Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Training

• URL: http://arxiv.org/abs/2402.11566v2
• Date: Fri, 8 Mar 2024 02:46:23 GMT
• Title: Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Training
• Authors: Huayi Zhou, Mukun Luo, Fei Jiang, Yue Ding, Hongtao Lu
• Abstract summary: We find that SSHPE can be boosted from two cores: advanced data augmentations and concise consistency training ways. We propose to repeatedly augment unlabeled images with diverse hard augmentations, and generate multi-path predictions sequentially. Compared to SOTA approaches, our method brings substantial improvements on public datasets.
• Score: 25.02026393037821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 2D human pose estimation (HPE) is a basic visual problem. However, its supervised learning requires massive keypoint labels, which is labor-intensive to collect. Thus, we aim at boosting a pose estimator by excavating extra unlabeled data with semi-supervised learning (SSL). Most previous SSHPE methods are consistency-based and strive to maintain consistent outputs for differently augmented inputs. Under this genre, we find that SSHPE can be boosted from two cores: advanced data augmentations and concise consistency training ways. Specifically, for the first core, we discover the synergistic effects of existing augmentations, and reveal novel paradigms for conveniently producing new superior HPE-oriented augmentations which can more effectively add noise on unlabeled samples. We can therefore establish paired easy-hard augmentations with larger difficulty gaps. For the second core, we propose to repeatedly augment unlabeled images with diverse hard augmentations, and generate multi-path predictions sequentially for optimizing multi-losses in a single network. This simple and compact design is interpretable, and easily benefits from newly found augmentations. Comparing to SOTA approaches, our method brings substantial improvements on public datasets. Code is in \url{https://github.com/hnuzhy/MultiAugs}

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