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
this https URL

通过引入 Spatially Unidimensional Self-Attention (SUSA)，我们减少了 pointwise（1x1）卷积的计算复杂度，并构建了轻量级姿势估计框架 X-HRNet，该框架在 COCO 基准测试中表现卓越，并且 SUSA 模块的有效性得到了全面验证。