  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 complex background. Through analysis of multiple human parsing task,
we observe that human-centric global perception and accurate instance-level
parsing scoring are crucial for obtaining high-quality results. But the most
state-of-the-art methods have not paid enough attention to these issues. 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 representation to enhance multi-scale features for
generating human parsing maps, 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
are available at https://github.com/soeaver/RP-R-CNN.
