video_chat

🦜 VideoChat [paper/demo]

In this study, we initiate an exploration into video understanding by introducing VideoChat, an end-to-end chat-centric video understanding system. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference. To instructively tune this system, we propose a video-centric instruction dataset, composed of thousands of videos matched with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and causal relationships, providing a valuable asset for training chat-centric video understanding systems. Preliminary qualitative experiments reveal our system’s potential across a broad spectrum of video applications and set the standard for future research.

🔥 Updates

• 2023/05/12: Release the 7B version:
  • 🎊 Model-7B: 7B requires ~20GB GPU memory, while 13B requires ~32GB GPU memory.
• 2023/05/11: Release the 🦜VideoChat V1, which can handle both image and video understanding!
  • 🎊 Model-13B and Data.
  • 🤗 Online Demo
  • 🧑‍🔧 Tuning scripts are cleaning.

⏳ Schedule

• Small-scale video instuction data and tuning
• Instruction tuning on BLIP+UniFormerV2+Vicuna
• Large-scale and complex video instuction data
• Instruction tuning on strong video foundation model
• User-friendly interactions with longer videos
• ...

💬 Example Online🦜

Comparison with ChatGPT, MiniGPT-4, LLaVA and mPLUG-Owl.
Our VideoChat can handle both image and video understanding well!

[Video] Why the video is funny?

[Video] Spatial perception

[Video] Temporal perception

[Video] Multi-turn conversation

Image understanding

🏃 Usage

• Prepare the envirment.

  pip install -r requirements.txt

• Download BLIP2 model:

  • ViT: wget https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth
  • QFormer: wget https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth
  • Change the vit_model_path and q_former_model_path in config.json or config_7b.json.

• Download StabelVicuna model:

  • LLAMA: Download it from the original repo or hugging face.
  • If you download LLAMA from the original repo, please process it via the following command:

  # convert_llama_weights_to_hf is copied from transformers
  python src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir /path/to/downloaded/llama/weights \
    --model_size 13B --output_dir /output/path

  • For 13B: Download stable-vicuna-13b-delta and process it:

  # fastchat v0.1.10
  python3 apply_delta.py \
    --base /path/to/model_weights/llama-13b \
    --target stable-vicuna-13b \
    --delta CarperAI/stable-vicuna-13b-delta

  • For 7B: Download vicuna-7b-delta-v0 and process it:

  # fastchat v0.1.10
  python3 apply_delta.py \
    --base /path/to/model_weights/llama-7b \
    --target vicuna-7b-v0 \
    --delta CarperAI/vicuna-7b-delta-v0

  • Change the llama_model_path in config.json or config_7b.json.

• Download VideoChat-13B or VideoChat-7B:

  • Change the videochat_model_path in config.jsonor config_7b.json.

• Running demo with Gradio:

  python demo.py

• Another demo on Jupyter Notebook can found in demo.ipynb

📄 Citation

If you find this project useful in your research, please consider cite:

@article{2023videochat,
  title={VideoChat: Chat-Centric Video Understanding},
  author={KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao},
  journal={arXiv preprint arXiv:2305.06355},
  year={2023}
}

👍 Acknowledgement

Thanks to the open source of the following projects:

InternVideo, UniFormerV2, MiniGPT-4, LLaVA, BLIP2, StableLM.