  As a fine-grained segmentation task, human parsing is still faced with two
challenges: inter-part indistinction and intra-part inconsistency, due to the
ambiguous definitions and confusing relationships between similar human parts.
To tackle these two problems, this paper proposes a novel
\textit{Affinity-aware Compression and Expansion} Network (ACENet), which
mainly consists of two modules: Local Compression Module (LCM) and Global
Expansion Module (GEM). Specifically, LCM compresses parts-correlation
information through structural skeleton points, obtained from an extra skeleton
branch. It can decrease the inter-part interference, and strengthen structural
relationships between ambiguous parts. Furthermore, GEM expands semantic
information of each part into a complete piece by incorporating the spatial
affinity with boundary guidance, which can effectively enhance the semantic
consistency of intra-part as well. ACENet achieves new state-of-the-art
performance on the challenging LIP and Pascal-Person-Part datasets. In
particular, 58.1% mean IoU is achieved on the LIP benchmark.
