HEViTPose: High-Efficiency Vision Transformer for Human Pose Estimation

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 PCK@0.5 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}.