-**Contextual associations.**. Our method demonstrates a superior ability to infer the relationships between targets more effectively when answering questions, which results in delivering more comprehensive and insightful results.
-**Support resolution up to 1344 x 896.** Surpassing the standard 448 x 448 resolution typically employed for LMMs, this significant increase in resolution augments the ability to discern and understand unnoticeable or tightly clustered objects and dense text.
-**Enhanced general performance.** We carried out testing across 16 diverse datasets, leading to impressive performance by our Monkey model in tasks such as Image Captioning, General Visual Question Answering, Text-centric Visual Question Answering, and Document-oriented Visual Question Answering.
-**Contextual associations.**. Our method demonstrates a superior ability to infer the relationships between targets more effectively when answering questions, which results in delivering more comprehensive and insightful results.
-**Support resolution up to 1344 x 896.** Surpassing the standard 448 x 448 resolution typically employed for LMMs, this significant increase in resolution augments the ability to discern and understand unnoticeable or tightly clustered objects and dense text.
-**Enhanced general performance.** We carried out testing across 16 diverse datasets, leading to impressive performance by our Monkey model in tasks such as Image Captioning, General Visual Question Answering, Text-centric Visual Question Answering, and Document-oriented Visual Question Answering.
## performance
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@@ -47,5 +47,5 @@ With the power of large-scale architecture, we have also achieved impressive per
## Acknowledgement
[Qwen-VL]https://github.com/QwenLM/Qwen-VL.git): the codebase we built upon. Thanks for the authors of Qwen for providing the framework.
[Qwen-VL](https://github.com/QwenLM/Qwen-VL.git): the codebase we built upon. Thanks for the authors of Qwen for providing the framework.
