An Efficient Method for Boosting Human Pose Estimation
Shicheng Xiang, Xiao Chen, Jun Wei Zhou · 2021
Most existing human pose estimation approaches fall into designing new network architectures or tend to apply deeper layers. Most of the methods are time-consuming, and lightweight plug-in for improving human pose estimation to the existing network gets little investigation. In this paper, we propose a lightweight plug-in module to boost the performance of human pose estimation named PoseReNed. PoseReNet is a network with three branches that are designed to tackle the attenuation caused by occluded keypoints or different scales of the keypoints. Generally, small-scale keypoints are more difficult to detect, and we observe that different channels of the output feature map have different attributes to the performance of estimation. We apply a channel attention mechanism to re-weight the channel to trade-off among different scales of the keypoints. By aggregating multiscale output feature maps, the pose estimation performance can be improved. Serving as a model-agnostic plug-in, PoseReNet brings about significant performance boost to existing human pose estimation models. Extensive experiments show that PoseReNet can effectively improve precision on COCO and MPII.