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TimeChat Model Card

Model details

Model type: TimeChat is an open-source chatbot trained by fine-tuning LLaMA-2 on time-sensitive video-centric instruction-following data (See TimeIT-Instruct-104k). It is an auto-regressive language model, based on the transformer architecture.

Model date: TimeChat-7B was trained in November 2023.

Paper or resources for more information: Paper, Code

License

Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.

Where to send questions or comments about the model: https://github.com/RenShuhuai-Andy/TimeChat/issues

Intended use

Primary intended uses: The primary use of TimeChat is research on large multimodal models and chatbots.

Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Training dataset

Evaluation dataset

Three tasks of long video understanding, i.e., dense video captioning (YouCook2), temporal grounding (Charades-STA), and highlight detection (QVHighlights).

Citation

If you find our project useful, hope you can star our repo and cite our paper as follows:

@article{Ren2023TimeChat,
  title={TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding},
  author={Shuhuai Ren and Linli Yao and Shicheng Li and Xu Sun and Lu Hou},
  journal={ArXiv},
  year={2023},
  volume={abs/2312.02051},
}
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Dataset used to train ShuhuaiRen/TimeChat-7b