sheldongchen / a-baseline-framework-for-part-level-action-parsing-and-action-recognition

2nd place solution for Kinetics-TPS Track in ICCV DeeperAction Workshop 2021

A-Baseline-Framework-for-Part-level-Action-Parsing-and-Action-Recognition

2nd place solution for Kinetics-TPS Track in ICCV DeeperAction Workshop 2021

This technical report introduces our 2nd place solution to Kinetics-TPS Track on Part-level Action Parsing in ICCV DeeperAction Workshop 2021. Our entry is mainly based on YOLOF for instance and part detection, HRNet for human pose estimation, and CSN for video-level action recognition and frame-level part state parsing. We describe technical details for the Kinetics-TPS dataset, together with some experimental results. In the competition, we achieved 61.37% mAP on the test set of Kinetics-TPS.

@inproceedings{conf/iccv/ChenLLL021,
  title={A Baseline Framework for Part-level Action Parsing and Action Recognition},
  author={Xiaodong Chen, Xinchen Liu, Kun Liu, Wu Liu, Tao Mei},
  booktitle={IEEE International Conference on Computer Vision (ICCV) Workshop},
  year={2021}
}

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Welcome update to OpenMMLab 2.0

Welcome update to OpenMMLab 2.0

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