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metadata
license: apache-2.0
task_categories:
  - video-text-to-text
language:
  - en

VideoChat3-Academic2M

VideoChat3-Academic2M is the academic video instruction data used by VideoChat3. It re-annotates public academic video datasets for video captioning, video question answering, and fine-grained motion understanding.

The dataset follows an evidence-grounded annotation enhancement pipeline. Short answers, option-only labels, and concise captions are rewritten into richer instruction-following responses that mention visible objects, actions, scenes, temporal cues, and supporting evidence. A consistency filtering stage is then used to keep rewritten annotations aligned with the original academic labels.

This repository provides JSONL annotation files. The original videos are not duplicated here; users should resolve videos from the original dataset paths listed below.

📄 Paper · 🌐 Homepage · 💻 GitHub · 🤗 Paper Page

VideoChat3-Academic2M Overview

Data Sources

VideoChat3-Academic2M aggregates academic caption/QA sources including LLaVA-Video, Spoken-MIT, Vript, StarQA, Sports-QA, and Perception-Test.

The acadmic2M.json file provides the mapping between dataset annotations and video sources. You can refer to it when organizing the dataset structure.

Citation

If you use this data, please cite VideoChat3 and the original video datasets used by the annotations.

@article{li2026videochat3,
  title={VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding},
  author={Li, Xinhao and Zhu, Yuhan and Zeng, Xiangyu and Dong, Yuhao and Wu, Haoning and Zhang, Zhiqiu and Yang, Yuandong and Ma, Changlian and Zhang, Qingyu and Shi, Yansong and others},
  journal={arXiv preprint arXiv:2607.14935},
  year={2026}
}