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arXiv · 2310.00099

Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation

Zhuoran Yu·Manchen Wang·Yanbei Chen·Paolo Favaro·Davide Modolo
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Abstract

We propose a new semi-supervised learning design for human pose estimation that revisits the popular dual-student framework and enhances it two ways. First, we introduce a denoising scheme to generate reliable pseudo-heatmaps as targets for learning from unlabeled data. This uses multi-view augmentations and a threshold-and-refine procedure to produce a pool of pseudo-heatmaps. Second, we select the learning targets from these pseudo-heatmaps guided by the estimated cross-student uncertainty. We evaluate our proposed method on multiple evaluation setups on the COCO benchmark. Our results show that our model outperforms previous state-of-the-art semi-supervised pose estimators, especially in extreme low-data regime. For example with only 0.5K labeled images our method is capable of surpassing the best competitor by 7.22 mAP (+25% absolute improvement). We also demonstrate that our model can learn effectively from unlabeled data in the wild to further boost its generalization and performance.

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Zhuoran Yu, Manchen Wang, Yanbei Chen, Paolo Favaro, Davide Modolo. 2023-09-29. Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation. https://arxiv.org/abs/2310.00099

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