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
this https URL

本文介绍了一种基于分层语义分割任务的人体解析方法，其使用设计的分级树结构来编码人体生理结构，并在一组内联的过程中生成准确的结果。实验结果表明该方法具有较高的准确性和可行性。