687, -0.344754]] and translation vector: [5.442723, 4.031985, 1.348893], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.928984, -0.124208, 0.348657], [-0.370086, -0.32475, 0.870387], [0.005117, -0.937609, -0.347654]] and translation vector: [5.438782, 4.038163, 1.363364], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.930142, -0.10574, 0.351647], [-0.366759, -0.314483, 0.87555], [0.018006, -0.943355, -0.331295]] and translation vector: [5.443505, 4.02862, 1.369591]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_103_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_103_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_103_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_103_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_103_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_103_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_103_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_103_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.32152, -0.4706, 0.821681], [-0.946681, 0.178549, -0.268172], [-0.020508, -0.864092, -0.502915]] and translation vector: [2.120097, 2.367636, 1.494245], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.324752, -0.471365, 0.819971], [-0.945715, 0.173395, -0.274877], [-0.012612, -0.864725, -0.502087]] and translation vector: [2.101204, 2.346659, 1.492081], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.35351, -0.420371, 0.835655], [-0.935423, 0.155099, -0.317693], [0.00394, -0.893998, -0.448054]] and translation vector: [2.068189, 2.338444, 1.524964]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_104_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_104_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_104_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_104_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_104_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_104_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_104_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_104_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.504428, 0.479717, -0.717931], [0.860003, -0.204862, 0.467362], [0.077124, -0.853173, -0.515896]] and translation vector: [4.973708, 0.412451, 1.573636], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.50991, 0.478461, -0.714889], [0.856537, -0.205494, 0.47341], [0.079603, -0.853725, -0.514603]] and translation vector: [4.974949, 0.42052, 1.588198], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.529349, 0.450337, -0.719018], [0.846093, -0.217693, 0.486556], [0.062589, -0.865914, -0.496262]] and translation vector: [4.987175, 0.423323, 1.59454]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_105_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_105_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_105_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_105_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_105_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_105_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_105_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_105_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.070416, -0.411804, 0.908548], [-0.99671, 0.065705, -0.047468], [-0.040148, -0.908901, -0.415075]] and translation vector: [2.214543, 1.806687, 1.391502], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.072195, -0.409813, 0.909308], [-0.996578, 0.066438, -0.049181], [-0.040258, -0.909747, -0.413207]] and translation vector: [2.216063, 1.808517, 1.395188], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.080916, -0.398975, 0.913384], [-0.996223, 0.061337, -0.061462], [-0.031503, -0.914908, -0.402432]] and translation vector: [2.214478, 1.812354, 1.396036]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_106_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_106_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_106_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_106_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_106_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_106_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_106_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_106_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.349467, 0.022881, -0.936669], [0.936944, -0.011774, 0.349282], [-0.003037, -0.999669, -0.025553]] and translation vector: [3.08553, 2.787215, 1.609269], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.348555, 0.021762, -0.937036], [0.937279, -0.012701, 0.34835], [-0.00432, -0.999682, -0.024824]] and translation vector: [3.086167, 2.787834, 1.610474], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.369988, 0.031522, -0.928502], [0.929035, -0.010749, 0.369835], [0.001677, -0.999445, -0.033262]] and translation vector: [3.084904, 2.78765, 1.611416]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_107_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_107_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_107_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_107_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_107_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_107_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_107_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_107_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.986418, -0.051155, 0.156087], [-0.152905, 0.633099, -0.758819], [-0.060001, -0.772379, -0.632322]] and translation vector: [2.055195, 1.600374, 1.268236], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.986809, -0.050817, 0.15371], [-0.151071, 0.630346, -0.761474], [-0.058194, -0.77465, -0.629707]] and translation vector: [2.054364, 1.600927, 1.26836], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.986971, -0.056701, 0.150577], [-0.152339, 0.630474, -0.761115], [-0.051779, -0.774137, -0.630897]] and translation vector: [2.055561, 1.60142, 1.26922]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_108_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_108_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_108_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_108_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_108_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_108_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_108_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_108_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.987126, 0.106622, -0.119219], [0.159938, -0.652529, 0.740693], [0.00118, -0.750225, -0.661181]] and translation vector: [4.64166, 4.052867, 1.404314], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.987387, 0.107853, -0.115912], [0.158278, -0.654013, 0.73974], [0.003975, -0.748756, -0.662834]] and translation vector: [4.649776, 4.051806, 1.400746], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.98973, 0.078153, -0.119695], [0.141622, -0.649931, 0.746681], [-0.019438, -0.755964, -0.654324]] and translation vector: [4.654046, 4.058671, 1.412681]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_109_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_109_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_109_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_109_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_109_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_109_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_109_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_109_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.994446, -0.078697, 0.06988], [-0.104992, -0.787844, 0.606859], [0.007297, -0.610826, -0.791731]] and translation vector: [1.305105, 0.510448, 1.183315], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.994112, -0.083607, 0.068931], [-0.10831, -0.785774, 0.608956], [0.003251, -0.612836, -0.790203]] and translation vector: [1.308194, 0.508844, 1.184721], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.994174, -0.088174, 0.061991], [-0.107635, -0.781912, 0.614026], [-0.00567, -0.617121, -0.786848]] and translation vector: [1.316761, 0.496028, 1.1951]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_110_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_110_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_110_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_110_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_110_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_110_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_110_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_110_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.481759, -0.460793, 0.745371], [-0.875469, 0.290199, -0.386444], [-0.038235, -0.838722, -0.543216]] and translation vector: [3.08436, 2.075189, 1.468295], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.482142, -0.463533, 0.743422], [-0.87538, 0.289132, -0.387445], [-0.035354, -0.83758, -0.54517]] and translation vector: [3.085865, 2.079347, 1.468915], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.466183, -0.466331, 0.751804], [-0.884097, 0.276631, -0.376626], [-0.03234, -0.840244, -0.541243]] and translation vector: [3.069418, 2.081707, 1.467716]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_111_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_111_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_111_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_111_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_111_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_111_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_111_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_111_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.975982, 0.033782, -0.215214], [0.215389, -0.297687, 0.930048], [-0.032648, -0.954066, -0.297814]] and translation vector: [2.838751, 1.414222, 1.664536], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.976127, 0.034525, -0.21444], [0.21483, -0.298963, 0.929769], [-0.03201, -0.95364, -0.299243]] and translation vector: [2.83798, 1.414721, 1.663024], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.977071, 0.035817, -0.209879], [0.210869, -0.299025, 0.930655], [-0.029426, -0.953573, -0.299721]] and translation vector: [2.830656, 1.415531, 1.663803]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_112_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_112_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_112_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_112_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_112_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_112_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_112_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_112_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.054781, -0.427281, 0.902458], [-0.998013, -0.051617, 0.036143], [0.031139, -0.902644, -0.429259]] and translation vector: [1.328526, 0.849821, 1.501181], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.086578, -0.407933, 0.908898], [-0.995883, -0.060028, 0.067922], [0.026852, -0.911036, -0.41145]] and translation vector: [1.314662, 0.836147, 1.492068], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.123316, -0.40327, 0.906734], [-0.991749, -0.082348, 0.098253], [0.035045, -0.911368, -0.410097]] and translation vector: [1.307532, 0.816785, 1.49678]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_113_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_113_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_113_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_113_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_113_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_113_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_113_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_113_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.140295, 0.625342, -0.767636], [0.990108, -0.090149, 0.107516], [-0.001967, -0.775126, -0.631804]] and translation vector: [3.410891, 3.073526, 1.198756], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.148525, 0.612201, -0.776627], [0.988818, -0.102561, 0.108258], [-0.013376, -0.784022, -0.620589]] and translation vector: [3.421496, 3.097678, 1.206193], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.180299, 0.582031, -0.792926], [0.982291, -0.148308, 0.114495], [-0.050958, -0.799528, -0.598463]] and translation vector: [3.423417, 3.182928, 1.218892]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_114_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_114_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_114_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_114_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_114_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_114_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_114_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_114_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.408988, -0.323891, 0.853126], [-0.912443, -0.158736, 0.37716], [0.013263, -0.932683, -0.360453]] and translation vector: [3.672612, 2.990265, 1.494339], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.403714, -0.307769, 0.861564], [-0.914697, -0.154884, 0.373283], [0.018558, -0.93877, -0.344045]] and translation vector: [3.67724, 2.998002, 1.501107], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.418114, -0.223767, 0.880403], [-0.907864, -0.136047, 0.396578], [0.031035, -0.965101, -0.260033]] and translation vector: [3.686426, 2.992862, 1.516855]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_115_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_115_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_115_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_115_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_115_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_115_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_115_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_115_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.804414, -0.195207, 0.561082], [-0.593456, -0.306943, 0.74404], [0.026978, -0.931494, -0.362756]] and translation vector: [4.397897, 1.805397, 1.263968], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.81043, -0.19082, 0.553888], [-0.585149, -0.309439, 0.749566], [0.028363, -0.931577, -0.362436]] and translation vector: [4.406421, 1.797547, 1.276681], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.835561, -0.15907, 0.525866], [-0.54802, -0.309079, 0.777267], [0.038894, -0.937639, -0.345428]] and translation vector: [4.454782, 1.746297, 1.281162]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_116_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_116_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_116_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_116_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_116_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_116_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_116_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_116_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.454685, 0.144673, -0.878824], [0.890085, 0.109034, -0.442562], [0.031795, -0.983454, -0.178347]] and translation vector: [3.311996, 2.119304, 1.59409], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.453171, 0.138778, -0.880555], [0.890847, 0.10604, -0.441756], [0.032068, -0.98463, -0.171684]] and translation vector: [3.314367, 2.120091, 1.591769], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.43605, 0.134523, -0.889811], [0.898328, 0.123911, -0.42149], [0.053558, -0.983133, -0.174877]] and translation vector: [3.332471, 2.052713, 1.580764]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_117_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_117_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_117_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_117_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_117_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_117_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_117_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_117_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.51864, -0.44867, 0.727811], [-0.853934, -0.229463, 0.467059], [-0.04255, -0.863738, -0.502143]] and translation vector: [1.002297, 1.98866, 1.344191], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.519607, -0.444592, 0.729621], [-0.853432, -0.229314, 0.468049], [-0.040778, -0.865883, -0.498582]] and translation vector: [1.000441, 1.985865, 1.344846], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.525099, -0.430062, 0.734383], [-0.8496, -0.214703, 0.48175], [-0.049508, -0.876898, -0.478121]] and translation vector: [0.994465, 1.977308, 1.35476]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_118_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_118_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_118_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_118_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_118_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_118_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_118_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_118_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.68967, 0.288211, -0.664297], [0.724122, -0.27239, 0.633602], [0.001663, -0.918008, -0.396559]] and translation vector: [2.530043, 2.005069, 1.437417], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.68921, 0.288518, -0.66464], [0.724561, -0.273014, 0.632831], [0.001127, -0.917726, -0.397212]] and translation vector: [2.5334, 2.008455, 1.44069], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.695343, 0.287777, -0.658546], [0.718659, -0.271639, 0.640111], [0.005323, -0.918366, -0.395696]] and translation vector: [2.535345, 2.010031, 1.440264]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_119_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_119_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_119_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_119_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_119_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_119_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_119_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_119_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.24604, -0.551346, 0.797171], [-0.968826, -0.115295, 0.219278], [-0.028988, -0.826271, -0.562526]] and translation vector: [1.704247, 2.057158, 1.361636], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.236706, -0.55071, 0.800431], [-0.971342, -0.115817, 0.207564], [-0.021604, -0.826623, -0.562342]] and translation vector: [1.70792, 2.062619, 1.364929], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.170375, -0.545117, 0.820866], [-0.98536, -0.099505, 0.138438], [0.006215, -0.832434, -0.554089]] and translation vector: [1.68849, 2.12587, 1.375528]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_120_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_120_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_120_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_120_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_120_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_120_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_120_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_120_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.711391, -0.463973, 0.527875], [-0.700286, 0.531398, -0.476672], [-0.059349, -0.708763, -0.702945]] and translation vector: [2.53321, 4.394931, 1.530427], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.710702, -0.465347, 0.527594], [-0.701175, 0.5294, -0.477586], [-0.057065, -0.709357, -0.702536]] and translation vector: [2.526067, 4.393322, 1.526345], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.710832, -0.469663, 0.523579], [-0.701381, 0.52914, -0.477573], [-0.052748, -0.706702, -0.705542]] and translation vector: [2.532494, 4.391185, 1.524071]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_121_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_121_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_121_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_121_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_121_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_121_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_121_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_121_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.506976, -0.449046, 0.735753], [-0.861802, 0.247713, -0.442646], [0.016513, -0.858485, -0.512574]] and translation vector: [1.568574, 4.423309, 1.333385], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.503836, -0.444181, 0.740846], [-0.863753, 0.25025, -0.437385], [0.008882, -0.860278, -0.509748]] and translation vector: [1.576928, 4.418399, 1.331934], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.476896, -0.475938, 0.738954], [-0.878865, 0.245876, -0.408828], [0.012886, -0.84441, -0.535543]] and translation vector: [1.618973, 4.377153, 1.328238]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_122_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_122_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_122_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_122_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_122_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_122_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_122_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_122_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.857694, 0.203115, -0.472341], [0.513544, 0.293426, -0.806333], [-0.025181, -0.934155, -0.355978]] and translation vector: [3.161674, 3.662206, 1.335287], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.856666, 0.203827, -0.473897], [0.515344, 0.296604, -0.804019], [-0.023321, -0.932995, -0.359132]] and translation vector: [3.164327, 3.659025, 1.330704], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.851543, 0.201203, -0.48414], [0.523447, 0.274112, -0.806762], [-0.029614, -0.940415, -0.338738]] and translation vector: [3.169208, 3.645592, 1.345035]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_123_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_123_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_123_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_123_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_123_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_123_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_123_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_123_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.861262, 0.35211, -0.366398], [0.508128, 0.60504, -0.61297], [0.005853, -0.714105, -0.700014]] and translation vector: [3.145762, 3.637784, 1.437024], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.859655, 0.347273, -0.374693], [0.510745, 0.600786, -0.614977], [0.011546, -0.720041, -0.693836]] and translation vector: [3.145171, 3.63531, 1.440385], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.904923, 0.242096, -0.350005], [0.423906, 0.585528, -0.690985], [0.037653, -0.773658, -0.632485]] and translation vector: [3.179198, 3.619442, 1.477378]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_124_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_124_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_124_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_124_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_124_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_124_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_124_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_124_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.624751, -0.31057, 0.716403], [-0.780527, -0.273701, 0.562018], [0.021534, -0.910293, -0.413403]] and translation vector: [-0.212106, 0.775797, 1.619325], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.624146, -0.312612, 0.716042], [-0.781019, -0.274551, 0.56092], [0.02124, -0.909338, -0.415515]] and translation vector: [-0.212874, 0.777223, 1.616059], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.642142, -0.354499, 0.679694], [-0.766394, -0.316707, 0.558871], [0.017145, -0.879788, -0.475057]] and translation vector: [-0.180935, 0.825968, 1.590205]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_125_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_125_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_125_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_125_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_125_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_125_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_125_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_125_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.984594, -0.069457, 0.160469], [-0.174127, -0.305795, 0.936039], [-0.015944, -0.949561, -0.313178]] and translation vector: [3.941113, 2.817773, 1.559826], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.984592, -0.069572, 0.160429], [-0.174152, -0.307406, 0.935507], [-0.015768, -0.949032, -0.314785]] and translation vector: [3.94407, 2.817183, 1.553188], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.986412, -0.069361, 0.14893], [-0.163547, -0.328462, 0.93025], [-0.015605, -0.941967, -0.335343]] and translation vector: [3.970874, 2.81883, 1.551708]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_126_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_126_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_126_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_126_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_126_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_126_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_126_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_126_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.565317, -0.50256, 0.654103], [-0.824719, 0.328974, -0.460017], [0.016003, -0.799506, -0.600445]] and translation vector: [4.07549, 5.065369, 1.281872], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.538132, -0.502349, 0.676801], [-0.842747, 0.30749, -0.441846], [0.013851, -0.808143, -0.588824]] and translation vector: [4.054681, 5.042427, 1.283033], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.45677, -0.532015, 0.712967], [-0.889546, 0.265624, -0.371689], [0.008363, -0.803993, -0.594581]] and translation vector: [3.985017, 4.950093, 1.286783]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_127_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_127_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_127_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_127_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_127_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_127_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_127_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_127_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.971613, -0.06682, 0.226943], [-0.235147, 0.378036, -0.89543], [-0.02596, -0.923376, -0.383017]] and translation vector: [2.775299, 4.618156, 1.427592], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.969099, -0.066923, 0.237421], [-0.244849, 0.377786, -0.892932], [-0.029937, -0.923471, -0.382498]] and translation vector: [2.770648, 4.620754, 1.418404], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.959375, -0.08118, 0.270203], [-0.280099, 0.388898, -0.877669], [-0.033832, -0.917697, -0.395838]] and translation vector: [2.756619, 4.594989, 1.414391]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_128_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_128_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_128_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_128_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_128_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_128_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_128_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_128_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.218501, -0.721835, 0.656667], [-0.97193, -0.10083, 0.212566], [-0.087226, -0.684681, -0.723605]] and translation vector: [2.10902, 2.428258, 1.386435], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.218569, -0.722397, 0.656026], [-0.971546, -0.098231, 0.215522], [-0.091251, -0.684466, -0.723312]] and translation vector: [2.107975, 2.430531, 1.385643], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.234983, -0.674252, 0.70012], [-0.966581, -0.086145, 0.241454], [-0.102489, -0.73346, -0.671961]] and translation vector: [2.089091, 2.418566, 1.400829]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_129_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_129_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_129_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_129_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_129_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_129_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_129_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_129_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.863619, -0.252896, 0.436126], [-0.502889, 0.371124, -0.780621], [0.03556, -0.893482, -0.447688]] and translation vector: [2.007098, 3.82416, 1.536992], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.862677, -0.255046, 0.436739], [-0.504412, 0.370978, -0.779707], [0.036841, -0.892932, -0.448682]] and translation vector: [2.007321, 3.81907, 1.542811], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.863059, -0.255804, 0.435538], [-0.503401, 0.36489, -0.783226], [0.041429, -0.89522, -0.443694]] and translation vector: [2.011345, 3.815826, 1.540639]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_130_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_130_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_130_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_130_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_130_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_130_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_130_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_130_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.311411, -0.45253, 0.835607], [-0.948656, 0.199362, -0.245576], [-0.055457, -0.869179, -0.491379]] and translation vector: [2.299133, 2.388773, 1.459468], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.314195, -0.454542, 0.833471], [-0.947818, 0.20019, -0.248124], [-0.05407, -0.867937, -0.493722]] and translation vector: [2.299448, 2.389842, 1.45904], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.319309, -0.479365, 0.817466], [-0.946515, 0.203543, -0.250358], [-0.046377, -0.853686, -0.518719]] and translation vector: [2.297309, 2.382683, 1.450072]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_131_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_131_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_131_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_131_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_131_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_131_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_131_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_131_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.922168, 0.178823, -0.342969], [0.38661, 0.453076, -0.803278], [0.011746, -0.873352, -0.486947]] and translation vector: [3.207336, 1.959871, 1.267555], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.914921, 0.180426, -0.361063], [0.403188, 0.450583, -0.796502], [0.018979, -0.874312, -0.484993]] and translation vector: [3.204391, 1.957541, 1.273759], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.899907, 0.183343, -0.395667], [0.435126, 0.437531, -0.786913], [0.028842, -0.880314, -0.473515]] and translation vector: [3.195998, 1.957617, 1.285169]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_132_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_132_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_132_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_132_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_132_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_132_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_132_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_132_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.037281, 0.595041, -0.80283], [0.998378, -0.012419, -0.055566], [-0.043034, -0.803599, -0.593613]] and translation vector: [3.95675, 2.244474, 1.442954], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.038109, 0.594465, -0.803218], [0.998341, -0.012073, -0.056302], [-0.043167, -0.80403, -0.593019]] and translation vector: [3.957906, 2.244142, 1.441716], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.035792, 0.584102, -0.810891], [0.99863, -0.010099, -0.051354], [-0.038185, -0.811617, -0.58294]] and translation vector: [3.956708, 2.24149, 1.443636]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_133_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_133_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_133_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_133_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_133_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_133_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_133_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_133_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.941243, -0.209403, 0.264975], [-0.336113, 0.504116, -0.795548], [0.033012, -0.837865, -0.544878]] and translation vector: [4.828751, 9.008894, 1.463441], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.939528, -0.206646, 0.273103], [-0.341818, 0.516505, -0.785101], [0.021179, -0.830976, -0.555906]] and translation vector: [4.819307, 9.009376, 1.463735], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.929333, -0.218512, 0.297646], [-0.368979, 0.519063, -0.770992], [0.013974, -0.826333, -0.563008]] and translation vector: [4.802584, 9.04943, 1.458571]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_134_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_134_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_134_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_134_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_134_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_134_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_134_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_134_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.341382, 0.594812, -0.727775], [0.932196, 0.11517, -0.343142], [-0.120287, -0.795572, -0.593798]] and translation vector: [7.151203, 3.587152, 1.581923], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.344041, 0.585523, -0.734029], [0.930897, 0.110501, -0.348168], [-0.122749, -0.803089, -0.583079]] and translation vector: [7.150104, 3.60012, 1.584136], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.381268, 0.567894, -0.729473], [0.913798, 0.111991, -0.390424], [-0.140025, -0.815448, -0.561639]] and translation vector: [7.153435, 3.678253, 1.582921]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_135_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_135_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_135_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_135_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_135_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_135_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_135_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_135_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.631332, 0.312126, -0.709927], [0.775472, -0.26347, 0.573784], [-0.007951, -0.912776, -0.408382]] and translation vector: [1.600176, 0.624978, 1.327739], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.627277, 0.311053, -0.713982], [0.778666, -0.267257, 0.567673], [-0.014241, -0.912041, -0.409851]] and translation vector: [1.601099, 0.627571, 1.328079], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.610657, 0.317655, -0.725393], [0.791862, -0.253314, 0.555685], [-0.007236, -0.913744, -0.406226]] and translation vector: [1.603666, 0.628049, 1.323957]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_136_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_136_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_136_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_136_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_136_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_136_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_136_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_136_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.283698, -0.38675, 0.877463], [-0.95878, 0.129662, -0.252839], [-0.015988, -0.913024, -0.407593]] and translation vector: [3.69525, 3.551647, 1.352095], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.292652, -0.378333, 0.878191], [-0.956147, 0.127043, -0.2639], [-0.011726, -0.91691, -0.398922]] and translation vector: [3.694781, 3.553972, 1.346799], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.31632, -0.391232, 0.864222], [-0.948647, 0.127329, -0.28958], [0.003253, -0.911441, -0.411418]] and translation vector: [3.701458, 3.559184, 1.352364]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_137_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_137_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_137_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_137_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_137_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_137_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_137_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_137_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.831143, 0.312948, -0.459636], [0.555586, 0.43327, -0.709649], [-0.022937, -0.845187, -0.533978]] and translation vector: [2.360292, 3.05803, 1.315354], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.8108, 0.328121, -0.484706], [0.584922, 0.423558, -0.691711], [-0.021664, -0.844355, -0.535346]] and translation vector: [2.374215, 3.08026, 1.318953], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.76064, 0.373644, -0.530865], [0.648502, 0.400127, -0.647568], [-0.029546, -0.836832, -0.546661]] and translation vector: [2.421989, 3.144455, 1.295588]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_138_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_138_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_138_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_138_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_138_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_138_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_138_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_138_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.963317, 0.154363, -0.219528], [0.260086, 0.335369, -0.905474], [-0.066149, -0.929355, -0.363214]] and translation vector: [5.972451, 2.818726, 1.468896], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.963149, 0.154275, -0.220326], [0.260736, 0.334417, -0.905639], [-0.066037, -0.929712, -0.362318]] and translation vector: [5.973901, 2.819783, 1.467855], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.966667, 0.155296, -0.203565], [0.245918, 0.341836, -0.907013], [-0.07127, -0.926839, -0.368632]] and translation vector: [5.982299, 2.822232, 1.456096]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_139_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_139_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_139_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_139_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_139_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_139_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_139_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_139_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.48142, 0.335029, -0.809933], [0.872625, 0.096524, -0.478757], [-0.08222, -0.937251, -0.338823]] and translation vector: [4.429162, 2.287411, 1.464776], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.484328, 0.331289, -0.809737], [0.871134, 0.09698, -0.481374], [-0.080946, -0.938532, -0.335568]] and translation vector: [4.432656, 2.285767, 1.465956], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.510728, 0.315618, -0.799714], [0.857732, 0.123483, -0.499047], [-0.058757, -0.940817, -0.333782]] and translation vector: [4.456876, 2.264055, 1.467574]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_140_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_140_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_140_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_140_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_140_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_140_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_140_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_140_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.233902, -0.58763, 0.774584], [-0.967246, -0.059828, 0.246692], [-0.098622, -0.806915, -0.582377]] and translation vector: [0.860343, 3.117731, 1.418568], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.233684, -0.587102, 0.775051], [-0.967496, -0.061159, 0.24538], [-0.096661, -0.8072, -0.58231]] and translation vector: [0.859973, 3.119137, 1.418853], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.249158, -0.592393, 0.766154], [-0.964448, -0.07981, 0.251935], [-0.088098, -0.801687, -0.591217]] and translation vector: [0.847042, 3.133789, 1.403155]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_141_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_141_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_141_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_141_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_141_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_141_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_141_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_141_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.645842, -0.099101, 0.757012], [-0.761541, -0.013148, 0.647984], [-0.054263, -0.994991, -0.083961]] and translation vector: [3.729951, 1.432448, 1.733539], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.649827, -0.099601, 0.753528], [-0.757797, -0.00807, 0.652441], [-0.058903, -0.994995, -0.080722]] and translation vector: [3.727943, 1.43259, 1.731865], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.662065, -0.092976, 0.743657], [-0.747389, -0.008433, 0.664333], [-0.055496, -0.995633, -0.075073]] and translation vector: [3.728372, 1.436196, 1.743771]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_142_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_142_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_142_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_142_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_142_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_142_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_142_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_142_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.924746, 0.145405, -0.351715], [0.379908, 0.407811, -0.830277], [0.022707, -0.901414, -0.432362]] and translation vector: [3.891577, 4.106122, 1.335216], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.925289, 0.144931, -0.350479], [0.378485, 0.412032, -0.828842], [0.024284, -0.899569, -0.436102]] and translation vector: [3.892777, 4.104329, 1.336806], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.936719, 0.138164, -0.321666], [0.349495, 0.42231, -0.836366], [0.020288, -0.89586, -0.443873]] and translation vector: [3.898582, 4.105442, 1.335634]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_143_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_143_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_143_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_143_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_143_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_143_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_143_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_143_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.896132, -0.052356, 0.440688], [-0.436974, -0.277444, 0.855616], [0.07747, -0.959314, -0.271505]] and translation vector: [3.211431, 3.110947, 1.584554], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.889709, -0.065096, 0.451863], [-0.451099, -0.277541, 0.848222], [0.070195, -0.958506, -0.276295]] and translation vector: [3.215954, 3.116336, 1.570817], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.866761, -0.113538, 0.485628], [-0.495946, -0.298858, 0.815305], [0.052566, -0.94752, -0.315347]] and translation vector: [3.24594, 3.15503, 1.569742]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_144_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_144_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_144_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_144_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_144_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_144_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_144_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_144_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.052123, 0.492225, -0.868906], [0.996177, 0.08671, -0.010637], [0.070107, -0.866138, -0.494863]] and translation vector: [3.27549, 2.071379, 1.287401], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.035278, 0.492309, -0.869705], [0.997133, 0.075637, 0.002369], [0.066948, -0.867128, -0.493566]] and translation vector: [3.286684, 2.076202, 1.285681], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.002481, 0.481037, -0.876697], [0.99848, 0.047075, 0.028655], [0.055055, -0.875436, -0.480189]] and translation vector: [3.329912, 2.119781, 1.289403]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_145_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_145_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_145_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_145_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_145_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_145_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_145_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_145_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.935878, -0.161972, 0.312885], [-0.352322, 0.433116, -0.829627], [-0.001139, -0.886666, -0.46241]] and translation vector: [1.123681, 2.231354, 1.408983], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.935522, -0.159, 0.315466], [-0.353249, 0.430874, -0.830399], [-0.003893, -0.888294, -0.459258]] and translation vector: [1.123559, 2.231523, 1.408322], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.93225, -0.177625, 0.315214], [-0.361774, 0.444334, -0.819565], [0.005515, -0.878076, -0.47849]] and translation vector: [1.117516, 2.230649, 1.39948]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_146_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_146_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_146_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_146_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_146_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_146_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_146_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_146_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.305635, -0.390507, 0.868385], [-0.952144, 0.122302, -0.280116], [0.003183, -0.91244, -0.409198]] and translation vector: [4.266061, 1.773856, 1.285079], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.300987, -0.399102, 0.866097], [-0.953628, 0.125052, -0.273781], [0.00096, -0.908339, -0.418234]] and translation vector: [4.263163, 1.772832, 1.291083], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.290604, -0.37367, 0.880863], [-0.956686, 0.130175, -0.260397], [-0.017364, -0.918382, -0.395314]] and translation vector: [4.197608, 1.767915, 1.309526]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_147_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_147_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_147_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_147_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_147_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_147_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_147_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_147_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.485844, -0.617081, 0.619005], [-0.873216, -0.311825, 0.374512], [-0.038083, -0.722479, -0.690343]] and translation vector: [-0.164865, 3.073333, 1.323993], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.482952, -0.621872, 0.616468], [-0.874972, -0.315096, 0.367612], [-0.034361, -0.716931, -0.696297]] and translation vector: [-0.16601, 3.069565, 1.320265], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.481893, -0.627462, 0.611613], [-0.875383, -0.314055, 0.367526], [-0.038529, -0.712503, -0.70061]] and translation vector: [-0.162661, 3.069695, 1.32373]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_148_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_148_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_148_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_148_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_148_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_148_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_148_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_148_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.82141, -0.124481, 0.556588], [-0.562763, -0.33543, 0.755503], [0.092651, -0.933805, -0.345579]] and translation vector: [1.795382, 2.457259, 1.379582], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.820332, -0.124179, 0.558243], [-0.564621, -0.330977, 0.75608], [0.090876, -0.935432, -0.341626]] and translation vector: [1.795684, 2.460531, 1.380001], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.815123, -0.112718, 0.568216], [-0.568956, -0.340207, 0.748698], [0.108919, -0.933571, -0.341442]] and translation vector: [1.795413, 2.484714, 1.377791]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_149_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_149_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_149_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_149_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_149_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_149_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_149_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_149_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.464707, 0.496079, -0.733453], [0.882598, 0.326106, -0.338639], [0.071191, -0.804711, -0.589382]] and translation vector: [2.864701, 0.868861, 1.204561], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.473617, 0.501904, -0.723726], [0.878064, 0.332992, -0.343688], [0.068496, -0.798254, -0.598414]] and translation vector: [2.869803, 0.866998, 1.20304], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.486908, 0.474562, -0.733288], [0.872245, 0.308313, -0.379646], [0.045917, -0.82446, -0.564055]] and translation vector: [2.890215, 0.843054, 1.203118]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_150_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_150_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_150_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_150_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_150_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_150_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_150_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_150_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.199941, 0.263531, -0.943703], [0.979453, -0.027844, 0.19974], [0.026362, -0.964249, -0.263683]] and translation vector: [3.611549, 3.757055, 1.562045], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.20075, 0.267793, -0.94233], [0.97934, -0.030969, 0.199834], [0.024331, -0.962979, -0.268477]] and translation vector: [3.608934, 3.756757, 1.557843], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.195501, 0.302185, -0.932986], [0.980511, -0.041383, 0.192056], [0.019427, -0.95235, -0.304386]] and translation vector: [3.586484, 3.775929, 1.547968]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_151_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_151_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_151_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_151_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_151_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_151_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_151_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_151_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.869565, 0.231948, -0.435955], [0.492522, 0.471291, -0.731647], [0.035758, -0.850932, -0.524058]] and translation vector: [2.750575, 3.154689, 1.290553], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.871211, 0.246607, -0.424472], [0.49036, 0.478017, -0.72873], [0.023195, -0.843022, -0.53738]] and translation vector: [2.712538, 3.137298, 1.287246], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.868111, 0.301221, -0.394523], [0.496051, 0.497976, -0.711305], [-0.017797, -0.813195, -0.581719]] and translation vector: [2.638672, 3.09301, 1.251808]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_152_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_152_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_152_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_152_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_152_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_152_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_152_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_152_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.606468, -0.360414, 0.70873], [-0.789578, -0.16805, 0.590192], [-0.093612, -0.91753, -0.386492]] and translation vector: [2.373669, 6.226582, 1.48631], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.603564, -0.356146, 0.713352], [-0.791899, -0.163667, 0.588311], [-0.092772, -0.919986, -0.380815]] and translation vector: [2.370215, 6.229294, 1.484576], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.585698, -0.348105, 0.731971], [-0.805739, -0.152014, 0.572431], [-0.087997, -0.925048, -0.369516]] and translation vector: [2.368074, 6.23172, 1.479712]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_153_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_153_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_153_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_153_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_153_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_153_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_153_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_153_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.414473, -0.491559, 0.765887], [-0.909569, 0.196057, -0.366396], [0.029948, -0.848488, -0.528367]] and translation vector: [0.955419, 3.497842, 1.497559], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.410009, -0.490704, 0.768832], [-0.911757, 0.198024, -0.359841], [0.024328, -0.848526, -0.528594]] and translation vector: [0.937857, 3.503192, 1.495427], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.398859, -0.490133, 0.775036], [-0.916862, 0.197836, -0.346736], [0.016617, -0.848899, -0.528293]] and translation vector: [0.908797, 3.515594, 1.497193]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_154_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_154_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_154_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_154_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_154_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_154_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_154_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_154_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.935902, 0.160482, -0.313582], [0.351212, -0.493772, 0.795512], [-0.027173, -0.854655, -0.518485]] and translation vector: [4.465, -0.226232, 1.550028], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.933656, 0.161027, -0.319933], [0.356818, -0.495752, 0.791777], [-0.03111, -0.853405, -0.520319]] and translation vector: [4.478531, -0.229773, 1.540292], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.918867, 0.198209, -0.341168], [0.393883, -0.511652, 0.763589], [-0.023209, -0.836017, -0.548212]] and translation vector: [4.561479, -0.239772, 1.527731]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_155_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_155_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_155_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_155_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_155_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_155_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_155_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_155_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.079918, -0.690871, 0.718547], [-0.996802, 0.055321, -0.057677], [9.6e-05, -0.720858, -0.693082]] and translation vector: [1.142658, 0.968078, 1.385987], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.080635, -0.691404, 0.717954], [-0.996742, 0.054488, -0.059473], [0.002, -0.72041, -0.693545]] and translation vector: [1.144302, 0.967344, 1.387927], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.084359, -0.697761, 0.711347], [-0.996391, 0.05228, -0.066881], [0.009477, -0.714421, -0.699652]] and translation vector: [1.144001, 0.956717, 1.378471]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_156_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_156_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_156_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_156_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_156_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_156_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_156_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_156_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.799511, 0.533863, -0.275266], [0.600541, 0.71925, -0.349328], [0.011492, -0.4446, -0.895656]] and translation vector: [2.031323, 2.312379, 1.200993], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.794986, 0.540559, -0.275306], [0.606553, 0.715482, -0.346669], [0.009582, -0.442584, -0.896676]] and translation vector: [2.031011, 2.313572, 1.199732], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.773021, 0.563749, -0.290906], [0.633995, 0.702534, -0.323259], [0.022134, -0.434318, -0.900488]] and translation vector: [2.034953, 2.302037, 1.199248]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_157_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_157_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_157_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_157_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_157_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_157_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_157_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_157_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.573389, -0.355745, 0.738018], [-0.818965, 0.223754, -0.528424], [0.02285, -0.907403, -0.419641]] and translation vector: [2.061407, 3.857203, 1.382209], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.569689, -0.351701, 0.742806], [-0.821614, 0.221591, -0.525212], [0.020118, -0.909508, -0.4152]] and translation vector: [2.058259, 3.848013, 1.384733], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.577204, -0.345215, 0.740042], [-0.816391, 0.223437, -0.532524], [0.018482, -0.911539, -0.410799]] and translation vector: [2.052109, 3.841456, 1.390313]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_158_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_158_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_158_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_158_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_158_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_158_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_158_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_158_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.998162, -0.007354, -0.06016], [0.055338, 0.294228, -0.954132], [0.024717, -0.955707, -0.293281]] and translation vector: [1.687981, 4.43329, 1.569003], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.998237, -0.004775, -0.059163], [0.055295, 0.287523, -0.956176], [0.021577, -0.957762, -0.286752]] and translation vector: [1.687716, 4.435163, 1.571974], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.998336, 0.001509, -0.057642], [0.055709, 0.283251, -0.957427], [0.014882, -0.959045, -0.282864]] and translation vector: [1.68694, 4.439428, 1.572118]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_159_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_159_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_159_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_159_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_159_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_159_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_159_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_159_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.633294, -0.360819, 0.684652], [-0.773758, -0.312806, 0.550863], [0.015401, -0.878613, -0.477285]] and translation vector: [3.241882, 3.386626, 1.367882], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.618852, -0.359339, 0.698497], [-0.785116, -0.311057, 0.535572], [0.02482, -0.87984, -0.47462]] and translation vector: [3.234923, 3.400149, 1.365622], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.596077, -0.384708, 0.704764], [-0.800029, -0.359087, 0.480636], [0.068167, -0.850327, -0.521821]] and translation vector: [3.228332, 3.407161, 1.324573]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_160_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_160_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_160_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_160_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_160_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_160_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_160_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_160_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.977514, -0.102294, 0.184398], [-0.210796, -0.497303, 0.841578], [0.005613, -0.861525, -0.507684]] and translation vector: [3.555602, 1.207732, 1.356493], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.976582, -0.105336, 0.187593], [-0.215087, -0.498001, 0.840079], [0.00493, -0.860755, -0.508995]] and translation vector: [3.555365, 1.207812, 1.356155], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.974531, -0.107289, 0.196922], [-0.224038, -0.504207, 0.834016], [0.009809, -0.856892, -0.515402]] and translation vector: [3.552069, 1.20032, 1.350158]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_161_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_161_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_161_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_161_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_161_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_161_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_161_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_161_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.934222, -0.219071, 0.281493], [-0.356558, -0.595286, 0.72007], [0.009823, -0.773073, -0.634241]] and translation vector: [0.331108, 1.989283, 1.551545], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.93341, -0.222981, 0.281114], [-0.358788, -0.589093, 0.724045], [0.004154, -0.776691, -0.629868]] and translation vector: [0.338532, 1.98258, 1.554168], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.924209, -0.231475, 0.303738], [-0.381819, -0.575084, 0.723528], [0.007196, -0.784664, -0.619879]] and translation vector: [0.352139, 1.976578, 1.57555]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_162_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_162_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_162_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_162_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_162_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_162_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_162_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_162_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.928375, -0.17783, 0.326339], [-0.371449, 0.415395, -0.830345], [0.012101, -0.892089, -0.451697]] and translation vector: [2.096006, 1.919092, 1.36174], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.929206, -0.177937, 0.323905], [-0.369314, 0.414969, -0.83151], [0.013546, -0.892266, -0.451307]] and translation vector: [2.095672, 1.922099, 1.363168], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.930649, -0.183615, 0.31651], [-0.365027, 0.405695, -0.837954], [0.025454, -0.895375, -0.444584]] and translation vector: [2.086709, 1.937528, 1.366332]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_163_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_163_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_163_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_163_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_163_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_163_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_163_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_163_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.699126, -0.324611, 0.637064], [-0.713802, 0.265353, -0.648131], [0.041344, -0.907863, -0.417224]] and translation vector: [0.050403, 3.78209, 1.506908], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.698648, -0.327666, 0.636024], [-0.713993, 0.262294, -0.649166], [0.045885, -0.907654, -0.417203]] and translation vector: [0.047406, 3.786517, 1.504266], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.671591, -0.353844, 0.650968], [-0.738623, 0.250587, -0.625813], [0.058316, -0.901111, -0.429649]] and translation vector: [0.057884, 3.801169, 1.498956]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_164_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_164_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_164_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_164_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_164_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_164_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_164_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_164_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.892065, -0.360019, 0.273141], [-0.443019, -0.577417, 0.685801], [-0.089185, -0.732786, -0.674589]] and translation vector: [2.898737, 2.45906, 1.649541], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.888376, -0.366176, 0.276954], [-0.450762, -0.581088, 0.677606], [-0.087189, -0.726809, -0.681283]] and translation vector: [2.873446, 2.440832, 1.651115], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.866588, -0.389647, 0.31177], [-0.495846, -0.601945, 0.625939], [-0.056227, -0.697021, -0.714843]] and translation vector: [2.802999, 2.373059, 1.651133]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_165_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_165_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_165_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_165_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_165_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_165_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_165_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_165_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.660671, 0.426343, -0.617856], [0.749322, -0.423957, 0.508701], [-0.045063, -0.799057, -0.599565]] and translation vector: [1.739014, 2.260029, 1.323145], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.661948, 0.412501, -0.625834], [0.748146, -0.41469, 0.517987], [-0.045857, -0.811095, -0.583114]] and translation vector: [1.741474, 2.257287, 1.327618], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.667808, 0.364392, -0.649039], [0.743671, -0.363436, 0.561132], [-0.031412, -0.857399, -0.513693]] and translation vector: [1.753926, 2.258369, 1.342793]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_166_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_166_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_166_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_166_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_166_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_166_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_166_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_166_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.997074, 0.061747, -0.045056], [0.074474, 0.651998, -0.754554], [-0.017215, -0.755702, -0.654689]] and translation vector: [1.815792, 5.369752, 1.288561], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.994543, 0.080066, -0.066881], [0.102674, 0.63762, -0.763478], [-0.018484, -0.766179, -0.642361]] and translation vector: [1.819087, 5.36055, 1.286161], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.977666, 0.151417, -0.145745], [0.209051, 0.629394, -0.748438], [-0.021596, -0.762191, -0.646992]] and translation vector: [1.833647, 5.312907, 1.282765]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_167_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_167_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_167_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_167_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_167_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_167_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_167_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_167_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.207785, -0.462455, 0.861952], [-0.977184, 0.13779, -0.161637], [-0.044019, -0.875871, -0.480534]] and translation vector: [2.720584, 1.654419, 1.522448], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.211008, -0.462778, 0.860995], [-0.976592, 0.137438, -0.165466], [-0.04176, -0.875755, -0.480946]] and translation vector: [2.717844, 1.649691, 1.521912], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.235215, -0.460817, 0.855758], [-0.971358, 0.142015, -0.190515], [-0.033738, -0.876059, -0.481022]] and translation vector: [2.714951, 1.646852, 1.521954]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_168_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_168_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_168_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_168_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_168_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_168_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_168_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_168_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.686341, -0.358824, 0.632599], [-0.727213, -0.35045, 0.590209], [0.009912, -0.865119, -0.50147]] and translation vector: [2.486494, 4.601647, 1.455454], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.681394, -0.352774, 0.64129], [-0.731846, -0.340576, 0.590263], [0.010179, -0.871527, -0.490243]] and translation vector: [2.480601, 4.595852, 1.449959], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.622935, -0.386366, 0.680202], [-0.78205, -0.328403, 0.52967], [0.018734, -0.861901, -0.50673]] and translation vector: [2.469727, 4.596006, 1.44499]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_169_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_169_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_169_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_169_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_169_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_169_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_169_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_169_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.482968, -0.397392, 0.78027], [-0.874514, 0.173759, -0.452807], [0.044362, -0.901048, -0.431445]] and translation vector: [8.974016, 2.795387, 1.945192], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.496352, -0.388832, 0.776173], [-0.867003, 0.176647, -0.465943], [0.044064, -0.904216, -0.424797]] and translation vector: [8.98292, 2.792107, 1.939625], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.528625, -0.374982, 0.76154], [-0.848241, 0.199205, -0.490719], [0.032308, -0.905376, -0.42338]] and translation vector: [9.019628, 2.751405, 1.924251]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_170_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_170_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_170_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_170_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_170_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_170_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_170_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_170_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.891251, 0.378307, -0.25011], [0.443048, 0.608538, -0.658323], [-0.096846, -0.697542, -0.709969]] and translation vector: [4.935522, 3.588868, 1.45033], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.887006, 0.383874, -0.256633], [0.452131, 0.60913, -0.651566], [-0.093796, -0.693975, -0.713864]] and translation vector: [4.940225, 3.582454, 1.45688], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.875452, 0.38739, -0.288987], [0.475285, 0.581583, -0.660201], [-0.087685, -0.715325, -0.693269]] and translation vector: [4.970656, 3.561422, 1.469218]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_171_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_171_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_171_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_171_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_171_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_171_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_171_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_171_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.530794, 0.426739, -0.732224], [0.841151, 0.159702, -0.516681], [-0.10355, -0.890162, -0.443721]] and translation vector: [5.418979, 4.373359, 1.385162], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.532043, 0.421439, -0.734384], [0.841755, 0.169492, -0.512564], [-0.091542, -0.890877, -0.444925]] and translation vector: [5.415919, 4.39552, 1.38299], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.539398, 0.40032, -0.740806], [0.839984, 0.194205, -0.506666], [-0.05896, -0.89556, -0.441017]] and translation vector: [5.414681, 4.463818, 1.378667]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_172_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_172_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_172_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_172_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_172_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_172_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_172_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_172_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.086843, 0.425015, -0.901011], [0.995696, 0.066429, -0.064634], [0.032383, -0.902745, -0.428955]] and translation vector: [4.261571, 5.85756, 1.66629], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.086953, 0.422316, -0.902268], [0.995713, 0.06553, -0.065286], [0.031554, -0.904077, -0.426204]] and translation vector: [4.260677, 5.865657, 1.669414], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.081846, 0.421358, -0.903194], [0.995927, 0.068976, -0.058071], [0.03783, -0.904268, -0.425287]] and translation vector: [4.263237, 5.864869, 1.673574]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_173_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_173_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_173_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_173_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_173_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_173_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_173_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_173_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.725417, 0.297171, -0.620854], [0.687848, -0.279954, 0.669695], [0.025203, -0.912861, -0.407492]] and translation vector: [3.434752, 3.057745, 1.556519], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.722045, 0.303192, -0.621873], [0.691238, -0.278447, 0.666827], [0.029018, -0.911341, -0.410629]] and translation vector: [3.433538, 3.052318, 1.549734], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.693174, 0.307801, -0.651742], [0.720057, -0.255516, 0.645158], [0.032049, -0.916499, -0.398751]] and translation vector: [3.420418, 3.038936, 1.558387]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_174_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_174_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_174_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_174_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_174_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_174_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_174_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_174_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.032646, 0.194727, -0.980314], [0.998594, -0.034636, -0.040135], [-0.04177, -0.980246, -0.193322]] and translation vector: [3.506056, 2.493951, 1.706783], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.038857, 0.192835, -0.980462], [0.998032, -0.040846, -0.047587], [-0.049225, -0.980381, -0.190868]] and translation vector: [3.502031, 2.499079, 1.701362], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.064111, 0.226282, -0.97195], [0.996323, -0.040955, -0.075254], [-0.056835, -0.9732, -0.222824]] and translation vector: [3.459589, 2.490182, 1.701209]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_175_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_175_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_175_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_175_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_175_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_175_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_175_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_175_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.881415, -0.308012, 0.3581], [-0.47008, 0.646119, -0.601294], [-0.046169, -0.698325, -0.71429]] and translation vector: [3.147524, 1.689608, 1.273114], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.879224, -0.311908, 0.360109], [-0.474637, 0.638627, -0.605703], [-0.041052, -0.703469, -0.709539]] and translation vector: [3.141599, 1.689583, 1.27073], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.878218, -0.323901, 0.351882], [-0.476941, 0.647734, -0.594111], [-0.035492, -0.689586, -0.723334]] and translation vector: [3.127244, 1.682619, 1.264528]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_176_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_176_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_176_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_176_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_176_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_176_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_176_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_176_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.467192, 0.317292, -0.825262], [0.883302, -0.126478, 0.451421], [0.038855, -0.939856, -0.339354]] and translation vector: [2.723032, 3.168159, 1.438168], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.467636, 0.312306, -0.826911], [0.883318, -0.130557, 0.450227], [0.03265, -0.940968, -0.336919]] and translation vector: [2.722188, 3.168039, 1.441817], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.470302, 0.305008, -0.828122], [0.881834, -0.125828, 0.454462], [0.034414, -0.944001, -0.328143]] and translation vector: [2.718763, 3.171866, 1.451475]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_177_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_177_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_177_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_177_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_177_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_177_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_177_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_177_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.943065, -0.17817, 0.280864], [-0.332105, 0.550897, -0.765649], [-0.018311, -0.815333, -0.578703]] and translation vector: [2.74599, 1.673222, 1.294065], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.942639, -0.173012, 0.285478], [-0.332909, 0.550136, -0.765848], [-0.024551, -0.816957, -0.576177]] and translation vector: [2.737266, 1.663808, 1.300966], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.942881, -0.164787, 0.289518], [-0.331772, 0.54291, -0.771477], [-0.030053, -0.823465, -0.566571]] and translation vector: [2.712684, 1.645235, 1.301017]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_178_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_178_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_178_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_178_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_178_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_178_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_178_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_178_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.695296, -0.421579, 0.582095], [-0.717067, -0.351947, 0.601622], [-0.048765, -0.835707, -0.547007]] and translation vector: [2.470866, 0.652559, 1.473924], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.695871, -0.418819, 0.583399], [-0.716734, -0.353708, 0.600986], [-0.045352, -0.83635, -0.546317]] and translation vector: [2.469546, 0.651931, 1.473078], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.693531, -0.42586, 0.581085], [-0.719633, -0.371637, 0.586528], [-0.033826, -0.824943, -0.564204]] and translation vector: [2.467637, 0.650008, 1.462326]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_179_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_179_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_179_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_179_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_179_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_179_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_179_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_179_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.748873, -0.374013, 0.547087], [-0.662404, -0.447673, 0.600675], [0.020256, -0.812221, -0.582998]] and translation vector: [3.709567, 4.406117, 1.261793], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.747082, -0.370975, 0.551585], [-0.664465, -0.440253, 0.603874], [0.018814, -0.817652, -0.575405]] and translation vector: [3.708719, 4.403161, 1.261416], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.743545, -0.377269, 0.552096], [-0.66849, -0.439378, 0.600057], [0.016196, -0.81524, -0.578898]] and translation vector: [3.708687, 4.402202, 1.259327]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_180_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_180_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_180_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_180_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_180_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_180_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_180_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_180_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.925351, 0.122106, -0.358909], [0.376741, 0.190476, -0.906524], [-0.042329, -0.974068, -0.222259]] and translation vector: [4.735593, 2.732706, 1.21643], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.924788, 0.125024, -0.359357], [0.377675, 0.187086, -0.906841], [-0.046146, -0.974355, -0.220234]] and translation vector: [4.740286, 2.733964, 1.218072], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.925715, 0.103215, -0.363867], [0.37582, 0.142741, -0.915633], [-0.042569, -0.984363, -0.170928]] and translation vector: [4.730338, 2.742957, 1.247444]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_181_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_181_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_181_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_181_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_181_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_181_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_181_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_181_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.264492, -0.222038, 0.938479], [-0.962334, 0.002714, 0.271857], [-0.062909, -0.975034, -0.212957]] and translation vector: [0.925816, 4.784833, 1.497389], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.263009, -0.220134, 0.939344], [-0.962729, 0.003779, 0.270443], [-0.063084, -0.975462, -0.210935]] and translation vector: [0.925807, 4.784041, 1.498483], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.243124, -0.227834, 0.942858], [-0.968357, -0.000546, 0.249567], [-0.056345, -0.9737, -0.220758]] and translation vector: [0.931793, 4.784123, 1.4987]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_182_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_182_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_182_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_182_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_182_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_182_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_182_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_182_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.173351, 0.592298, -0.78685], [0.984858, -0.105806, 0.137329], [-0.001913, -0.798742, -0.601671]] and translation vector: [3.264189, 1.940071, 1.28435], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.172933, 0.589263, -0.789217], [0.98493, -0.105695, 0.136901], [-0.002745, -0.800998, -0.598661]] and translation vector: [3.267153, 1.942133, 1.284021], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.139436, 0.623012, -0.769684], [0.990166, -0.096604, 0.101183], [-0.011316, -0.776224, -0.630355]] and translation vector: [3.29114, 1.970334, 1.268272]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_183_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_183_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_183_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_183_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_183_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_183_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_183_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_183_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.442667, -0.46733, 0.765277], [-0.896368, 0.253361, -0.363776], [-0.023888, -0.847001, -0.531054]] and translation vector: [2.453469, 1.905797, 1.451684], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.441405, -0.472001, 0.763136], [-0.897015, 0.253848, -0.361837], [-0.022933, -0.844261, -0.535442]] and translation vector: [2.45238, 1.90449, 1.449179], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.442687, -0.461983, 0.768505], [-0.8965, 0.24504, -0.369112], [-0.017791, -0.852366, -0.522643]] and translation vector: [2.451253, 1.899634, 1.462124]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_184_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_184_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_184_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_184_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_184_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_184_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_184_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_184_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.643628, -0.362528, 0.674031], [-0.765241, -0.290748, 0.574345], [-0.012243, -0.88546, -0.464555]] and translation vector: [2.632762, 2.243425, 1.452714], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.642371, -0.361874, 0.675579], [-0.76623, -0.285016, 0.575898], [-0.015852, -0.887589, -0.460364]] and translation vector: [2.634792, 2.237319, 1.452971], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.637523, -0.35682, 0.682821], [-0.770314, -0.279737, 0.573031], [-0.013459, -0.891306, -0.453202]] and translation vector: [2.638724, 2.233015, 1.462981]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_185_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_185_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_185_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_185_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_185_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_185_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_185_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_185_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.731293, 0.384445, -0.563394], [0.682011, 0.401944, -0.610984], [-0.008437, -0.831049, -0.556135]] and translation vector: [5.176627, 2.209938, 1.427488], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.733453, 0.387758, -0.558292], [0.679719, 0.411882, -0.606907], [-0.005383, -0.82462, -0.565663]] and translation vector: [5.175584, 2.209993, 1.422561], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.739748, 0.384821, -0.551984], [0.672884, 0.424134, -0.606084], [0.000881, -0.819771, -0.572692]] and translation vector: [5.164479, 2.208437, 1.426833]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_186_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_186_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_186_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_186_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_186_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_186_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_186_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_186_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.996822, -0.027813, -0.074656], [0.056495, -0.413943, 0.908548], [-0.056173, -0.909878, -0.411056]] and translation vector: [4.405487, 5.403347, 1.494535], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.996757, -0.027349, -0.075677], [0.057466, -0.416379, 0.907373], [-0.056327, -0.90878, -0.413457]] and translation vector: [4.408994, 5.403286, 1.494292], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.997265, -0.029561, -0.067745], [0.049832, -0.408017, 0.911613], [-0.05459, -0.912496, -0.405428]] and translation vector: [4.415172, 5.400004, 1.499593]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_187_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_187_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_187_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_187_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_187_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_187_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_187_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_187_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.789457, 0.162095, -0.592016], [0.613764, 0.197318, -0.764434], [-0.007096, -0.966846, -0.255262]] and translation vector: [5.114759, 3.17533, 1.386193], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.785271, 0.158609, -0.598492], [0.619131, 0.193201, -0.761151], [-0.005096, -0.968255, -0.249915]] and translation vector: [5.11251, 3.170745, 1.383731], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.782732, 0.165019, -0.600083], [0.622288, 0.192888, -0.758652], [-0.009443, -0.967245, -0.253669]] and translation vector: [5.104394, 3.153102, 1.37449]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_188_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_188_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_188_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_188_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_188_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_188_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_188_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_188_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.95695, -0.100486, 0.272304], [-0.288986, 0.24231, -0.92616], [0.027085, -0.964981, -0.260918]] and translation vector: [1.227478, 4.879099, 1.55452], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.957752, -0.097454, 0.27058], [-0.286469, 0.240112, -0.927514], [0.025421, -0.965841, -0.257885]] and translation vector: [1.221714, 4.885019, 1.554874], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.941817, -0.081741, 0.326036], [-0.336056, 0.20922, -0.91831], [0.00685, -0.974446, -0.224516]] and translation vector: [1.204022, 4.901892, 1.569033]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_189_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_189_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_189_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_189_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_189_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_189_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_189_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_189_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.982764, 0.054289, -0.17671], [0.184841, -0.27426, 0.943724], [0.002769, -0.960122, -0.279568]] and translation vector: [4.072058, 1.220293, 1.47625], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.982485, 0.057917, -0.177113], [0.186218, -0.270474, 0.944546], [0.0068, -0.960984, -0.276522]] and translation vector: [4.071517, 1.218265, 1.477941], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.980674, 0.05705, -0.187148], [0.195532, -0.252477, 0.947641], [0.006813, -0.96592, -0.258752]] and translation vector: [4.0711, 1.209071, 1.48705]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_190_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_190_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_190_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_190_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_190_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_190_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_190_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_190_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.693623, 0.392298, -0.604144], [0.720137, 0.397492, -0.568686], [0.017048, -0.82952, -0.558217]] and translation vector: [2.706242, 2.586761, 1.453005], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.690051, 0.396658, -0.605386], [0.723517, 0.399766, -0.56277], [0.018785, -0.826347, -0.562848]] and translation vector: [2.704536, 2.590014, 1.45316], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.674504, 0.428853, -0.600941], [0.737993, 0.414011, -0.53288], [0.020269, -0.80292, -0.595742]] and translation vector: [2.699649, 2.603579, 1.443268]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_191_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_191_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_191_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_191_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_191_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_191_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_191_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_191_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.610102, 0.375008, -0.697958], [0.791763, 0.255448, -0.554849], [-0.029781, -0.891132, -0.452767]] and translation vector: [2.349929, 1.419923, 1.358478], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.607496, 0.374505, -0.700496], [0.793845, 0.255679, -0.551759], [-0.027534, -0.891277, -0.452623]] and translation vector: [2.354864, 1.421781, 1.358478], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.579764, 0.373065, -0.724359], [0.814546, 0.24389, -0.526338], [-0.019694, -0.895176, -0.445277]] and translation vector: [2.359462, 1.423068, 1.367348]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_192_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_192_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_192_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_192_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_192_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_192_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_192_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_192_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.081815, 0.638296, -0.765431], [0.996577, -0.061545, 0.055199], [-0.011875, -0.767327, -0.641146]] and translation vector: [3.004073, 1.570726, 1.431248], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.083332, 0.64082, -0.763155], [0.996457, -0.062303, 0.056492], [-0.011346, -0.765159, -0.643742]] and translation vector: [3.00242, 1.571458, 1.432065], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.083112, 0.654572, -0.751417], [0.996444, -0.065065, 0.053535], [-0.013848, -0.753195, -0.657652]] and translation vector: [3.01468, 1.572497, 1.43131]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_193_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_193_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_193_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_193_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_193_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_193_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_193_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_193_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.348231, 0.123124, -0.929288], [0.936413, -1.6e-05, 0.350899], [0.043189, -0.992391, -0.1153]] and translation vector: [2.712005, 2.075202, 1.464169], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.348319, 0.120186, -0.929639], [0.93641, 0.000395, 0.350907], [0.042542, -0.992751, -0.112406]] and translation vector: [2.712393, 2.076758, 1.463984], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.330226, 0.128954, -0.935052], [0.94318, -0.00633, 0.332223], [0.036923, -0.99163, -0.123717]] and translation vector: [2.702959, 2.087481, 1.468829]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_194_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_194_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_194_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_194_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_194_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_194_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_194_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_194_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.524333, 0.441188, -0.728305], [0.848808, -0.202677, 0.488311], [0.067827, -0.874228, -0.480754]] and translation vector: [3.10696, 1.250425, 1.344077], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.531491, 0.437044, -0.72561], [0.844432, -0.205894, 0.494513], [0.066725, -0.875557, -0.478485]] and translation vector: [3.107462, 1.25329, 1.344278], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.56012, 0.431145, -0.707375], [0.826071, -0.226557, 0.516021], [0.062219, -0.873376, -0.483056]] and translation vector: [3.110022, 1.262991, 1.348097]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_195_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_195_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_195_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_195_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_195_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_195_5.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_195_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_195_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.000188, -0.47362, 0.88073], [-0.997828, 0.057931, 0.031365], [-0.065877, -0.878822, -0.47258]] and translation vector: [4.366519, 5.511691, 1.307889], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.002248, -0.465195, 0.885205], [-0.998254, 0.053289, 0.02547], [-0.05902, -0.883603, -0.464503]] and translation vector: [4.36891, 5.516212, 1.317108], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.024267, -0.440835, 0.89726], [-0.998159, 0.06059, 0.002773], [-0.055588, -0.895541, -0.441493]] and translation vector: [4.36929, 5.527184, 1.331889]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "B"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_196_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_196_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_196_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_196_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_196_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_196_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_196_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_196_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.819759, -0.274444, 0.502669], [-0.572709, 0.39303, -0.719397], [-0.00013, -0.877615, -0.479366]] and translation vector: [2.765326, 1.370172, 1.355227], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.819555, -0.26888, 0.505998], [-0.572993, 0.389095, -0.721307], [-0.002936, -0.881084, -0.472951]] and translation vector: [2.765196, 1.369276, 1.358405], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.80543, -0.264338, 0.530479], [-0.592674, 0.365802, -0.717584], [-0.004366, -0.892365, -0.451294]] and translation vector: [2.783833, 1.382351, 1.368477]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_197_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_197_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_197_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_197_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_197_4.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_197_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_197_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_197_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.060487, 0.154719, -0.986105], [0.998165, 0.006603, -0.060191], [-0.002801, -0.987936, -0.154835]] and translation vector: [6.630666, 2.572317, 1.44523], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.062036, 0.175232, -0.982571], [0.998074, 0.011306, -0.060998], [0.00042, -0.984462, -0.175596]] and translation vector: [6.62843, 2.567178, 1.442285], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.077658, 0.209818, -0.974652], [0.996978, 0.01426, -0.076367], [-0.002124, -0.977636, -0.210291]] and translation vector: [6.626263, 2.56408, 1.439607]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "A"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_198_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_198_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_198_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_198_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_198_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_198_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_198_6.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_198_7.png"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.286652, 0.220257, -0.932372], [0.958024, -0.061246, 0.28007], [0.004584, -0.973517, -0.228568]] and translation vector: [3.76659, 1.676076, 1.452194], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[0.299829, 0.216367, -0.929133], [0.953977, -0.07366, 0.290693], [-0.005544, -0.973529, -0.228495]] and translation vector: [3.753121, 1.670498, 1.452776], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[0.332229, 0.205241, -0.920597], [0.943053, -0.089416, 0.320398], [-0.016558, -0.974618, -0.22326]] and translation vector: [3.692962, 1.621141, 1.4585]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "C"}, "task": "threeD_Scene_Reconstruction"}
{"source": "SCANNET_threed_scene_reconstruction", "options": "A: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image", "visual_input_component": "3d image", "input": {"input_image_path": ["3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_199_0.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_199_1.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_199_2.jpg", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_199_3.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_199_4.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_199_5.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_199_6.png", "3D-spatial/threeD_Scene_Reconstruction/threeD_Scene_Reconstruction_199_7.jpg"], "question": "Given a pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.409087, -0.112571, 0.905525], [-0.910894, 0.109148, -0.397943], [-0.05404, -0.987631, -0.147191]] and translation vector: [4.421403, 3.579741, 1.526424], and another pair of RGB and depth images with the corresponding camera pose, i.e., rotation matrix: [[-0.417977, -0.10834, 0.901974], [-0.906895, 0.107978, -0.407287], [-0.053267, -0.988232, -0.143386]] and translation vector: [4.418822, 3.582731, 1.526625], please estimate the RGB image for the query camera pose, i.e., rotation matrix: [[-0.44932, -0.10036, 0.887716], [-0.891042, 0.12205, -0.437205], [-0.064468, -0.987437, -0.144264]] and translation vector: [4.403283, 3.625828, 1.518726]. The provided camera poses represent the the transformation from the camera coordinate system to the world coordinate system.", "context": "Your task is to reconstruct the 3D geometry of a scene. This is tested through the image retrieval for a specific camera pose. The input images are the first 4 images\nSelect from the following choices.\nA: The 5th image\nB: The 6th image\nC: The 7th image\nD: The 8th image"}, "output": {"output_text": "D"}, "task": "threeD_Scene_Reconstruction"}
{"source": "PKUMMD", "options": "A: read a book\nB: drink water\nC: ride a bike\nD: play guitar", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_0_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: read a book\nB: drink water\nC: ride a bike\nD: play guitar"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: running\nB: sitting down\nC: lying down\nD: standing up", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_1_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: running\nB: sitting down\nC: lying down\nD: standing up"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: lying down\nB: standing up\nC: sitting down\nD: jumping", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_2_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: lying down\nB: standing up\nC: sitting down\nD: jumping"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride bicycle\nB: play guitar\nC: write letter\nD: eat meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_3_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride bicycle\nB: play guitar\nC: write letter\nD: eat meal"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit\nB: jump\nC: pickup\nD: run", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_4_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit\nB: jump\nC: pickup\nD: run"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: make a phone call\nB: play a guitar\nC: ride a bicycle\nD: drink a coffee", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_5_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: make a phone call\nB: play a guitar\nC: ride a bicycle\nD: drink a coffee"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: read book\nB: play piano\nC: jog\nD: eat meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_6_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: read book\nB: play piano\nC: jog\nD: eat meal"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: pickup\nB: sit\nC: run\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_7_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: pickup\nB: sit\nC: run\nD: jump"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: running\nB: sleeping\nC: dancing\nD: reading", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_8_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: running\nB: sleeping\nC: dancing\nD: reading"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride a bicycle\nB: make a phone call\nC: cook a meal\nD: play a piano", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_9_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride a bicycle\nB: make a phone call\nC: cook a meal\nD: play a piano"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: read a book\nB: tie shoelaces\nC: check time (from watch)\nD: wave hand", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_10_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: read a book\nB: tie shoelaces\nC: check time (from watch)\nD: wave hand"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: raise hand (greeting)\nB: touch chest (stomachache\nC: tie shoelaces (preparing to run)\nD: clap hands (applause)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_11_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: raise hand (greeting)\nB: touch chest (stomachache\nC: tie shoelaces (preparing to run)\nD: clap hands (applause)"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: pickup\nB: sit\nC: jump\nD: run", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_12_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: pickup\nB: sit\nC: jump\nD: run"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: reading a book\nB: cooking a meal\nC: writing a letter\nD: brushing teeth", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_13_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: reading a book\nB: cooking a meal\nC: writing a letter\nD: brushing teeth"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit down\nB: jump\nC: wave hand\nD: touch chest (stomachache", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_14_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit down\nB: jump\nC: wave hand\nD: touch chest (stomachache"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: standing up\nB: jumping\nC: running\nD: sitting down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_15_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: standing up\nB: jumping\nC: running\nD: sitting down"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: pickup\nB: run\nC: sit down\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_16_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: pickup\nB: run\nC: sit down\nD: jump"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jogging\nB: brushing teeth\nC: eating\nD: reading a book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_17_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jogging\nB: brushing teeth\nC: eating\nD: reading a book"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat sandwich\nB: read book\nC: ride bicycle\nD: wear jacket", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_18_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat sandwich\nB: read book\nC: ride bicycle\nD: wear jacket"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: take off a hat\nB: tie shoelaces\nC: put on a hat\nD: put on gloves", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_19_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: take off a hat\nB: tie shoelaces\nC: put on a hat\nD: put on gloves"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride bicycle\nB: wear jacket\nC: read book\nD: cook dinner", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_20_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride bicycle\nB: wear jacket\nC: read book\nD: cook dinner"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride a bicycle\nB: tie a shoelace\nC: drink water\nD: read a book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_21_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride a bicycle\nB: tie a shoelace\nC: drink water\nD: read a book"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting down\nB: jumping\nC: standing up\nD: lying down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_22_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting down\nB: jumping\nC: standing up\nD: lying down"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drink water\nB: read a book\nC: tie shoes\nD: climb stairs", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_23_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drink water\nB: read a book\nC: tie shoes\nD: climb stairs"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play a guitar\nB: drink water\nC: ride a bike\nD: write a note", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_24_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play a guitar\nB: drink water\nC: ride a bike\nD: write a note"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: take off a hat\nB: put on a hat\nC: pick up a book\nD: tie shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_25_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: take off a hat\nB: put on a hat\nC: pick up a book\nD: tie shoelaces"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride bike\nB: read book\nC: play guitar\nD: eat meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_26_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride bike\nB: read book\nC: play guitar\nD: eat meal"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: touch chest (stomachache\nB: throw a ball\nC: jump up\nD: tie shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_27_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: touch chest (stomachache\nB: throw a ball\nC: jump up\nD: tie shoelaces"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave\nB: sit down\nC: jump\nD: pickup", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_28_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave\nB: sit down\nC: jump\nD: pickup"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat sandwich\nB: ride bicycle\nC: wear jacket\nD: play guitar", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_29_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat sandwich\nB: ride bicycle\nC: wear jacket\nD: play guitar"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: dancing\nB: reading\nC: sleeping\nD: cooking", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_30_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: dancing\nB: reading\nC: sleeping\nD: cooking"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: brushing teeth\nB: washing face\nC: brushing hair\nD: combing hair", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_31_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: brushing teeth\nB: washing face\nC: brushing hair\nD: combing hair"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoelaces\nB: check time (from watch)\nC: drink water\nD: read a book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_32_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoelaces\nB: check time (from watch)\nC: drink water\nD: read a book"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jump up\nB: touch chest (stomachache\nC: wave hand\nD: sit down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_33_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jump up\nB: touch chest (stomachache\nC: wave hand\nD: sit down"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: read a book\nB: tie shoelaces\nC: eat an apple\nD: check time (from watch)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_34_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: read a book\nB: tie shoelaces\nC: eat an apple\nD: check time (from watch)"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: run\nB: drop\nC: jump\nD: sit", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_35_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: run\nB: drop\nC: jump\nD: sit"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: riding a bike\nB: baking a cake\nC: brushing teeth\nD: playing a guitar", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_36_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: riding a bike\nB: baking a cake\nC: brushing teeth\nD: playing a guitar"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: write on a board\nB: tie shoelaces\nC: check time (from watch)\nD: drink water", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_37_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: write on a board\nB: tie shoelaces\nC: check time (from watch)\nD: drink water"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: touch chest (stomachache\nB: clapping hands\nC: tying shoes\nD: jumping in place", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_38_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: touch chest (stomachache\nB: clapping hands\nC: tying shoes\nD: jumping in place"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting down\nB: standing up\nC: jumping\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_39_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting down\nB: standing up\nC: jumping\nD: running"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play guitar\nB: run\nC: sleep\nD: eat meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_40_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play guitar\nB: run\nC: sleep\nD: eat meal"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play piano\nB: eat meal\nC: paint picture\nD: ride bicycle", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_41_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play piano\nB: eat meal\nC: paint picture\nD: ride bicycle"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: cooking\nB: reading\nC: dancing\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_42_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: cooking\nB: reading\nC: dancing\nD: running"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jumping\nB: sitting down\nC: lying down\nD: standing up", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_43_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jumping\nB: sitting down\nC: lying down\nD: standing up"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride a bike\nB: eat a sandwich\nC: make a phone call\nD: tie a shoe", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_44_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride a bike\nB: eat a sandwich\nC: make a phone call\nD: tie a shoe"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jump\nB: pickup\nC: sit down\nD: wave", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_45_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jump\nB: pickup\nC: sit down\nD: wave"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: playing guitar\nB: tieing shoes\nC: drinking water\nD: brushing teeth", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_46_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: playing guitar\nB: tieing shoes\nC: drinking water\nD: brushing teeth"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: paint a picture\nB: eat meal\nC: run a marathon\nD: play a musical instrument", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_47_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: paint a picture\nB: eat meal\nC: run a marathon\nD: play a musical instrument"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave hand\nB: check time (from watch)\nC: tie shoelaces\nD: drink water", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_48_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave hand\nB: check time (from watch)\nC: tie shoelaces\nD: drink water"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: brushing teeth\nB: tying shoes\nC: cooking food\nD: watering plants", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_49_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: brushing teeth\nB: tying shoes\nC: cooking food\nD: watering plants"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: put on a hat\nB: take off a hat\nC: button a shirt\nD: tie a shoelace", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_50_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: put on a hat\nB: take off a hat\nC: button a shirt\nD: tie a shoelace"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie a shoe\nB: make a phone call\nC: play a guitar\nD: cook a meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_51_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie a shoe\nB: make a phone call\nC: play a guitar\nD: cook a meal"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drop\nB: jump\nC: run\nD: sit", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_52_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drop\nB: jump\nC: run\nD: sit"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave hand\nB: scratch head\nC: touch chest (stomachache\nD: jump up and down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_53_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave hand\nB: scratch head\nC: touch chest (stomachache\nD: jump up and down"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: lying down\nB: standing up\nC: running\nD: sitting down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_54_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: lying down\nB: standing up\nC: running\nD: sitting down"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: pickup\nB: run\nC: jump\nD: sit", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_55_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: pickup\nB: run\nC: jump\nD: sit"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wipe face\nB: snap fingers\nC: brush hair\nD: tie shoelace", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_56_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wipe face\nB: snap fingers\nC: brush hair\nD: tie shoelace"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sleeping\nB: dancing\nC: reading\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_57_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sleeping\nB: dancing\nC: reading\nD: running"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wear jacket\nB: sit down\nC: jump\nD: tie shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_58_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wear jacket\nB: sit down\nC: jump\nD: tie shoelaces"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting down\nB: jumping\nC: standing up\nD: lying down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_59_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting down\nB: jumping\nC: standing up\nD: lying down"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride a bike\nB: read a book\nC: eat meal\nD: play a musical instrument", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_60_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride a bike\nB: read a book\nC: eat meal\nD: play a musical instrument"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: touch back (backache)\nB: clap hands\nC: sit down\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_61_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: touch back (backache)\nB: clap hands\nC: sit down\nD: jump"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting down\nB: lying down\nC: standing up\nD: jumping", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_62_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting down\nB: lying down\nC: standing up\nD: jumping"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat meal\nB: write letter\nC: ride bicycle\nD: play guitar", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_63_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat meal\nB: write letter\nC: ride bicycle\nD: play guitar"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave hand\nB: pick up a book\nC: wipe face\nD: tie shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_64_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave hand\nB: pick up a book\nC: wipe face\nD: tie shoelaces"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: read a book\nB: play a guitar\nC: make a phone call\nD: cook a meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_65_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: read a book\nB: play a guitar\nC: make a phone call\nD: cook a meal"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: read a book\nB: make a phone call\nC: eat a meal\nD: play a video game", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_66_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: read a book\nB: make a phone call\nC: eat a meal\nD: play a video game"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drop\nB: jump\nC: pick\nD: hold", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_67_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drop\nB: jump\nC: pick\nD: hold"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: climb ladder\nB: kick ball\nC: tie shoe\nD: wipe face", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_68_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: climb ladder\nB: kick ball\nC: tie shoe\nD: wipe face"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: dancing\nB: reading\nC: cooking\nD: sleeping", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_69_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: dancing\nB: reading\nC: cooking\nD: sleeping"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting down\nB: running\nC: standing up\nD: jumping", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_70_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting down\nB: running\nC: standing up\nD: jumping"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sleeping\nB: dancing\nC: cooking\nD: reading", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_71_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sleeping\nB: dancing\nC: cooking\nD: reading"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: running\nB: jumping\nC: sitting down\nD: standing up", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_72_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: running\nB: jumping\nC: sitting down\nD: standing up"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drop\nB: sit\nC: run\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_73_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drop\nB: sit\nC: run\nD: jump"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: brushing teeth\nB: riding a bicycle\nC: cooking dinner\nD: tying shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_74_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: brushing teeth\nB: riding a bicycle\nC: cooking dinner\nD: tying shoelaces"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave hand\nB: tie shoelace\nC: clap hands\nD: wipe face", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_75_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave hand\nB: tie shoelace\nC: clap hands\nD: wipe face"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: clap hands\nB: wave hand\nC: wipe face\nD: tie shoelace", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_76_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: clap hands\nB: wave hand\nC: wipe face\nD: tie shoelace"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: dancing\nB: sleeping\nC: reading\nD: cooking", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_77_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: dancing\nB: sleeping\nC: reading\nD: cooking"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride bicycle\nB: play guitar\nC: climb ladder\nD: drink water", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_78_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride bicycle\nB: play guitar\nC: climb ladder\nD: drink water"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sleeping\nB: reading\nC: dancing\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_79_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sleeping\nB: reading\nC: dancing\nD: running"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wipe face\nB: tie shoelaces\nC: brush hair\nD: write a note", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_80_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wipe face\nB: tie shoelaces\nC: brush hair\nD: write a note"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play a guitar\nB: read a book\nC: tie shoelaces\nD: check time (from watch)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_81_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play a guitar\nB: read a book\nC: tie shoelaces\nD: check time (from watch)"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: put on a hat\nB: open a door\nC: tie shoelaces\nD: take off a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_82_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: put on a hat\nB: open a door\nC: tie shoelaces\nD: take off a hat"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat food\nB: brush hair\nC: wipe face\nD: tie shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_83_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat food\nB: brush hair\nC: wipe face\nD: tie shoelaces"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: standing up\nB: jumping\nC: sitting down\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_84_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: standing up\nB: jumping\nC: sitting down\nD: running"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: adjust glasses\nB: check time (from watch)\nC: wave hand\nD: tie shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_85_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: adjust glasses\nB: check time (from watch)\nC: wave hand\nD: tie shoelaces"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: reading\nB: swimming\nC: cooking\nD: dancing", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_86_9.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: reading\nB: swimming\nC: cooking\nD: dancing"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit\nB: run\nC: drop\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_87_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit\nB: run\nC: drop\nD: jump"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jump\nB: wave hand\nC: tie shoelaces\nD: wipe face", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_88_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jump\nB: wave hand\nC: tie shoelaces\nD: wipe face"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: make a phone call\nB: eat a meal\nC: play a musical instrument\nD: tie a shoelace", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_89_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: make a phone call\nB: eat a meal\nC: play a musical instrument\nD: tie a shoelace"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drop\nB: sit\nC: jump\nD: run", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_90_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drop\nB: sit\nC: jump\nD: run"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: dancing\nB: reading\nC: jumping\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_91_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: dancing\nB: reading\nC: jumping\nD: running"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: bake a cake\nB: play a guitar\nC: ride a bike\nD: wear jacket", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_92_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: bake a cake\nB: play a guitar\nC: ride a bike\nD: wear jacket"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jumping\nB: touch back (backache)\nC: running\nD: sitting", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_93_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jumping\nB: touch back (backache)\nC: running\nD: sitting"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jumping\nB: sitting down\nC: running\nD: standing up", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_94_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jumping\nB: sitting down\nC: running\nD: standing up"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: write on blackboard\nB: touch chest (stomachache\nC: jump\nD: sit down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_95_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: write on blackboard\nB: touch chest (stomachache\nC: jump\nD: sit down"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: remove jacket\nB: tie shoelaces\nC: wear jacket\nD: sit down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_96_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: remove jacket\nB: tie shoelaces\nC: wear jacket\nD: sit down"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: put on a coat\nB: take off a hat\nC: put on a hat\nD: tie shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_97_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: put on a coat\nB: take off a hat\nC: put on a hat\nD: tie shoelaces"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jumping\nB: lying down\nC: sitting down\nD: standing up", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_98_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jumping\nB: lying down\nC: sitting down\nD: standing up"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play guitar\nB: tie shoelaces\nC: cook meal\nD: wear jacket", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_99_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play guitar\nB: tie shoelaces\nC: cook meal\nD: wear jacket"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit\nB: jump\nC: drop\nD: run", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_100_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit\nB: jump\nC: drop\nD: run"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: standing up\nB: jumping\nC: sitting down\nD: lying down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_101_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: standing up\nB: jumping\nC: sitting down\nD: lying down"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: brushing teeth\nB: playing basketball\nC: dancing\nD: cooking", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_102_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: brushing teeth\nB: playing basketball\nC: dancing\nD: cooking"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jump\nB: sit\nC: run\nD: bow", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_103_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jump\nB: sit\nC: run\nD: bow"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: clap\nB: drop\nC: run\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_104_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: clap\nB: drop\nC: run\nD: jump"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: remove shoes\nB: wear shoes\nC: remove jacket\nD: wear jacket", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_105_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: remove shoes\nB: wear shoes\nC: remove jacket\nD: wear jacket"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie a shoelace\nB: eat a sandwich\nC: put on a hat\nD: throw a ball", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_106_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie a shoelace\nB: eat a sandwich\nC: put on a hat\nD: throw a ball"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: check time (from watch)\nB: drink water\nC: tie shoelace\nD: wave hand", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_107_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: check time (from watch)\nB: drink water\nC: tie shoelace\nD: wave hand"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit down\nB: jump up\nC: take off hat\nD: wear jacket", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_108_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit down\nB: jump up\nC: take off hat\nD: wear jacket"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave hand\nB: clap hands\nC: tie shoe\nD: wipe face", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_109_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave hand\nB: clap hands\nC: tie shoe\nD: wipe face"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: kick a ball\nB: take off a hat\nC: wave a hand\nD: put on a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_110_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: kick a ball\nB: take off a hat\nC: wave a hand\nD: put on a hat"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie a shoelace\nB: put on a hat\nC: button a shirt\nD: take off a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_111_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie a shoelace\nB: put on a hat\nC: button a shirt\nD: take off a hat"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drink water\nB: play guitar\nC: tie shoelaces\nD: check time (from watch)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_112_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drink water\nB: play guitar\nC: tie shoelaces\nD: check time (from watch)"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play football\nB: ride a bike\nC: read a book\nD: eat meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_113_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play football\nB: ride a bike\nC: read a book\nD: eat meal"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit\nB: run\nC: jump\nD: drop", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_114_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit\nB: run\nC: jump\nD: drop"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: put on glasses\nB: tie a shoelace\nC: take off a hat\nD: put on a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_115_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: put on glasses\nB: tie a shoelace\nC: take off a hat\nD: put on a hat"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie a shoelace\nB: drink water\nC: ride a bicycle\nD: read a book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_116_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie a shoelace\nB: drink water\nC: ride a bicycle\nD: read a book"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: dance\nB: pickup\nC: sleep\nD: basketball", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_117_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: dance\nB: pickup\nC: sleep\nD: basketball"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat meal\nB: play guitar\nC: write letter\nD: ride bicycle", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_118_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat meal\nB: play guitar\nC: write letter\nD: ride bicycle"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drink water\nB: read a book\nC: play basketball\nD: ride a bicycle", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_119_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drink water\nB: read a book\nC: play basketball\nD: ride a bicycle"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoe\nB: wave hand\nC: check time (from watch)\nD: pick up phone", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_120_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoe\nB: wave hand\nC: check time (from watch)\nD: pick up phone"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie a shoelace\nB: put on a hat\nC: adjust a scarf\nD: take off a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_121_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie a shoelace\nB: put on a hat\nC: adjust a scarf\nD: take off a hat"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting down\nB: lying down\nC: standing up\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_122_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting down\nB: lying down\nC: standing up\nD: running"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: read a book\nB: drink water\nC: play guitar\nD: ride a bike", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_123_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: read a book\nB: drink water\nC: play guitar\nD: ride a bike"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoelaces\nB: check time (from watch)\nC: brush hair\nD: eat food", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_124_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoelaces\nB: check time (from watch)\nC: brush hair\nD: eat food"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave hand\nB: eat\nC: scratch head\nD: wipe face", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_125_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave hand\nB: eat\nC: scratch head\nD: wipe face"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drink water\nB: write notes\nC: play guitar\nD: tie shoes", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_126_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drink water\nB: write notes\nC: play guitar\nD: tie shoes"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: make a phone call\nB: write a letter\nC: tie shoes\nD: brush teeth", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_127_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: make a phone call\nB: write a letter\nC: tie shoes\nD: brush teeth"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: reading a book\nB: playing basketball\nC: brushing teeth\nD: riding a bicycle", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_128_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: reading a book\nB: playing basketball\nC: brushing teeth\nD: riding a bicycle"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: raise hand (question)\nB: touch chest (stomachache\nC: sit down (rest)\nD: step forward (walk)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_129_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: raise hand (question)\nB: touch chest (stomachache\nC: sit down (rest)\nD: step forward (walk)"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: talk on phone\nB: pick up object\nC: tie shoelaces\nD: wipe face", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_130_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: talk on phone\nB: pick up object\nC: tie shoelaces\nD: wipe face"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jump (exercise)\nB: touch chest (stomachache\nC: sit down (rest)\nD: wave hand (greeting)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_131_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jump (exercise)\nB: touch chest (stomachache\nC: sit down (rest)\nD: wave hand (greeting)"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: lying down\nB: sitting down\nC: standing up\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_132_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: lying down\nB: sitting down\nC: standing up\nD: running"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jumping\nB: sitting\nC: pickup\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_133_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jumping\nB: sitting\nC: pickup\nD: running"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit\nB: jump\nC: sleep\nD: pickup", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_134_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit\nB: jump\nC: sleep\nD: pickup"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat meal\nB: play guitar\nC: write letter\nD: read book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_135_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat meal\nB: play guitar\nC: write letter\nD: read book"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat meal\nB: play basketball\nC: walk dog\nD: sleep", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_136_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat meal\nB: play basketball\nC: walk dog\nD: sleep"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: dance\nB: read book\nC: play tennis\nD: eat meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_137_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: dance\nB: read book\nC: play tennis\nD: eat meal"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: playing piano\nB: brushing teeth\nC: riding a bike\nD: cooking dinner", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_138_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: playing piano\nB: brushing teeth\nC: riding a bike\nD: cooking dinner"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: run\nB: pickup\nC: jump\nD: sit down", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_139_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: run\nB: pickup\nC: jump\nD: sit down"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit\nB: pickup\nC: jump\nD: run", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_140_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit\nB: pickup\nC: jump\nD: run"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave a hand\nB: tie a shoe\nC: kick a ball\nD: drink water", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_141_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave a hand\nB: tie a shoe\nC: kick a ball\nD: drink water"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drop\nB: jump\nC: sit\nD: turn", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_142_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drop\nB: jump\nC: sit\nD: turn"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: pickup\nB: jump\nC: sit\nD: run", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_143_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: pickup\nB: jump\nC: sit\nD: run"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: make a phone call\nB: play a guitar\nC: cook a meal\nD: paint a picture", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_144_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: make a phone call\nB: play a guitar\nC: cook a meal\nD: paint a picture"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: check time (from watch)\nB: tie shoelaces\nC: eat an apple\nD: play guitar", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_145_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: check time (from watch)\nB: tie shoelaces\nC: eat an apple\nD: play guitar"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: running a marathon\nB: cooking dinner\nC: playing a guitar\nD: brushing teeth", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_146_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: running a marathon\nB: cooking dinner\nC: playing a guitar\nD: brushing teeth"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: make a phone call\nB: read a book\nC: ride a bicycle\nD: play a guitar", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_147_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: make a phone call\nB: read a book\nC: ride a bicycle\nD: play a guitar"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit down\nB: wave\nC: put on a hat\nD: take off a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_148_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit down\nB: wave\nC: put on a hat\nD: take off a hat"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: put on a hat\nB: tie shoes\nC: lift weights\nD: take off a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_149_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: put on a hat\nB: tie shoes\nC: lift weights\nD: take off a hat"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: adjust a tie\nB: take off a hat\nC: put on glasses\nD: put on a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_150_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: adjust a tie\nB: take off a hat\nC: put on glasses\nD: put on a hat"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: raise hand (greeting)\nB: jump (excited)\nC: touch chest (stomachache\nD: sit down (tired)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_151_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: raise hand (greeting)\nB: jump (excited)\nC: touch chest (stomachache\nD: sit down (tired)"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: raise hand\nB: touch chest (stomachache\nC: jump in place\nD: bend forward", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_152_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: raise hand\nB: touch chest (stomachache\nC: jump in place\nD: bend forward"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride bike\nB: read book\nC: play guitar\nD: eat meal", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_153_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride bike\nB: read book\nC: play guitar\nD: eat meal"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat meal\nB: play guitar\nC: read book\nD: ride bicycle", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_154_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat meal\nB: play guitar\nC: read book\nD: ride bicycle"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sleep\nB: read book\nC: eat meal\nD: run", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_155_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sleep\nB: read book\nC: eat meal\nD: run"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play guitar\nB: drink water\nC: jump rope\nD: read a book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_156_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play guitar\nB: drink water\nC: jump rope\nD: read a book"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: drop\nB: spin\nC: run\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_157_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: drop\nB: spin\nC: run\nD: jump"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride a bicycle\nB: play a guitar\nC: put on a hat\nD: write on a board", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_158_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride a bicycle\nB: play a guitar\nC: put on a hat\nD: write on a board"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: clap hands\nB: tie shoelaces\nC: touch back (backache)\nD: jump rope", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_159_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: clap hands\nB: tie shoelaces\nC: touch back (backache)\nD: jump rope"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoelaces\nB: drink water\nC: read a book\nD: play guitar", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_160_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoelaces\nB: drink water\nC: read a book\nD: play guitar"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting\nB: touch chest (stomachache\nC: jumping\nD: waving", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_161_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting\nB: touch chest (stomachache\nC: jumping\nD: waving"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play tennis\nB: read a book\nC: eat meal\nD: ride a bike", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_162_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play tennis\nB: read a book\nC: eat meal\nD: ride a bike"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: lying down\nB: running\nC: sitting down\nD: standing up", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_163_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: lying down\nB: running\nC: sitting down\nD: standing up"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jump\nB: run\nC: sit\nD: drop", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_164_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jump\nB: run\nC: sit\nD: drop"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: pick up object\nB: wipe face\nC: tie shoes\nD: jump rope", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_165_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: pick up object\nB: wipe face\nC: tie shoes\nD: jump rope"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sit\nB: run\nC: jump\nD: bow", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_166_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sit\nB: run\nC: jump\nD: bow"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: remove shoes\nB: wear jacket\nC: sit down\nD: drink water", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_167_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: remove shoes\nB: wear jacket\nC: sit down\nD: drink water"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: run in place\nB: wave hand\nC: touch chest (stomachache\nD: jump up", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_168_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: run in place\nB: wave hand\nC: touch chest (stomachache\nD: jump up"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoelaces\nB: open a door\nC: brush teeth\nD: check time (from watch)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_169_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoelaces\nB: open a door\nC: brush teeth\nD: check time (from watch)"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jump rope\nB: play guitar\nC: wipe face\nD: tie shoelaces", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_170_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jump rope\nB: play guitar\nC: wipe face\nD: tie shoelaces"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting down\nB: running\nC: lying down\nD: standing up", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_171_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting down\nB: running\nC: lying down\nD: standing up"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: button a shirt\nB: take off a hat\nC: put on a hat\nD: tie a shoe", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_172_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: button a shirt\nB: take off a hat\nC: put on a hat\nD: tie a shoe"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride bicycle\nB: play piano\nC: wear jacket\nD: eat apple", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_173_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride bicycle\nB: play piano\nC: wear jacket\nD: eat apple"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: brushing teeth\nB: jogging\nC: reading a book\nD: cooking", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_174_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: brushing teeth\nB: jogging\nC: reading a book\nD: cooking"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoelaces\nB: pick up bag\nC: clap hands\nD: check time (from watch)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_175_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoelaces\nB: pick up bag\nC: clap hands\nD: check time (from watch)"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave hand\nB: touch back (backache)\nC: eat food\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_176_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave hand\nB: touch back (backache)\nC: eat food\nD: jump"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: running\nB: reading\nC: dancing\nD: cooking", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_177_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: running\nB: reading\nC: dancing\nD: cooking"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play guitar\nB: eat meal\nC: dance\nD: read book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_178_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play guitar\nB: eat meal\nC: dance\nD: read book"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoelaces\nB: make a phone call\nC: brush teeth\nD: write in a notebook", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_179_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoelaces\nB: make a phone call\nC: brush teeth\nD: write in a notebook"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: check time (from watch)\nB: tie shoes\nC: take a photo\nD: read a book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_180_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: check time (from watch)\nB: tie shoes\nC: take a photo\nD: read a book"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat sandwich\nB: wear jacket\nC: play piano\nD: ride bicycle", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_181_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat sandwich\nB: wear jacket\nC: play piano\nD: ride bicycle"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: brushing teeth\nB: cooking\nC: jogging\nD: reading a book", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_182_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: brushing teeth\nB: cooking\nC: jogging\nD: reading a book"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: pick up phone\nB: tie shoe\nC: adjust glasses\nD: check time (from watch)", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_183_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: pick up phone\nB: tie shoe\nC: adjust glasses\nD: check time (from watch)"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jumping\nB: sitting down\nC: dancing\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_184_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jumping\nB: sitting down\nC: dancing\nD: running"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: ride bike\nB: play guitar\nC: wear jacket\nD: eat food", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_185_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: ride bike\nB: play guitar\nC: wear jacket\nD: eat food"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: eat a sandwich\nB: sit down\nC: play a guitar\nD: put on a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_186_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: eat a sandwich\nB: sit down\nC: play a guitar\nD: put on a hat"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: jump\nB: drop\nC: run\nD: climb", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_187_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: jump\nB: drop\nC: run\nD: climb"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: bow\nB: run\nC: sit\nD: jump", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_188_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: bow\nB: run\nC: sit\nD: jump"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: reading\nB: dancing\nC: sleeping\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_189_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: reading\nB: dancing\nC: sleeping\nD: running"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: wave\nB: jump\nC: bow\nD: sit", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_190_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: wave\nB: jump\nC: bow\nD: sit"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: standing up\nB: sitting down\nC: jumping\nD: running", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_191_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: standing up\nB: sitting down\nC: jumping\nD: running"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoelaces\nB: play guitar\nC: read a book\nD: drink water", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_192_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoelaces\nB: play guitar\nC: read a book\nD: drink water"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: running\nB: cooking\nC: reading\nD: dancing", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_193_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: running\nB: cooking\nC: reading\nD: dancing"}, "output": {"output_text": "C"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play guitar\nB: check time (from watch)\nC: tie shoelaces\nD: eat sandwich", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_194_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play guitar\nB: check time (from watch)\nC: tie shoelaces\nD: eat sandwich"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: make a phone call\nB: read a book\nC: ride a bicycle\nD: play a guitar", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_195_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: make a phone call\nB: read a book\nC: ride a bicycle\nD: play a guitar"}, "output": {"output_text": "A"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: tie shoes\nB: eat a sandwich\nC: read a book\nD: put on a hat", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_196_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: tie shoes\nB: eat a sandwich\nC: read a book\nD: put on a hat"}, "output": {"output_text": "D"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: sitting down\nB: standing up\nC: lying down\nD: jumping", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_197_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: sitting down\nB: standing up\nC: lying down\nD: jumping"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "PKUMMD", "options": "A: play a guitar\nB: make a phone call\nC: tie a shoelace\nD: ride a bicycle", "visual_input_component": "natural image", "input": {"input_image_path": ["3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_0.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_1.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_2.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_3.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_4.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_5.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_6.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_7.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_8.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_9.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_10.png", "3D-spatial/Multiview_Action_Recognition/Multiview_Action_Recognition_198_11.png"], "question": "Given the set of images from three different views (i.e., left, middle and right views), please identify the action that this person performs.", "context": "Your task is recognize human actions or activities in a scene using information from multiple views. \nSelect from the following choices.\nA: play a guitar\nB: make a phone call\nC: tie a shoelace\nD: ride a bicycle"}, "output": {"output_text": "B"}, "task": "Multiview_Action_Recognition"}
{"source": "EgoTaskQA", "options": "A: microwave\nB: refrigerator\nC: stove\nD: television", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_0_31.png"], "question": "which object changed its status when the person do the first action did before he/she point to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: microwave\nB: refrigerator\nC: stove\nD: television"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: apple1\nB: orange2\nC: banana3\nD: grape4", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_1_31.png"], "question": "which object changed its status when the person put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: apple1\nB: orange2\nC: banana3\nD: grape4"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: broken\nB: emptiness\nC: cleanliness\nD: fullness", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_2_31.png"], "question": "what status of cup changed while the person do the first action did before he/she wash something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: broken\nB: emptiness\nC: cleanliness\nD: fullness"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: locked\nB: opened\nC: half-opened\nD: closed", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_3_31.png"], "question": "what will the status of fridge change to if the actor do the first action in the video in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: locked\nB: opened\nC: half-opened\nD: closed"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put water-pot to table\nB: Placed water-pot on shelf\nC: Put water-pot to floor\nD: Moved water-pot to window", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_4_31.png"], "question": "How did the person changed the spatial relationships of the last object that has status change in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put water-pot to table\nB: Placed water-pot on shelf\nC: Put water-pot to floor\nD: Moved water-pot to window"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: I don't know\nB: maybe\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_5_31.png"], "question": "Does the first action did after the person point to something fulfills the preconditions of the action eating something with something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: I don't know\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Reading a book quietly\nB: Chopping vegetables on a board\nC: Put fish to basin using fishing-net\nD: Playing a musical instrument", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_6_31.png"], "question": "During which action does the person knows about the other person's action?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Reading a book quietly\nB: Chopping vegetables on a board\nC: Put fish to basin using fishing-net\nD: Playing a musical instrument"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: maybe\nC: no\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_7_31.png"], "question": "If the person did not get something from something, is the person able to open something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: maybe\nC: no\nD: yes"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Sit on the couch\nB: Turn on the TV\nC: Open microwave\nD: Close the window", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_8_31.png"], "question": "what will the other person do next?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Sit on the couch\nB: Turn on the TV\nC: Open microwave\nD: Close the window"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Pick up the book\nB: Put cup to the other person\nC: Turn off the lights\nD: Close the door", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_9_31.png"], "question": "If the person did not do the last action in the video, what remaining actions in the video is executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Pick up the book\nB: Put cup to the other person\nC: Turn off the lights\nD: Close the door"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: maybe\nC: yes\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_10_31.png"], "question": "If the person did not sweep something using something, is the person able to turn off something with something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: maybe\nC: yes\nD: sometimes"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: partially\nB: yes\nC: maybe\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_11_31.png"], "question": "Did the attribute of remote changed because of the first action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: partially\nB: yes\nC: maybe\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put sandwich to plate\nB: Take sandwich off the plate\nC: Throw sandwich away\nD: Put sandwich in the fridge", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_12_31.png"], "question": "What is the last action the person did in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put sandwich to plate\nB: Take sandwich off the plate\nC: Throw sandwich away\nD: Put sandwich in the fridge"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Wash knife\nB: Dry dishes\nC: Cook meal\nD: Sweep floor", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_13_31.png"], "question": "what is the other person doing while the person put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Wash knife\nB: Dry dishes\nC: Cook meal\nD: Sweep floor"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: not sure\nB: maybe\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_14_31.png"], "question": "Does the last action in the video fulfills the preconditions of the action putting something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: not sure\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put the fork in the fridge\nB: Dropped the fork on the floor\nC: Get fork from table\nD: Mixed the fork with a spoon", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_15_31.png"], "question": "How did the person changed the state of mixture of fork?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put the fork in the fridge\nB: Dropped the fork on the floor\nC: Get fork from table\nD: Mixed the fork with a spoon"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: no\nC: sometimes\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_16_31.png"], "question": "Does the last action in the video fulfills the preconditions of the action putting something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: no\nC: sometimes\nD: yes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: falling to the ground\nB: broken in half\nC: completely detached\nD: attached to knife base", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_17_31.png"], "question": "What is the status of knife after the person do the first action did before he/she get something from something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: falling to the ground\nB: broken in half\nC: completely detached\nD: attached to knife base"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: remote\nB: book\nC: lamp\nD: cup", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_18_31.png"], "question": "which object changed its status first in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: remote\nB: book\nC: lamp\nD: cup"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in fridge\nB: in microwave\nC: on table\nD: in sink", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_19_31.png"], "question": "what will the status of cup1 change to if the actor put something to something in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in fridge\nB: in microwave\nC: on table\nD: in sink"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: maybe\nC: yes\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_20_31.png"], "question": "Did the attribute of controller changed because of the first action did before the person point to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: maybe\nC: yes\nD: sometimes"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Add seasoning to meat\nB: Cut meat with a knife\nC: Put meat in oven\nD: Get meat from pan using fork", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_21_31.png"], "question": "How did the person changed the wrappedness of meat1?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Add seasoning to meat\nB: Cut meat with a knife\nC: Put meat in oven\nD: Get meat from pan using fork"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put the cup in the fridge\nB: Wash cup\nC: Break the cup\nD: Throw the cup away", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_22_31.png"], "question": "what will the person do next after this video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put the cup in the fridge\nB: Wash cup\nC: Break the cup\nD: Throw the cup away"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: sometimes\nC: no\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_23_31.png"], "question": "If the person did not do the first action did before he/she drink something with something, is the person able to wash something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: sometimes\nC: no\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get kettle from stove\nB: Pick up a spoon\nC: Open the fridge\nD: Turn on the faucet", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_24_31.png"], "question": "What is the first action the person did in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get kettle from stove\nB: Pick up a spoon\nC: Open the fridge\nD: Turn on the faucet"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get bowl from microwave\nB: Put a cup inside\nC: Turned it on without food\nD: Left it empty", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_25_31.png"], "question": "How did the person changed the emptiness of microwave?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get bowl from microwave\nB: Put a cup inside\nC: Turned it on without food\nD: Left it empty"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: frozen\nB: boiled\nC: cooked\nD: raw", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_26_31.png"], "question": "What does the person want meat1 to be for the action cooking something using something in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: frozen\nB: boiled\nC: cooked\nD: raw"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: next to the sink\nB: inside the cabinet\nC: under the table\nD: on top of knife", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_27_31.png"], "question": "What is the status of watermelon2 before the person put something to something using knife to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: next to the sink\nB: inside the cabinet\nC: under the table\nD: on top of knife"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: under juicer base\nB: behind juicer base\nC: next to juicer base\nD: on top of juicer base", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_28_31.png"], "question": "What does the person want the last object that has status change in the video to be for the action putting something to something in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: under juicer base\nB: behind juicer base\nC: next to juicer base\nD: on top of juicer base"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Hold controller\nB: Drop controller\nC: Throw controller\nD: Put controller to table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_29_31.png"], "question": "what is the other person doing while the person do the first action did after he/she turn off something with something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Hold controller\nB: Drop controller\nC: Throw controller\nD: Put controller to table"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: it depends\nC: no\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_30_31.png"], "question": "If the person did not open something, is the person able to pour from something into something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: it depends\nC: no\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: only if they do the second action\nC: no\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_31_31.png"], "question": "If the person did not do the first action in the video, will juicer-lid change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: only if they do the second action\nC: no\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Eating a snack\nB: Talking on the phone\nC: Point to TV\nD: Reading a book", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_32_31.png"], "question": "What is the person doing before he/she stand-up?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Eating a snack\nB: Talking on the phone\nC: Point to TV\nD: Reading a book"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Ignore it\nB: Wipe with a dry cloth\nC: Use a paper towel\nD: Wash cutting-board", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_33_31.png"], "question": "How did the person changed the cleanliness of cutting-board?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Ignore it\nB: Wipe with a dry cloth\nC: Use a paper towel\nD: Wash cutting-board"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: no\nC: possibly\nD: uncertain", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_34_31.png"], "question": "Did the attribute of closet changed because of the action closing something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: no\nC: possibly\nD: uncertain"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Open a window\nB: Read a book\nC: Make a phone call\nD: Get remote from shelf", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_35_31.png"], "question": "What is the person doing before he/she turn on something with something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Open a window\nB: Read a book\nC: Make a phone call\nD: Get remote from shelf"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: cannot be determined\nB: no\nC: yes\nD: not sure", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_36_31.png"], "question": "Is kettle-base visible to the other person before the person do the first action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: cannot be determined\nB: no\nC: yes\nD: not sure"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Drive a car\nB: Take a nap\nC: Put juicer to juicer-base\nD: Read a book", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_37_31.png"], "question": "what will the other person do next?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Drive a car\nB: Take a nap\nC: Put juicer to juicer-base\nD: Read a book"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: yes\nC: no\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_38_31.png"], "question": "If the person did not sweep something using something, will the last object that has status change in the video change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: yes\nC: no\nD: sometimes"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: taste\nB: color\nC: shape\nD: size", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_39_31.png"], "question": "what status will the person change on tomato?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: taste\nB: color\nC: shape\nD: size"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Move vacuum to closet\nB: Get vacuum from floor\nC: Leave vacuum outside\nD: Put vacuum on table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_40_31.png"], "question": "How did the person changed the spatial relationships of the last object that has status change in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Move vacuum to closet\nB: Get vacuum from floor\nC: Leave vacuum outside\nD: Put vacuum on table"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: door\nB: sink\nC: chair\nD: table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_41_31.png"], "question": "which object changed its status when the person do the first action did before he/she fill something using something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: door\nB: sink\nC: chair\nD: table"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: height\nB: wateredness\nC: humidity\nD: brightness", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_42_31.png"], "question": "Which attribute does the person want to change with plant for doing the action pouring from something into something in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: height\nB: wateredness\nC: humidity\nD: brightness"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: wateredness\nB: leaf size\nC: height\nD: color", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_43_31.png"], "question": "what status of plant changed while the person do the first action did after he/she fill something using something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: wateredness\nB: leaf size\nC: height\nD: color"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Cook food in the kitchen\nB: Watch TV instead of fishing\nC: Get fishing-net and fish from basin and fishing-net\nD: Play a game on the computer", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_44_31.png"], "question": "If the person did not fill something using something, what remaining actions in the video is executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Cook food in the kitchen\nB: Watch TV instead of fishing\nC: Get fishing-net and fish from basin and fishing-net\nD: Play a game on the computer"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: plate\nB: spoon\nC: cup\nD: fork", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_45_31.png"], "question": "which object changed its status last in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: plate\nB: spoon\nC: cup\nD: fork"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Work on juicer-lid\nB: Read a book\nC: Cook dinner\nD: Go for a run", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_46_31.png"], "question": "what will the person do next after this video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Work on juicer-lid\nB: Read a book\nC: Cook dinner\nD: Go for a run"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: size\nB: openess\nC: brand\nD: color", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_47_31.png"], "question": "Which attribute does the person want to change with fridge for doing the last action in the video in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: size\nB: openess\nC: brand\nD: color"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: only if the person opens something else\nC: no\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_48_31.png"], "question": "If the person did not close something, will cereal1 change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: only if the person opens something else\nC: no\nD: yes"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: uncertain\nB: no\nC: yes\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_49_31.png"], "question": "Did the attribute of lettuce changed because of the first action did after the person get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: uncertain\nB: no\nC: yes\nD: maybe"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Talking on the phone\nB: Walking away\nC: Eating noodles\nD: Get noodles from table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_50_31.png"], "question": "what is the other person doing while the person do the first action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Talking on the phone\nB: Walking away\nC: Eating noodles\nD: Get noodles from table"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: wrapping\nB: lamp\nC: table\nD: chair", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_51_31.png"], "question": "which object changed its status first in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: wrapping\nB: lamp\nC: table\nD: chair"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: no\nC: maybe\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_52_31.png"], "question": "If the person did not get something from something, is the person able to put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: no\nC: maybe\nD: sometimes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Break the seal\nB: Flip the switch\nC: Open wrapping\nD: Cut the ribbon", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_53_31.png"], "question": "What action caused the first object that has status change in the video's status to change to opened?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Break the seal\nB: Flip the switch\nC: Open wrapping\nD: Cut the ribbon"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: sometimes\nC: no\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_54_31.png"], "question": "If the person did not open something, is the person able to put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: sometimes\nC: no\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Place fork on table\nB: Dry fork\nC: Pick up spoon\nD: Wash fork", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_55_31.png"], "question": "If the person did not do the first action did after he/she put something to something, what remaining actions in the video is executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Place fork on table\nB: Dry fork\nC: Pick up spoon\nD: Wash fork"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: under the table\nB: inside the cupboard\nC: in the sink\nD: on top of shelf", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_56_31.png"], "question": "What is the status of plate before the other person do the first action before he/she put something to something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: under the table\nB: inside the cupboard\nC: in the sink\nD: on top of shelf"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: attached to fork\nB: on the plate\nC: in the pan\nD: detached from fork", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_57_31.png"], "question": "What is the status of meat3 before the person do the first action did after he/she get something from something using fork to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: attached to fork\nB: on the plate\nC: in the pan\nD: detached from fork"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: chair\nB: lamp\nC: book\nD: knife", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_58_31.png"], "question": "If the actor do not get something from something, which object will he/she not be able to change in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: chair\nB: lamp\nC: book\nD: knife"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: I don’t know\nB: yes\nC: maybe\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_59_31.png"], "question": "Did the attribute of the last object that has status change in the video changed because of the action turning off something with something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: I don’t know\nB: yes\nC: maybe\nD: no"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes\nC: maybe\nD: uncertain", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_60_31.png"], "question": "Did the attribute of fridge changed because of the action closing something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes\nC: maybe\nD: uncertain"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: no\nC: yes\nD: I don’t know", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_61_31.png"], "question": "Did the attribute of fridge changed because of the action opening something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: I don’t know"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: no\nC: sometimes\nD: unknown", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_62_31.png"], "question": "Is the other person aware when the person stand-up?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: no\nC: sometimes\nD: unknown"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: no\nC: maybe\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_63_31.png"], "question": "Did the attribute of juicer changed because of the first action did before the person open something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: no\nC: maybe\nD: sometimes"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get lettuce from lettuce\nB: Go to sleep\nC: Write a report\nD: Eat a sandwich", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_64_31.png"], "question": "What is the person doing after he/she work on something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get lettuce from lettuce\nB: Go to sleep\nC: Write a report\nD: Eat a sandwich"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: partially wrapped\nB: wrapped\nC: double wrapped\nD: unwrapped", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_65_31.png"], "question": "what will the person want to have coffee's wrappedness be in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: partially wrapped\nB: wrapped\nC: double wrapped\nD: unwrapped"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: sometimes\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_66_31.png"], "question": "If the person did not do the first action did after he/she turn on something with something, is the person able to get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: sometimes\nC: yes\nD: no"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: no\nC: often\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_67_31.png"], "question": "Did the attribute of fishing-net changed because of the action filling something using something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: no\nC: often\nD: sometimes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in the car\nB: in closet\nC: on the table\nD: under the bed", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_68_31.png"], "question": "What does the person want cup to be for the action putting something to something in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in the car\nB: in closet\nC: on the table\nD: under the bed"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Reading a book\nB: Playing video games\nC: Cooking dinner\nD: Wash juicer and juicer-lid", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_69_31.png"], "question": "what is the other person doing while the person do the first action did before he/she get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Reading a book\nB: Playing video games\nC: Cooking dinner\nD: Wash juicer and juicer-lid"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: maybe\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_70_31.png"], "question": "If the person did not open something, will the first object that has status change in the video change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: separate from the other person\nB: attached to the other person\nC: above the other person\nD: beneath the other person", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_71_31.png"], "question": "what will the person want to have juice's spatial relationships be in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: separate from the other person\nB: attached to the other person\nC: above the other person\nD: beneath the other person"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Pour from bottle-water into juicer\nB: Running a Marathon\nC: Playing a Piano\nD: Sleeping", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_72_31.png"], "question": "What is the person doing after he/she open something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Pour from bottle-water into juicer\nB: Running a Marathon\nC: Playing a Piano\nD: Sleeping"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: not enough information\nB: no\nC: maybe\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_73_31.png"], "question": "If the person did not do the first action did before he/she pour from something into something, will spoon change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: not enough information\nB: no\nC: maybe\nD: yes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: sometimes\nC: maybe\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_74_31.png"], "question": "Did the attribute of closet changed because of the first action did after the person put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: sometimes\nC: maybe\nD: no"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: temporal relationships\nB: emotional status\nC: spatial relationships\nD: frequency", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_75_31.png"], "question": "what status of cup changed while the person get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: temporal relationships\nB: emotional status\nC: spatial relationships\nD: frequency"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: placed on the table\nB: attached to me\nC: inside the drawer\nD: on the kitchen counter", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_76_31.png"], "question": "What is the status of knife before the person put something to something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: placed on the table\nB: attached to me\nC: inside the drawer\nD: on the kitchen counter"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: irrelevant\nB: no\nC: yes\nD: partially", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_77_31.png"], "question": "Did the attribute of knife changed because of the first action did after the person wash something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: irrelevant\nB: no\nC: yes\nD: partially"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: yes\nC: no\nD: unsure", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_78_31.png"], "question": "If the person did not do the first action in the video, will tv change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: yes\nC: no\nD: unsure"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get juicer from juicer-base\nB: Turn on the blender\nC: Chop vegetables\nD: Check the timer", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_79_31.png"], "question": "What is the person doing after he/she put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get juicer from juicer-base\nB: Turn on the blender\nC: Chop vegetables\nD: Check the timer"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: broken\nB: opened\nC: painted\nD: removed", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_80_31.png"], "question": "what will the person want to have the last object that has status change in the video's openess be in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: broken\nB: opened\nC: painted\nD: removed"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put cup to shelf\nB: Cook meal\nC: Wash car\nD: Water plants", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_81_31.png"], "question": "If the person did not do the first action did after he/she close something, what remaining actions in the video is not executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put cup to shelf\nB: Cook meal\nC: Wash car\nD: Water plants"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: wet\nB: broken\nC: dirty\nD: clean", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_82_31.png"], "question": "What is the status of plate after the person wash something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: wet\nB: broken\nC: dirty\nD: clean"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get tomato from table\nB: Wash the knife\nC: Chop the tomato\nD: Slice the bread", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_83_31.png"], "question": "What is the person doing before he/she cut something using something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get tomato from table\nB: Wash the knife\nC: Chop the tomato\nD: Slice the bread"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Cook a meal\nB: Read a book\nC: Paint a picture\nD: Put fish to basin using tank", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_84_31.png"], "question": "If the person did not get something from something using fishing-net, what remaining actions in the video is not executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Cook a meal\nB: Read a book\nC: Paint a picture\nD: Put fish to basin using tank"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: only sometimes\nB: maybe\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_85_31.png"], "question": "If the person did not pour from something into something, will kettle-lid change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: only sometimes\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Read a book\nB: Cook a meal\nC: Get coffee from shelf\nD: Wash the dishes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_86_31.png"], "question": "what is the other person doing while the person do the first action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Read a book\nB: Cook a meal\nC: Get coffee from shelf\nD: Wash the dishes"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put tank to table\nB: Take tank from table\nC: Move tank to floor\nD: Put table to tank", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_87_31.png"], "question": "What is the person doing before he/she get something from something and fishing-net?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put tank to table\nB: Take tank from table\nC: Move tank to floor\nD: Put table to tank"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sofa\nB: lamp\nC: table\nD: vacuum", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_88_31.png"], "question": "which object changed its status first in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sofa\nB: lamp\nC: table\nD: vacuum"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Reading a book\nB: Eating lunch\nC: Taking a nap\nD: Put wrapping to table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_89_31.png"], "question": "what is the other person doing while the person do the first action did before he/she close something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Reading a book\nB: Eating lunch\nC: Taking a nap\nD: Put wrapping to table"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: half-squeezed\nB: in market\nC: in juicer\nD: unpeeled", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_90_31.png"], "question": "what will the status of orange2 change to if the actor do the first action did before he/she get something from something in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: half-squeezed\nB: in market\nC: in juicer\nD: unpeeled"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Slice the watermelon\nB: Peel the watermelon\nC: Throw away the watermelon\nD: Put watermelon to juicer", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_91_31.png"], "question": "what will the other person do next?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Slice the watermelon\nB: Peel the watermelon\nC: Throw away the watermelon\nD: Put watermelon to juicer"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: possibly\nC: no\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_92_31.png"], "question": "Did the attribute of kettle-base changed because of the first action did after the person close something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: possibly\nC: no\nD: yes"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Reading a book\nB: Boiling water\nC: Put kettle to table\nD: Sitting on a chair", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_93_31.png"], "question": "what is the other person doing while the person do the first action did before he/she point to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Reading a book\nB: Boiling water\nC: Put kettle to table\nD: Sitting on a chair"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: it depends\nB: yes\nC: sometimes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_94_31.png"], "question": "Does the first action did before the person turn on something with something fulfills the preconditions of the action getting something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: it depends\nB: yes\nC: sometimes\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Place kettle on stovetop\nB: Fill kettle using sink\nC: Turn on the kettle\nD: Add tea leaves to kettle", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_95_31.png"], "question": "What action caused kettle's status to change to nonempty?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Place kettle on stovetop\nB: Fill kettle using sink\nC: Turn on the kettle\nD: Add tea leaves to kettle"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: no\nC: maybe\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_96_31.png"], "question": "Did the attribute of tomato1 changed because of the first action did before the person wash something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: no\nC: maybe\nD: sometimes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Stand up and walk away\nB: Sit down on sofa\nC: Open the window\nD: Start cooking dinner", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_97_31.png"], "question": "what will the other person do next?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Stand up and walk away\nB: Sit down on sofa\nC: Open the window\nD: Start cooking dinner"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Watch TV\nB: Read a book\nC: Go for a run\nD: Work on noodles", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_98_31.png"], "question": "what will the person do next after this video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Watch TV\nB: Read a book\nC: Go for a run\nD: Work on noodles"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: knife\nB: cup\nC: phone\nD: pen", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_99_31.png"], "question": "which object changed its status when the other person get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: knife\nB: cup\nC: phone\nD: pen"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Close the tank lid\nB: Put tank on table\nC: Put tank to sink\nD: Pour tank contents into a glass", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_100_31.png"], "question": "What is the person doing after he/she pour from something into something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Close the tank lid\nB: Put tank on table\nC: Put tank to sink\nD: Pour tank contents into a glass"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: maybe\nC: no\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_101_31.png"], "question": "If the person did not do the first action did before he/she open something, is the person able to get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: maybe\nC: no\nD: sometimes"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in room temperature\nB: in boiling water\nC: in the freezer\nD: in the microwave", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_102_31.png"], "question": "what will the person want to have the last object that has status change in the video's temperature be in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in room temperature\nB: in boiling water\nC: in the freezer\nD: in the microwave"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Tie shoes\nB: Read a book\nC: Drink water from cup\nD: Get meat from floor", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_103_31.png"], "question": "What is the person doing before he/she throw something into something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Tie shoes\nB: Read a book\nC: Drink water from cup\nD: Get meat from floor"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: can not be opened\nB: left part removed\nC: completely sealed\nD: right part added", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_104_31.png"], "question": "What is the precondition of changing the openability of meat2?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: can not be opened\nB: left part removed\nC: completely sealed\nD: right part added"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: undecided\nB: no\nC: yes\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_105_31.png"], "question": "Did the attribute of cutting-board changed because of the first action did after the person point to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: undecided\nB: no\nC: yes\nD: maybe"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: cleanliness\nB: weight\nC: sharpness\nD: color", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_106_31.png"], "question": "Which attribute does the person want to change with knife for doing the last action in the video in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: cleanliness\nB: weight\nC: sharpness\nD: color"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: lettuce\nB: carrot\nC: pepper\nD: tomato", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_107_31.png"], "question": "which object changed its status when the other person do the last action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: lettuce\nB: carrot\nC: pepper\nD: tomato"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Leave the watermelon unpeeled\nB: Wash the cutting-board\nC: Cut the watermelon on the floor\nD: Get watermelon from cutting-board", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_108_31.png"], "question": "If the other person did not wash something, what actions of this person in the video is executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Leave the watermelon unpeeled\nB: Wash the cutting-board\nC: Cut the watermelon on the floor\nD: Get watermelon from cutting-board"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: always on\nB: could be turned off\nC: always off\nD: could be turned on", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_109_31.png"], "question": "What is the precondition of changing the switchability of the last object that has status change in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: always on\nB: could be turned off\nC: always off\nD: could be turned on"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: not sure\nB: maybe\nC: no\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_110_31.png"], "question": "Did the attribute of fridge changed because of the action closing something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: not sure\nB: maybe\nC: no\nD: yes"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes\nC: not sure\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_111_31.png"], "question": "If the person did not do the last action in the video, is the person able to put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes\nC: not sure\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: max temperature\nB: on\nC: off\nD: half full", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_112_31.png"], "question": "What is the precondition of changing the poweredness of kettle?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: max temperature\nB: on\nC: off\nD: half full"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: no\nC: yes\nD: not sure", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_113_31.png"], "question": "Did the attribute of vacuum changed because of the last action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: not sure"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: sometimes\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_114_31.png"], "question": "Is milk visible to the other person after the person do the first action did after he/she open something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: sometimes\nC: yes\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: maybe\nC: not sure\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_115_31.png"], "question": "Does the first action in the video fulfills the preconditions of the action opening something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: maybe\nC: not sure\nD: no"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: only if it is a chair\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_116_31.png"], "question": "Does the action sitting down on something fulfills the preconditions of the action putting something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: only if it is a chair\nC: yes\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: on the side of shelf\nB: next to the door\nC: on the edge of table\nD: under the chair", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_117_31.png"], "question": "What is the status of vacuum before the person get something from something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: on the side of shelf\nB: next to the door\nC: on the edge of table\nD: under the chair"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes\nC: maybe\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_118_31.png"], "question": "Does the action sitting down on something fulfills the preconditions of the action switching with something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes\nC: maybe\nD: sometimes"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: fishing-rod\nB: boat\nC: fishing-net\nD: life-jacket", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_119_31.png"], "question": "which object changed its status when the person do the first action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: fishing-rod\nB: boat\nC: fishing-net\nD: life-jacket"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Moved the milk to the fridge\nB: Placed the milk on the floor\nC: Put milk to table\nD: Took the milk off the table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_120_31.png"], "question": "How did the person changed the spatial relationships of the first object that has status change in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Moved the milk to the fridge\nB: Placed the milk on the floor\nC: Put milk to table\nD: Took the milk off the table"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: spoon\nB: knife\nC: plate\nD: fork", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_121_31.png"], "question": "which object changed its status when the other person do the first action before he/she eat something with something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: spoon\nB: knife\nC: plate\nD: fork"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: maybe\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_122_31.png"], "question": "If the person did not do the first action did before he/she wash something, will sink change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: maybe\nC: yes\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_123_31.png"], "question": "If the person did not do the first action did before he/she get something from something, is the person able to put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: maybe\nC: yes\nD: sometimes"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes\nC: maybe\nD: sometimes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_124_31.png"], "question": "Did the attribute of meat changed because of the action putting something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes\nC: maybe\nD: sometimes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get meat from meat using spoon\nB: Use a knife to cut the meat\nC: Boil the meat to change its shape\nD: Squash the meat with a fork", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_125_31.png"], "question": "How did the person changed the shape of meat2?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get meat from meat using spoon\nB: Use a knife to cut the meat\nC: Boil the meat to change its shape\nD: Squash the meat with a fork"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: only partially\nB: maybe\nC: no\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_126_31.png"], "question": "Did the attribute of spoon changed because of the first action did after the person put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: only partially\nB: maybe\nC: no\nD: yes"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: uncertain\nB: no\nC: maybe\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_127_31.png"], "question": "Does the first action did before the person put something to something fulfills the preconditions of the action opening something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: uncertain\nB: no\nC: maybe\nD: yes"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes\nC: only if watermelon1 changes its status first\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_128_31.png"], "question": "If the person did not do the first action did before he/she get something from something, will watermelon2 change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes\nC: only if watermelon1 changes its status first\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in the fridge\nB: on the table\nC: in cup1\nD: in cup2", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_129_31.png"], "question": "What is the status of juice before the person do the first action did after he/she get something from something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in the fridge\nB: on the table\nC: in cup1\nD: in cup2"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in the garden\nB: on the table\nC: under the bed\nD: in sink", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_130_31.png"], "question": "What does the person want tank to be for the first action did before the person pour from something into something in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in the garden\nB: on the table\nC: under the bed\nD: in sink"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in the refrigerator\nB: under the sink\nC: on top of stove\nD: outside in the garden", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_131_31.png"], "question": "What is the status of water-pot after the other person put something to something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in the refrigerator\nB: under the sink\nC: on top of stove\nD: outside in the garden"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get watermelon from cutting-board\nB: Wash hands\nC: Put apple on the counter\nD: Chop vegetables", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_132_31.png"], "question": "What is the last action the person did in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get watermelon from cutting-board\nB: Wash hands\nC: Put apple on the counter\nD: Chop vegetables"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: possibly\nB: yes\nC: no\nD: unknown", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_133_31.png"], "question": "Does the first action in the video fulfills the preconditions of the action pouring from something into something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: possibly\nB: yes\nC: no\nD: unknown"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: no\nC: I am not sure\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_134_31.png"], "question": "Did the attribute of controller1 changed because of the first action did before the person stand-up?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: no\nC: I am not sure\nD: yes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: full\nB: half-full\nC: boiling\nD: empty", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_135_31.png"], "question": "What is the status of kettle after the person do the last action in the video to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: full\nB: half-full\nC: boiling\nD: empty"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Turned on the lights\nB: Plugged in the TV\nC: Turned off the remote\nD: Turn on TV with remote", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_136_31.png"], "question": "How did the person changed the poweredness of the first object that has status change in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Turned on the lights\nB: Plugged in the TV\nC: Turned off the remote\nD: Turn on TV with remote"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: maybe\nC: sometimes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_137_31.png"], "question": "Does the action putting something to something fulfills the preconditions of the action watching something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: maybe\nC: sometimes\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: no\nC: yes\nD: only if performed sequentially", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_138_31.png"], "question": "Does the action getting something from something fulfills the preconditions of the action putting something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: no\nC: yes\nD: only if performed sequentially"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Cooking food on stove\nB: Fill water-pot using water-dispenser\nC: Reading a book\nD: Talking on the phone", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_139_31.png"], "question": "what is the other person doing while the person stand-up?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Cooking food on stove\nB: Fill water-pot using water-dispenser\nC: Reading a book\nD: Talking on the phone"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: only if they open the fridge again\nB: yes\nC: maybe\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_140_31.png"], "question": "Is fridge visible to the other person after the person do the first action did after he/she put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: only if they open the fridge again\nB: yes\nC: maybe\nD: no"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: no\nC: not sure\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_141_31.png"], "question": "Did the attribute of fridge changed because of the action opening something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: no\nC: not sure\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get cereal from table\nB: Turn off the lights\nC: Wash dishes\nD: Open fridge", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_142_31.png"], "question": "What is the last action the person did in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get cereal from table\nB: Turn off the lights\nC: Wash dishes\nD: Open fridge"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put orange in the fridge\nB: Put watermelon to fridge\nC: Take watermelon out of the fridge\nD: Take apples from the table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_143_31.png"], "question": "What is the person doing before he/she get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put orange in the fridge\nB: Put watermelon to fridge\nC: Take watermelon out of the fridge\nD: Take apples from the table"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: probably\nC: maybe\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_144_31.png"], "question": "Did the attribute of the first object that has status change in the video changed because of the action getting something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: probably\nC: maybe\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: television\nB: remote\nC: phone\nD: computer", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_145_31.png"], "question": "If the actor do not put something to something, which object will he/she not be able to change in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: television\nB: remote\nC: phone\nD: computer"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Throw the cup away\nB: Break the cup\nC: Put cup to the other person\nD: Keep the cup for themselves", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_146_31.png"], "question": "If the person did not do the first action did after he/she get something from something, what remaining actions in the video is executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Throw the cup away\nB: Break the cup\nC: Put cup to the other person\nD: Keep the cup for themselves"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: mixing\nB: harvesting\nC: watering\nD: pruning", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_147_31.png"], "question": "What does the person want plant to be for the first action did before the person fill something using something in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: mixing\nB: harvesting\nC: watering\nD: pruning"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: yes\nC: no\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_148_31.png"], "question": "If the person did not do the first action did after he/she fill something using something, is the person able to work on something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: yes\nC: no\nD: maybe"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: emptiness\nB: happiness\nC: fullness\nD: sadness", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_149_31.png"], "question": "what status will the person change on juicer-base?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: emptiness\nB: happiness\nC: fullness\nD: sadness"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes\nC: uncertain\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_150_31.png"], "question": "Did the attribute of water-pot changed because of the first action did after the person sit down on something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes\nC: uncertain\nD: maybe"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: kettle\nB: towel\nC: window\nD: chair", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_151_31.png"], "question": "which object changed its status when the other person do the last action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: kettle\nB: towel\nC: window\nD: chair"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put cup to cutting-board\nB: Playing a game\nC: Reading a book\nD: Watching TV", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_152_31.png"], "question": "what is the other person doing while the person open something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put cup to cutting-board\nB: Playing a game\nC: Reading a book\nD: Watching TV"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: state of separation\nB: state of mixture\nC: state of disintegration\nD: state of dissolution", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_153_31.png"], "question": "what status of noodles changed while the person do the last action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: state of separation\nB: state of mixture\nC: state of disintegration\nD: state of dissolution"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in bottle\nB: in cup2\nC: in cup1\nD: on table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_154_31.png"], "question": "How would the first action did after the person close something change the state of milk1?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in bottle\nB: in cup2\nC: in cup1\nD: on table"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: off\nB: ignored\nC: on\nD: broken", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_155_31.png"], "question": "What does the person want kettle to be for the first action did after the person work on something in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: off\nB: ignored\nC: on\nD: broken"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes\nC: sometimes\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_156_31.png"], "question": "Does the action sitting down on something fulfills the preconditions of the action drinking something with something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes\nC: sometimes\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: box1\nB: box2\nC: wrapping1\nD: wrapping2", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_157_31.png"], "question": "which object changed its status when the person get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: box1\nB: box2\nC: wrapping1\nD: wrapping2"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: unknown\nC: no\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_158_31.png"], "question": "Did the attribute of lettuce changed because of the first action did after the person put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: unknown\nC: no\nD: maybe"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Cook meat using microwave\nB: Cook meat using pan and stove\nC: Cook meat using oven\nD: Cook meat using grill", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_159_31.png"], "question": "How did the person changed the cookedness of meat12?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Cook meat using microwave\nB: Cook meat using pan and stove\nC: Cook meat using oven\nD: Cook meat using grill"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: partially\nC: yes\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_160_31.png"], "question": "Did the attribute of the first object that has status change in the video changed because of the action filling something using something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: partially\nC: yes\nD: maybe"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: underneath watermelon\nB: on top of watermelon\nC: next to watermelon\nD: inside watermelon", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_161_31.png"], "question": "What is the precondition of changing the spatial relationships of watermelon1?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: underneath watermelon\nB: on top of watermelon\nC: next to watermelon\nD: inside watermelon"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: sometimes\nC: maybe\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_162_31.png"], "question": "Did the attribute of the first object that has status change in the video changed because of the action putting something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: sometimes\nC: maybe\nD: no"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: vacuum cleaner\nB: refrigerator\nC: microwave\nD: television", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_163_31.png"], "question": "which object changed its status when the other person do the first action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: vacuum cleaner\nB: refrigerator\nC: microwave\nD: television"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Sit on the couch\nB: Check their phone\nC: Go outside\nD: Get bowl and spoon from table", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_164_31.png"], "question": "What is the person doing after he/she point to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Sit on the couch\nB: Check their phone\nC: Go outside\nD: Get bowl and spoon from table"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in basin\nB: on ground\nC: in tree\nD: in sky", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_165_31.png"], "question": "How would the action putting something to something using fishing-net change the state of fish?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in basin\nB: on ground\nC: in tree\nD: in sky"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Read a book\nB: Get remote from table\nC: Go for a walk\nD: Start cooking dinner", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_166_31.png"], "question": "what will the other person do next?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Read a book\nB: Get remote from table\nC: Go for a walk\nD: Start cooking dinner"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: probably not\nB: maybe\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_167_31.png"], "question": "Did the attribute of juicer changed because of the last action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: probably not\nB: maybe\nC: yes\nD: no"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Watch TV\nB: Go for a run\nC: Read a book\nD: Wash bowl", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_168_31.png"], "question": "If the person did not eat something with something, what remaining actions in the video is executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Watch TV\nB: Go for a run\nC: Read a book\nD: Wash bowl"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Painting a portrait\nB: Playing a musical instrument\nC: Cook meat using fork and pan and stove\nD: Reading a book by the fireplace", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_169_31.png"], "question": "what is the other person doing while the person do the last action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Painting a portrait\nB: Playing a musical instrument\nC: Cook meat using fork and pan and stove\nD: Reading a book by the fireplace"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put meat to pan using fork\nB: Put meat to pan using knife\nC: Put meat to plate using fork\nD: Put meat to pan using spatula", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_170_31.png"], "question": "How did the person changed the spatial relationships of meat1?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put meat to pan using fork\nB: Put meat to pan using knife\nC: Put meat to plate using fork\nD: Put meat to pan using spatula"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: maybe\nC: uncertain\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_171_31.png"], "question": "Did the attribute of meat1 changed because of the action getting something from something using fork?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: maybe\nC: uncertain\nD: yes"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: coffee2\nB: coffee1\nC: bottle\nD: tea", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_172_31.png"], "question": "which object changed its status last in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: coffee2\nB: coffee1\nC: bottle\nD: tea"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: maybe\nC: uncertain\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_173_31.png"], "question": "Does the first action in the video fulfills the preconditions of the action opening something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: maybe\nC: uncertain\nD: no"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: no\nC: maybe\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_174_31.png"], "question": "If the person did not do the first action in the video, will drawer change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: no\nC: maybe\nD: yes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes, the status changed due to pouring\nC: the action completed without any status change\nD: the attribute has been initialized", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_175_31.png"], "question": "Did the attribute of the object has status change changed because of the action pouring from something into something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes, the status changed due to pouring\nC: the action completed without any status change\nD: the attribute has been initialized"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: no\nC: yes\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_176_31.png"], "question": "If the person did not do the first action did before he/she wash something, is the person able to get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: no\nC: yes\nD: maybe"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: transparent\nB: blue\nC: empty\nD: nonempty", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_177_31.png"], "question": "What is the status of trash-can after the other person throw something into something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: transparent\nB: blue\nC: empty\nD: nonempty"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Put lettuce to trash-can\nB: Moved the trash-can\nC: Cleaned the trash-can\nD: Removed the lettuce", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_178_31.png"], "question": "What action caused trash-can's status to change to nonempty?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Put lettuce to trash-can\nB: Moved the trash-can\nC: Cleaned the trash-can\nD: Removed the lettuce"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Turn on the light\nB: Open the refrigerator\nC: Get knife from knife-base\nD: Sit on the couch", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_179_31.png"], "question": "During which action does the person knows about the other person's action?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Turn on the light\nB: Open the refrigerator\nC: Get knife from knife-base\nD: Sit on the couch"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: no\nC: sometimes\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_180_31.png"], "question": "Did the attribute of vacuum changed because of the action putting something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: no\nC: sometimes\nD: yes"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: inside the refrigerator\nB: next to the coffee machine\nC: on top of juicer base\nD: under the microwave", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_181_31.png"], "question": "what will the person want to have the first object that has status change in the video's spatial relationships be in the future?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: inside the refrigerator\nB: next to the coffee machine\nC: on top of juicer base\nD: under the microwave"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Washed the car\nB: Sweep floor using vacuum\nC: Read a book\nD: Cooked dinner", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_182_31.png"], "question": "How did the person changed the cleanliness of vacuum?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Washed the car\nB: Sweep floor using vacuum\nC: Read a book\nD: Cooked dinner"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: bowl\nB: book\nC: door\nD: lamp", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_183_31.png"], "question": "which object changed its status when the person get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: bowl\nB: book\nC: door\nD: lamp"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: no\nC: sometimes\nD: always", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_184_31.png"], "question": "Is the other person aware when the person get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: no\nC: sometimes\nD: always"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: yes\nC: I don’t know\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_185_31.png"], "question": "If the person did not fill something using something, is the person able to do the first action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: yes\nC: I don’t know\nD: maybe"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Close the tank-lid\nB: Get tank-lid from table\nC: Take a seat\nD: Pour water into the tank", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_186_31.png"], "question": "what is the other person doing while the person do the first action did after he/she put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Close the tank-lid\nB: Get tank-lid from table\nC: Take a seat\nD: Pour water into the tank"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: only if the object is transparent\nC: sometimes\nD: yes", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_187_31.png"], "question": "Is cutting-board visible to the other person after the person put something to something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: only if the object is transparent\nC: sometimes\nD: yes"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Cut another vegetable\nB: Put knife to knife-base\nC: Throw the knife away\nD: Wash the knife", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_188_31.png"], "question": "what will the person do next after this video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Cut another vegetable\nB: Put knife to knife-base\nC: Throw the knife away\nD: Wash the knife"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in a drawer\nB: on a shelf\nC: under the bed\nD: in trash can", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_189_31.png"], "question": "How would the action throwing something into something change the state of wrapping?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in a drawer\nB: on a shelf\nC: under the bed\nD: in trash can"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: in the dishwasher\nB: in sink\nC: on the table\nD: in the fridge", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_190_31.png"], "question": "What is the status of cup before the other person do the first action after he/she put something to something to change it?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: in the dishwasher\nB: in sink\nC: on the table\nD: in the fridge"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: table\nB: tv\nC: window\nD: phone", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_191_31.png"], "question": "which object changed its status when the person do the first action did after he/she stand-up?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: table\nB: tv\nC: window\nD: phone"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: no\nB: maybe\nC: yes\nD: I don't know", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_192_31.png"], "question": "If the person did not throw something into something, is the person able to get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: no\nB: maybe\nC: yes\nD: I don't know"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Get fishing-net from basin\nB: Throw the net into the water\nC: Cover the basin with a lid\nD: Pour water from the basin", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_193_31.png"], "question": "If the person did not pour from something into something, what remaining actions in the video is executable?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Get fishing-net from basin\nB: Throw the net into the water\nC: Cover the basin with a lid\nD: Pour water from the basin"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Cut lettuce using knife\nB: Boiling water\nC: Stirring a pot\nD: Peeling an orange", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_194_31.png"], "question": "What is the person doing after he/she throw something into something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Cut lettuce using knife\nB: Boiling water\nC: Stirring a pot\nD: Peeling an orange"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: yes\nC: no\nD: maybe", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_195_31.png"], "question": "If the person did not do the first action did after he/she put something to something, is the person able to get something from something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: yes\nC: no\nD: maybe"}, "output": {"output_text": "B"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: yes\nB: maybe\nC: no\nD: I don’t know", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_196_31.png"], "question": "If the person did not do the first action in the video, will cereal change its status?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: yes\nB: maybe\nC: no\nD: I don’t know"}, "output": {"output_text": "A"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: maybe\nB: uncertain\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_197_31.png"], "question": "Does the action getting something from something fulfills the preconditions of the last action in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: maybe\nB: uncertain\nC: yes\nD: no"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: Used a remote\nB: Pressed a button\nC: Get controller from table\nD: Turned on the switch", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_198_31.png"], "question": "How did the person changed the poweredness of the first object that has status change in the video?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: Used a remote\nB: Pressed a button\nC: Get controller from table\nD: Turned on the switch"}, "output": {"output_text": "C"}, "task": "Egocentric_Video_QuestionAnswering"}
{"source": "EgoTaskQA", "options": "A: sometimes\nB: only if the action is prolonged\nC: yes\nD: no", "visual_input_component": "egocentric image", "input": {"input_image_path": ["3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_0.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_1.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_2.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_3.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_4.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_5.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_6.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_7.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_8.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_9.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_10.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_11.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_12.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_13.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_14.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_15.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_16.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_17.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_18.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_19.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_20.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_21.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_22.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_23.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_24.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_25.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_26.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_27.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_28.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_29.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_30.png", "3D-spatial/Egocentric_Video_QuestionAnswering/Egocentric_Video_QuestionAnswering_199_31.png"], "question": "Did the attribute of meat changed because of the action closing something?", "context": "Your task is to understand and reasoning about activities and events from the first-person perspective. \nSelect from the following choices.\nA: sometimes\nB: only if the action is prolonged\nC: yes\nD: no"}, "output": {"output_text": "D"}, "task": "Egocentric_Video_QuestionAnswering"}