  The majority of existing human parsing methods formulate the task as semantic
segmentation, which regard each semantic category equally and fail to exploit
the intrinsic physiological structure of human body, resulting in inaccurate
results. In this paper, we design a novel semantic neural tree for human
parsing, which uses a tree architecture to encode physiological structure of
human body, and designs a coarse to fine process in a cascade manner to
generate accurate results. Specifically, the semantic neural tree is designed
to segment human regions into multiple semantic subregions (e.g., face, arms,
and legs) in a hierarchical way using a new designed attention routing module.
Meanwhile, we introduce the semantic aggregation module to combine multiple
hierarchical features to exploit more context information for better
performance. Our semantic neural tree can be trained in an end-to-end fashion
by standard stochastic gradient descent (SGD) with back-propagation. Several
experiments conducted on four challenging datasets for both single and multiple
human parsing, i.e., LIP, PASCAL-Person-Part, CIHP and MHP-v2, demonstrate the
effectiveness of the proposed method. Code can be found at
https://isrc.iscas.ac.cn/gitlab/research/sematree.
