CFENet: Content-aware feature enhancement network for multi-person pose estimation.

Multi-person pose estimation is a fundamental yet challenging task in computer vision. Although great success has been made in this field due to the rapid development of deep learning, complex situations (e.g., extreme poses, occlusions, overlapped persons, and crowded scenes) are still not well solved. To further mitigate these issues, we propose a novel Content-aware Feature Enhancement Network (CFENet), which consists of three effective modules: Feature Aggregation and Selection Module (FASM), Feature Fusion Module (FFM) and Dense Upsampling Convolution (DUC) module. The FASM includes Feature Aggregation Module (FAM) and Information Selection Module (ISM). The FAM constructs the hierarchical multi-scale feature aggregations in a granular level to capture more accurate fine-grained representations. The ISM makes the aggregated representations more distinguished, which adaptively highlights the discriminative human part representations both in the spatial location and channel context. Then, we perform FFM which effectively fuses high-resolution spatial features and low-resolution semantic features to obtain more reliable context information for well-estimated joints. Finally, we adopt DUC module to generate more precise prediction, which can recover missing joint details that are usually unavailable in common upsampling process. Comprehensive experiments demonstrate that the proposed approach outperforms most of the popular methods and achieves a competitive performance with the state-of-the-art methods over three benchmark datasets: the recent big dataset CrowdPose, the COCO keypoint detection dataset and the MPII Human Pose dataset. Our code will be released upon acceptance. [ABSTRACT FROM AUTHOR]