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-agnos...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.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences,China(No.XDA27030600)National Natural Science Foundation of China(No.62206283).
文摘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.
