Boundary-guided part reasoning network for human parsing

The task of human parsing aims to segment the human body into different semantic regions. Despite advancements in this field, there are still two issues with current works: boundary indistinction and parsing inconsistency. In this paper, we investigate how to utilize structural information and auxiliary information to jointly solve the above two problems. Drawing inspiration from Transformer architecture, a Boundary-guided Part Reasoning Network (BPRNet) is proposed to combine edge information and associated semantics of body parts for human parsing. Specifically, we design a part representation module to represent human body parts as part features. Based on the Transformer decoder, a multi-head self-attention is used to capture the semantic correlation between the human body. Moreover, we propose a boundary-guided module consisting of absolute boundary attention and reinforced boundary attention. They take advantage of edge information and multi-scale image features to jointly constrain cross-attention to extract global features. Experiments and corresponding results on three public datasets show that the proposed method performs favorably against the state-of-the-art methods.