Improving the Performance of Human Part Segmentation Based on Swin Transformer

Abstract

One of the current challenges in deep learning is semantic segmentation. Moreover, human part segmentation is a sub-task in image segmentation, which differs from traditional segmentation to understand the human body’s intrinsic connections. Convolutional Neural Network (CNN) has always been a standard feature extraction network in human part segmentation. Recently, the proposed Swin Transformer surpasses CNN for many image applications. However, few articles have explored the performance of Swin Transformer in human part segmentation compared to CNN. In this paper, we make a comparison experiment on this issue, and the experimental results prove that even in the area of human part segmentation and without any additional trick, the Swin Transformer has good results compared with CNN. At the same time, this paper also combines the Edge Perceiving Module (EPM) currently commonly used in CNN with Swin Transformer to prove that Swin Transformer can see the intrinsic connection of segmented parts. This research demonstrates the feasibility of applying Swin Transformer to the part segmentation of images, which is conducive to advancing image segmentation technology in the future.