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home / papers / 2023 / VideoChat: Chat-Centric Video Understanding

VideoChat: Chat-Centric Video Understanding

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 4.0 (arXiv)" does not allow redistributing the text itself. Original — arxiv.org/pdf/2305.06355

Summary

Video understanding systems are usually fine-tuned separately for each task, so they lack general spatiotemporal comprehension. Image-centric multimodal chatbots also could not yet handle video properly. The authors propose VideoChat, which treats every video task as multi-round question answering, with a large language model acting as a universal decoder. They build it in two variants. VideoChat-Text runs off-the-shelf perception models and turns the video into a time-stamped text stream that the LLM reasons over. This text conversion loses visual detail and handles causal reasoning poorly. VideoChat-Embed instead links a video foundation model to the LLM through a trainable token interface. It is trained in two lightweight stages: first alignment on large video-text corpora, then tuning on a new video instruction dataset. That dataset has thousands of videos with detailed descriptions and conversations, which ChatGPT generated from dense captions given in temporal order. The evaluation is only qualitative: dialogue examples suggest better temporal, spatial and causal reasoning than the text-only version. The work matters as an early open prototype and data resource for conversational video understanding.

Key points

  • VideoChat-Text combines several perception tools: InternVideo, Tag2Text, GRiT, Whisper (speech-to-subtitles) and T5. Their outputs are arranged into a timestamped template and passed to an LLM such as ChatGPT, Vicuna or MOSS.
  • VideoChat-Embed trains only the spatiotemporal and video-language alignment modules, so it is cheap to train end to end.
  • The new instruction dataset focuses on objects, actions, events and causal links over time.
  • There are no quantitative benchmarks. The system struggles with videos of one minute or longer, and its temporal and causal reasoning is still basic.
  • Code and demos are released in the OpenGVLab Ask-Anything repository on GitHub.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

166 papers of our library cite it (of the 140673 whose bibliographies we read); 15 of them have a page here, 151 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2305.06355 · arXiv 2305.06355

@misc{li2023videochat,
  title = {{VideoChat: Chat-Centric Video Understanding}},
  author = {Li, KunChang and He, Yinan and Wang, Yi and Li, Yizhuo and Wang, Wenhai and Luo, Ping and Wang, Yali and Wang, Limin and Qiao, Yu},
  year = {2023},
  doi = {10.48550/arxiv.2305.06355},
  eprint = {2305.06355},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.06355}
}

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