# A Context-and-Spatial Aware Network for Multi-Person Pose Estimation Source: [A Context-and-Spatial Aware Network for Multi-Person Pose Estimation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:33:37.375Z Authors: Dongdong Yu, Kai Su, Xin Geng, Changhu Wang Published: 2019-05-14T02:25:40.000Z ## Abstract Multi-person pose estimation is a fundamental yet challenging task in computer vision. Both rich context information and spatial information are required to precisely locate the keypoints for all persons in an image. In this paper, a novel Context-and-Spatial Aware Network (CSANet), which integrates both a Context Aware Path and Spatial Aware Path, is proposed to obtain effective features involving both context information and spatial information. Specifically, we design a Context Aware Path with structure supervision strategy and spatial pyramid pooling strategy to enhance the context information. Meanwhile, a Spatial Aware Path is proposed to preserve the spatial information, which also shortens the information propagation path from low-level features to high-level features. On top of these two paths, we employ a Heavy Head Path to further combine and enhance the features effectively. Experimentally, our proposed network outperforms state-of-the-art methods on the COCO keypoint benchmark, which verifies the effectiveness of our method and further corroborates the above proposition. ## 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. - [Human Pose Estimation on COCO (test-dev)](): Human Pose Estimation; COCO (test-dev); AP: 74.5. ## Original sources and implementation [Original paper]() [PDF]()