SAMKR: Bottom-up Keypoint Regression Pose Estimation Method Based On Subspace Attention Module

As a hot research issue in computer vision, 2D human pose estimation plays an important role in human-computer interaction, intelligent monitoring, 3D human pose estimation and so on. Aiming at the problem of human scale inconsistency in the situation of multi-person, a keypoint regression method based on subspace attention module (SAMKR) is proposed in this paper for the 2D human pose estimation. Firstly, each keypoint is divided into independent regression branches, and then the feature mapping in each keypoint regression branch is evenly divided into a specified number of feature mapping subspaces, and different attention mappings are derived for each feature mapping subspace. By learning different attention maps in each feature subspace, multi-scale feature representation can be effectively improved. The experimental results show that SAMKR achieved 74.4 AP score on the CrowdPose test set, which may lead to an improvement of + 7.1AP, and reached 70.4 AP score on the COCO test-dev data set, which was 0.4AP higher than the baseline.