Renovating Parsing R-CNN for Accurate Multiple Human Parsing

Abstract

Multiple human parsing aims to segment various human parts and associate each part with the corresponding instance simultaneously. This is a very challenging task due to the diverse human appearance, semantic ambiguity of different body parts and clothing, and complex background. Through analysis of human parsing task, we observe that human-centric context perception and accurate instance-level parsing scoring are particularly important for obtaining high-quality results. But the most state-of-the-art methods have not paid enough attention to these problems. To reverse this phenomenon, we present Renovating Parsing R-CNN (RP R-CNN), which introduces a global semantic enhanced feature pyramid network and a parsing re-scoring network into the existing high-performance pipeline. The proposed RP R-CNN adopts global semantic feature to enhance multi-scale features for generating human parsing, and regresses a confidence score to represent its quality. Extensive experiments show that RP R-CNN performs favorably against state-of-the-art methods on CIHP and MHP-v2 datasets. Code and models will be publicly available.