  Recent research on human pose estimation exploits complex structures to
improve performance on benchmark datasets, ignoring the resource overhead and
inference speed when the model is actually deployed. In this paper, we lighten
the computation cost and parameters of the deconvolution head network in
SimpleBaseline and introduce an attention mechanism that utilizes original,
inter-level, and intra-level information to intensify the accuracy.
Additionally, we propose a novel loss function called heatmap weighting loss,
which generates weights for each pixel on the heatmap that makes the model more
focused on keypoints. Experiments demonstrate our method achieves a balance
between performance, resource volume, and inference speed. Specifically, our
method can achieve 65.3 AP score on COCO test-dev, while the inference speed is
55 FPS and 18 FPS on the mobile GPU and CPU, respectively.
