# Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information Source: [Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:34:37.515Z Authors: Kai Su, Dongdong Yu, Zhenqi Xu, Xin Geng, Changhu Wang Published: 2019-05-09T07:12:40.000Z ## 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. ## 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. - [Multi-Person Pose Estimation on COCO (test-dev)](): Multi-person Pose Estimation; COCO (test-dev); AP: 74.3. - [Human Pose Estimation on COCO (test-dev)](): Human Pose Estimation; COCO (test-dev); AP: 74.3. ## Original sources and implementation [Original paper]() [PDF]()