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E.T. Chat is a novel time-sensitive Video-LLM that reformulates timestamp prediction as an embedding matching problem, serving as a strong baseline on E.T. Bench. E.T. Chat consists of a visual encoder, a frame compressor, and a LLM. A special token <vid> is introduced to trigger frame embedding matching for timestamp prediction.

The stage-2 checkpoint of E.T. Chat was trained from VideoChatGPT and LLaVA-1.5-Instruct datasets.

Please refer to our GitHub Repository for more details about this model.

Please kindly cite our paper if you find this project helpful.

@inproceedings{liu2024etbench,
  title={E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding},
  author={Liu, Ye and Ma, Zongyang and Qi, Zhongang and Wu, Yang and Chen, Chang Wen and Shan, Ying},
  booktitle={Neural Information Processing Systems (NeurIPS)},
  year={2024}
}