Part Decomposition and Refinement Network for Human Parsing

Abstract

Dear Editor, This letter is concerned with human parsing based on part-wise semantic prediction. Human body can be regarded as a whole structure composed of different semantic parts, and the mainstream single human parser uses semantic segmentation pipeline to solve this problem. However, the differences between human parsing and semantic segmentation tasks bring some issues that are inevitable to avoid. In this paper, we propose a novel method called part decomposition and refinement network (PDRNet), which adopt part-wise mask prediction other than pixel-wise semantic prediction to tackle human parsing task. Specifically, we decompose the human body into different semantic parts and design a decomposition module to learn the central position of each part. The refinement module is proposed to obtain the mask of each human part by learning convolution kernel and convolved feature. In inference stage, the predicted human part masks are combined into a complete human parsing result. Through the decomposition, refinement and combination of human parts, PDRNet greatly reduces the confusion between the target human and the background human, and also significantly improves the semantic consistency of human part. Extensive experiments show that PDRNet performs favorably against state-of-the-art methods on several human parsing benchmarks, including LIP, CIHP and Pascal-Person-Part.