An Axial Compression Transformer for Efficient Human Pose Estimation

Transformer has a wide range of applications in human posture estimation. It can model the global dependence relationship of images through the self-attention mechanism to obtain key human body information. However, Transformer consumes a lot of computation. An axial compression pose transformer (ACPose) method is proposed to reduce part of the computational cost of Transformer by the axial compression of the input matrix, while maintaining the global receptive field by feature fusion. A Local Enhancement Module is constructed to avoid the loss of too much feature information in the compression process. In the COCO dataset experiment, there was a significant reduction in computational cost compared to those of state-of-the-art transformer-based algorithms.