# VTimeLLM \[[Paper](https://arxiv.org/pdf/2311.18445.pdf)\]
Official PyTorch implementation of the paper "VTimeLLM: Empower LLM to Grasp Video Moments".
[](https://paperswithcode.com/sota/video-based-generative-performance-4?p=vtimellm-empower-llm-to-grasp-video-moments)
[](https://paperswithcode.com/sota/dense-video-captioning-on-activitynet?p=vtimellm-empower-llm-to-grasp-video-moments)
[](https://paperswithcode.com/sota/video-based-generative-performance-3?p=vtimellm-empower-llm-to-grasp-video-moments)
[](https://paperswithcode.com/sota/video-based-generative-performance-1?p=vtimellm-empower-llm-to-grasp-video-moments)
[](https://paperswithcode.com/sota/video-based-generative-performance-5?p=vtimellm-empower-llm-to-grasp-video-moments)
[](https://paperswithcode.com/sota/video-based-generative-performance-2?p=vtimellm-empower-llm-to-grasp-video-moments)
[](https://paperswithcode.com/sota/video-based-generative-performance?p=vtimellm-empower-llm-to-grasp-video-moments)
---
## :loudspeaker: Latest Updates
- **Jan-2**: Thanks to [Xiao Xia](https://github.com/Rishubi) , [Shengbo Tong](https://github.com/tsb-19) and [Beining Wang](https://github.com/Benson0704), we have refactored the code to now support both the LLAMA and ChatGLM3 architectures. We translated the training data into Chinese and fine-tuned a Chinese version based on the ChatGLM3-6b.
- **Dec-14**: Released the training code and data. All the resources including models, datasets and extracted features are available
[here](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2F&mode=list). :fire::fire:
- **Dec-4**: VTimeLLM: demo released.
---
## VTimeLLM Overview :bulb:
VTimeLLM is a novel Video LLM designed for fine-grained video moment understanding and reasoning with respect to time boundary.
VTimeLLM adopts a boundary-aware three-stage training strategy, which respectively utilizes image-text pairs for feature alignment, multiple-event videos to increase temporal-boundary awareness, and high-quality video-instruction tuning to further improve temporal understanding ability as well as align with human intents.
---
## Contributions :trophy:
- We propose VTimeLLM, the first boundary-aware Video LLM, to the best of our knowledge.
- We propose the boundary-aware three-stage training strategy, which consecutively leverages i) large-scale image-text data for feature alignment, ii) large-scale multi-event video-text data together with the temporal-related single-turn and multi-turn QA to enhance the awareness of time boundary, and iii) instruction tuning on the high-quality dialog dataset for better temporal reasoning ability.
- We conduct extensive experiments to demonstrate that the proposed VTimeLLM significantly outperforms existing Video LLMs in various fine-grained temporal-related video tasks, showing its superior ability for video understanding and reasoning.
---
## Installation :wrench:
We recommend setting up a conda environment for the project:
```shell
conda create --name=vtimellm python=3.10
conda activate vtimellm
git clone https://github.com/huangb23/VTimeLLM.git
cd VTimeLLM
pip install -r requirements.txt
```
Additionally, install additional packages for training cases.
```shell
pip install ninja
pip install flash-attn --no-build-isolation
```
## Running Demo Offline :cd:
To run the demo offline, please refer to the instructions in [offline_demo.md](docs/offline_demo.md).
## Training :train:
For training instructions, check out [train.md](docs/train.md).
## Qualitative Analysis :mag:
A Comprehensive Evaluation of VTimeLLM's Performance across Multiple Tasks.
### Video Understanding and Conversational Tasks :speech_balloon:
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### Creative Tasks :paintbrush:
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### Fine-grained Understanding Tasks :globe_with_meridians:
---
### Video Reasoning Tasks :question:
---
## Acknowledgements :pray:
We are grateful for the following awesome projects our VTimeLLM arising from:
* [LLaVA](https://github.com/haotian-liu/LLaVA): Large Language and Vision Assistant
* [FastChat](https://github.com/lm-sys/FastChat): An Open Platform for Training, Serving, and Evaluating Large Language Model based Chatbots
* [Video-ChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT): Towards Detailed Video Understanding via Large Vision and Language Models
* [LLaMA](https://github.com/facebookresearch/llama): Open and Efficient Foundation Language Models
* [Vid2seq](https://github.com/google-research/scenic/tree/main/scenic/projects/vid2seq): Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
* [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid): A Large-scale Video-Text dataset
If you're using VTimeLLM in your research or applications, please cite using this BibTeX:
```bibtex
@inproceedings{huang2024vtimellm,
title={Vtimellm: Empower llm to grasp video moments},
author={Huang, Bin and Wang, Xin and Chen, Hong and Song, Zihan and Zhu, Wenwu},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={14271--14280},
year={2024}
}
```
## License :scroll:
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License.
Looking forward to your feedback, contributions, and stars! :star2: