Multi-Person Pose Estimation With Enhanced Channel-Wise and Spatial Information.pdf
Abstract
Multi-person pose estimation is an important but challenging problem in computer vision. Although current approaches have achieved significant progress by fusing the
multi-scale feature maps, they pay little attention to enhancing the channel-wise and spatial information of the
feature maps. In this paper, we propose two novel modules to perform the enhancement of the information for the
multi-person pose estimation. First, a Channel Shuffle Module (CSM) is proposed to adopt the channel shuffle operation on the feature maps with different levels, promoting
cross-channel information communication among the pyramid feature maps. Second, a Spatial, Channel-wise Attention Residual Bottleneck (SCARB) is designed to boost
the original residual unit with attention mechanism, adaptively highlighting the information of the feature maps both
in the spatial and channel-wise context. The effectiveness
of our proposed modules is evaluated on the COCO keypoint benchmark, and experimental results show that our
approach achieves the state-of-the-art results.