Selective Complementary Features For Multi-Person Pose Estimation

Multi-person pose estimation is a fundamental yet challenging research topic for many computer vision applications. It is difficult to achieve accurate localization results due to occlusion and complex background. In this paper, we propose a novel multi-person pose estimation approach with information complement and attention refinement residual module. To recover occlusion, the complementary features with multi-scale semantics information are extracted by our proposed Information Complement Module (ICM). To effectively discover the channel relationship and selectively highlight task-related regions in the feature maps, we design an Attention Refinement Residual Bottleneck (ARRB) module, which is an extension of residual unit with attention mechanism. We conduct ablation studies to investigate the efficacy of our method and compare it with the state-of-the-art methods on the COCO keypoint benchmark. Experimental results demonstrate that the selective complementary features are effective for multi-person pose estimation.