A Baseline Framework for Part-level Action Parsing and Action Recognition
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 9 references that are in the map. First posted on arXiv 7 Oct 2021.
Builds on
7 of 9 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,492
- 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
- 2019Video Classification with Channel-Separated Convolutional Networkscited by 290
- 2019Large-scale weakly-supervised pre-training for video action recognitioncited by 130
- 2021You Only Look One-level Featurecited by 116
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Citing papers per year: 2022: 1.
1 paper.