VideoVista-CoTs

# VideoVista-CoTs This repository contains VideoVista-CoTs, used in [Uni-MoE-2.0](https://huggingface.co/HIT-TMG/Uni-MoE-2.0-Omni) training. This dataset samples a portion of data from [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K), [SEED-Bench-R1](https://huggingface.co/datasets/TencentARC/SEED-Bench-R1), [SR-91K](https://huggingface.co/datasets/RUBBISHLIKE/SpaceR-151k), and [STAR](https://bobbywu.com/STAR/), and uses our automatic Video QA generation framework to perform multi-step reasoning annotations for filtered complex questions. The automatic video QA generation codes and our VideoVista series are presented in [VideoVista Family](https://github.com/HITsz-TMG/VideoVista) # Citation If you find VideoVista-CulturalLingo useful for your research and applications, please cite using this BibTeX: ```bibtex @article{chen2025videovista, title={VideoVista-CulturalLingo: 360$\^{}$\backslash$circ $ Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension}, author={Chen, Xinyu and Li, Yunxin and Shi, Haoyuan and Hu, Baotian and Luo, Wenhan and Wang, Yaowei and Zhang, Min}, journal={arXiv preprint arXiv:2504.17821}, year={2025} } @article{li2024videovista, title={Videovista: A versatile benchmark for video understanding and reasoning}, author={Li, Yunxin and Chen, Xinyu and Hu, Baotian and Wang, Longyue and Shi, Haoyuan and Zhang, Min}, journal={arXiv preprint arXiv:2406.11303}, year={2024} } ```