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<p>Semi-supervised learning is a significant approach to learn robust human pose estimation models that perform well on wild images. Existing semi-supervised methods of human pose estimation mainly focus on instance-agnostic keypoint detection. In multi-person scenes, the arbitrary number of instances that have made pose estimation much more challenging, and current semi-supervised methods cannot fully mine the information in unlabeled data. To leverage the instance information in unlabeled data, we propose an end-to-end semi-supervised training strategy. Different from previous semi-supervised methods in two stages, our method focuses on detector-free frameworks including bottom-up and single-stage ones. It not only performs consistency regularization on heatmaps, but also employs a pseudo-labeling approach to generate instance-specific pseudo annotations. On the COCO and CrowdPose benchmark, the proposed approach outperforms previous instance-agnostic methods under various labeling ratios. Our method is applicable to both bottom up and single-stage frameworks, showing its general applicability.</p>

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Semi-supervised Learning for Detector-free Multi-person Pose Estimation

  • 期刊论文

  • 发表日期：

    2025年8月16日

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  • Haixin Wang,
  • Lu Zhou,
  • Yingying Chen,
  • Ming Tang,
  • Jinqiao Wang

摘要

Semi-supervised learning is a significant approach to learn robust human pose estimation models that perform well on wild images. Existing semi-supervised methods of human pose estimation mainly focus on instance-agnostic keypoint detection. In multi-person scenes, the arbitrary number of instances that have made pose estimation much more challenging, and current semi-supervised methods cannot fully mine the information in unlabeled data. To leverage the instance information in unlabeled data, we propose an end-to-end semi-supervised training strategy. Different from previous semi-supervised methods in two stages, our method focuses on detector-free frameworks including bottom-up and single-stage ones. It not only performs consistency regularization on heatmaps, but also employs a pseudo-labeling approach to generate instance-specific pseudo annotations. On the COCO and CrowdPose benchmark, the proposed approach outperforms previous instance-agnostic methods under various labeling ratios. Our method is applicable to both bottom up and single-stage frameworks, showing its general applicability.