SeFAR combines dual-level temporal sampling, local temporal reversal, and uncertainty-based loss weighting to set new state-of-the-art results in semi-supervised fine-grained action recognition on FineGym and FineDiving.
In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, 9568– 9578
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SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization
SeFAR combines dual-level temporal sampling, local temporal reversal, and uncertainty-based loss weighting to set new state-of-the-art results in semi-supervised fine-grained action recognition on FineGym and FineDiving.