FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding
Dian Shao, Yue Zhao, Bo Dai, Dahua Lin
2020年份
99顶会引用
摘要
Balance Beam Floor Exercise Balance Beam Beam-turns Leap-Jump-Hop BB-flight-handspring 3 turn in tuck stand Wolf jump--hip angle at 45, knees together Flic-flac with step-out Tree Reasoning Tree Reasoning Tree Reasoning Sets Elements More Fine-grained Events level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes. We systematically investigate representative methods on this dataset and obtain a number of interesting findings. We hope this dataset could advance research towards action understanding.
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引用它的顶会 Paper99
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它引用的顶会 Paper4
- TSM: Temporal Shift Module for Efficient Video UnderstandingJi Lin, Chuang Gan, Song HanICCV 2019 · 被引用 2,049 次
- HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationHang Zhao, Antonio Torralba, Lorenzo Torresani, Zhicheng YanICCV 2019 · 被引用 298 次
- Intra- and Inter-Action Understanding via Temporal Action ParsingDian Shao, Yue Zhao, Bo Dai, Dahua LinCVPR 2020
- Temporal Pyramid Network for Action RecognitionCeyuan Yang, Yinghao Xu, Jianping Shi, Bo Dai 等CVPR 2020
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