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

• URL: http://arxiv.org/abs/2310.00099v1
• Date: Fri, 29 Sep 2023 19:17:30 GMT
• Title: Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation
• Authors: Zhuoran Yu, Manchen Wang, Yanbei Chen, Paolo Favaro, Davide Modolo
• Abstract summary: We introduce a denoising scheme to generate reliable pseudo-heatmaps as targets for learning from unlabeled data. We select the learning targets from these pseudo-heatmaps guided by the estimated cross-student uncertainty. Our results show that our model outperforms previous state-of-the-art semi-supervised pose estimators.
• Score: 38.97427474379367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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