X-HRNet: Towards Lightweight Human Pose Estimation with Spatially
  Unidimensional Self-Attention
Yixuan Zhou Xuanhan Wang Xing Xu Lei Zhao Jingkuan Song
High-resolution representation is necessary for human pose estimation to
achieve high performance, and the ensuing problem is high computational
complexity. In particular, predominant pose estimation methods estimate human
joints by 2D single-peak heatmaps. Each 2D heatmap can be horizontally and
vertically projected to and reconstructed by a pair of 1D heat vectors.
Inspired by this observation, we introduce a lightweight and powerful
alternative, Spatially Unidimensional Self-Attention (SUSA), to the pointwise
(1x1) convolution that is the main computational bottleneck in the depthwise
separable 3c3 convolution. Our SUSA reduces the computational complexity of the
pointwise (1x1) convolution by 96% without sacrificing accuracy. Furthermore,
we use the SUSA as the main module to build our lightweight pose estimation
backbone X-HRNet, where `X' represents the estimated cross-shape attention
vectors. Extensive experiments on the COCO benchmark demonstrate the
superiority of our X-HRNet, and comprehensive ablation studies show the
effectiveness of the SUSA modules. The code is publicly available at
https://github.com/cool-xuan/x-hrnet.