A Context-and-Spatial Aware Network for Multi-Person Pose Estimation

TL;DR

This paper introduces CSANet, a novel network that effectively combines context and spatial information for improved multi-person pose estimation, achieving state-of-the-art results on the COCO benchmark.

Contribution

The paper proposes a new network architecture with dedicated context and spatial paths, enhancing feature extraction for pose estimation.

Findings

Outperforms existing methods on COCO keypoint benchmark

Effectively integrates context and spatial information

Validates the importance of combined feature paths

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.