  Human pose estimation in complicated situations has always been a challenging
task. Many Transformer-based pose networks have been proposed recently,
achieving encouraging progress in improving performance. However, the
remarkable performance of pose networks is always accompanied by heavy
computation costs and large network scale. In order to deal with this problem,
this paper proposes a High-Efficiency Vision Transformer for Human Pose
Estimation (HEViTPose). In HEViTPose, a Cascaded Group Spatial Reduction
Multi-Head Attention Module (CGSR-MHA) is proposed, which reduces the
computational cost through feature grouping and spatial degradation mechanisms,
while preserving feature diversity through multiple low-dimensional attention
heads. Moreover, a concept of Patch Embedded Overlap Width (PEOW) is defined to
help understand the relationship between the amount of overlap and local
continuity. By optimising PEOW, our model gains improvements in performance,
parameters and GFLOPs.
  Comprehensive experiments on two benchmark datasets (MPII and COCO)
demonstrate that the small and large HEViTPose models are on par with
state-of-the-art models while being more lightweight. Specifically, HEViTPose-B
achieves 90.7<EMAIL_ADDRESS>on the MPII test set and 72.6 AP on the COCO test-dev2017
set. Compared with HRNet-W32 and Swin-S, our HEViTPose-B significantly reducing
Params ($\downarrow$62.1%,$\downarrow$80.4%,) and GFLOPs
($\downarrow$43.4%,$\downarrow$63.8%,). Code and models are available at
\url{here}.
