# Pose-MUM : Reinforcing Key Points Relationship for Semi-Supervised Human Pose Estimation Source: [Pose-MUM : Reinforcing Key Points Relationship for Semi-Supervised Human Pose Estimation]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:00:55.020Z Authors: JongMok Kim, Hwijun Lee, Jaeseung Lim, Jongkeun Na, Nojun Kwak, Jin Young Choi Published: 2022-03-15T12:48:40.000Z ## 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 semi-supervised human pose estimation (SSHPE) task, we propose a novel approach referred to as Pose-MUM that modifies Mix/UnMix (MUM) augmentation. 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 point much better than the conventional methods by adding the mixing process in intermediate layers in a stochastic manner. In addition, we employ the exponential-moving-average-normalization (EMAN) teacher, which is stable and well-suited to the SSL framework and furthermore 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()