Pose-MUM : Reinforcing Key Points Relationship for Semi-Supervised Human Pose Estimation

Abstract

A well-designed strong-weak augmentation strategy and the stable teacher to generate reliable pseudo labels are essential in the teacher-student framework of semi-supervised learning (SSL). Considering these in mind, to suit the semisupervised human pose estimation (SSHPE) task, we propose a novel approach referred to as Pose-MUM that modifies Mix/UnMix (MUM) augmentation [10]. Like MUM in the dense prediction task, the proposed Pose-MUM makes strong-weak augmentation for pose estimation and leads the network to learn the relationship between each human key points much better than the conventional methods by adding the mixing process in intermediate layers in a stochastic manner. In addition, we employ the exponentialmoving-average-normalization (EMAN) [2] teacher, which is stable and well-suited to the SSL framework and further boosts the performance. Extensive experiments on MS-COCO dataset show the superiority of our proposed method by consistently improving the performance over the previous methods following SSHPE benchmark.