  We propose a novel Enhanced Feature Aggregation and Selection network
(EFASNet) for multi-person 2D human pose estimation. Due to enhanced feature
representation, our method can well handle crowded, cluttered and occluded
scenes. More specifically, a Feature Aggregation and Selection Module (FASM),
which constructs hierarchical multi-scale feature aggregation and makes the
aggregated features discriminative, is proposed to get more accurate
fine-grained representation, leading to more precise joint locations. Then, we
perform a simple Feature Fusion (FF) strategy which effectively fuses
high-resolution spatial features and low-resolution semantic features to obtain
more reliable context information for well-estimated joints. Finally, we build
a Dense Upsampling Convolution (DUC) module to generate more precise
prediction, which can recover missing joint details that are usually
unavailable in common upsampling process. As a result, the predicted keypoint
heatmaps are more accurate. Comprehensive experiments demonstrate that the
proposed approach outperforms the state-of-the-art methods and achieves the
superior performance 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.
