README.md

---
license: bsd-3-clause
---

# E.T. Chat

[arXiv](https://arxiv.org/abs/2409.18111) | [Project Page](https://polyu-chenlab.github.io/etbench) | [GitHub](https://github.com/PolyU-ChenLab/ETBench)

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.

> [!NOTE]
> This checkpoint was trained from a mixture of stage-2 and stage-3 data, yielding much better general chatting capabilities but slightly sub-optimal grounding performance. It shall be considered as the default setting for this model.

## 🔖 Model Details

### Model Description

- **Developed by:** Ye Liu
- **Model type:** Multi-modal Large Language Model
- **Language(s):** English
- **License:** BSD-3-Clause

### Training Data

The stage-2+3 checkpoint of E.T. Chat was trained from [ET-Instruct-164K](https://huggingface.co/datasets/PolyU-ChenLab/ET-Instruct-164K), [VideoChatGPT](https://mbzuaiac-my.sharepoint.com/:f:/g/personal/hanoona_bangalath_mbzuai_ac_ae/EnLRDehrr8lGqHpC5w1zZ9QBnsiVffYy5vCv8Hl14deRcg?e=Ul5DUE), and [LLaVA-1.5-Instruct](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#visual-instruction-tuning) datasets.

### More Details

Please refer to our [GitHub Repository](https://github.com/PolyU-ChenLab/ETBench) for more details about this model.

## 📖 Citation

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}
}
```