Datasetpublic

iLearn-Lab/FineBadmintonBenchmark

FineBadmintonBenchmark Fine-grained badminton video question answering benchmark. Dataset Structure hf_video_clips_qa/: one clip per QA item, named by video_uid (for example video_000001.mp4). finebadmintonbenchmark/: annotation JSON files. each item contains video_uid each QA item maps to exactly one video clip through video_uid Citation @inproceedings{he2025finebadminton, title={Finebadminton: A multi-level dataset for fine-grained badminton… See the full description on the dataset page: https://huggingface.co/datasets/iLearn-Lab/FineBadmintonBenchmark.

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face

0likes66downloads

Dataset Card

FineBadmintonBenchmark

Fine-grained badminton video question answering benchmark.

Dataset Structure

—hf_video_clips_qa/: one clip per QA item, named by video_uid (for example video_000001.mp4).
—finebadmintonbenchmark/: annotation JSON files.
—each item contains video_uid
—each QA item maps to exactly one video clip through video_uid

Citation

bibtex

@inproceedings{he2025finebadminton,
  title={Finebadminton: A multi-level dataset for fine-grained badminton video understanding},
  author={He, Xusheng and Liu, Wei and Ma, Shanshan and Liu, Qian and Ma, Chenghao and Wu, Jianlong},
  booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
  pages={12776--12783},
  year={2025}
}

@misc{he2025finebadminton_arxiv,
  title={Finebadminton: A multi-level dataset for fine-grained badminton video understanding},
  author={He, Xusheng and Liu, Wei and Ma, Shanshan and Liu, Qian and Ma, Chenghao and Wu, Jianlong},
  year={2025},
  eprint={2508.07554},
  archivePrefix={arXiv},
  primaryClass={cs.MM},
  url={https://arxiv.org/abs/2508.07554}
}