  Human keypoint detection from a single image is very challenging due to
occlusion, blur, illumination and scale variance. In this paper, we address
this problem from three aspects by devising an efficient network structure,
proposing three effective training strategies, and exploiting four useful
postprocessing techniques. First, we find that context information plays an
important role in reasoning human body configuration and invisible keypoints.
Inspired by this, we propose a cascaded context mixer (CCM), which efficiently
integrates spatial and channel context information and progressively refines
them. Then, to maximize CCM's representation capability, we develop a
hard-negative person detection mining strategy and a joint-training strategy by
exploiting abundant unlabeled data. It enables CCM to learn discriminative
features from massive diverse poses. Third, we present several sub-pixel
refinement techniques for postprocessing keypoint predictions to improve
detection accuracy. Extensive experiments on the MS COCO keypoint detection
benchmark demonstrate the superiority of the proposed method over
representative state-of-the-art (SOTA) methods. Our single model achieves
comparable performance with the winner of the 2018 COCO Keypoint Detection
Challenge. The final ensemble model sets a new SOTA on this benchmark.
