Most existing methods of human parsing still face
a challenge: how to extract the accurate foreground from similar
or cluttered scenes effectively. In this paper, we propose a
Grammar-induced Wavelet Network (GWNet), to deal with the
challenge. GWNet mainly consists of two modules, including
a blended grammar-induced module and a wavelet prediction
module. We design the blended grammar-induced module to
exploit the relationship of different human parts and the inherent
hierarchical structure of a human body by means of grammar
rules in both cascaded and paralleled manner. In this way,
conspicuous parts, which are easily distinguished from the
background, can amend the segmentation of inconspicuous ones,
improving the foreground extraction. We also design a Partaware
Convolutional Recurrent Neural Network (PCRNN) to
pass messages which are generated by grammar rules. To further
improve the performance, we propose a wavelet prediction
module to capture the basic structure and the edge details of
a person by decomposing the low-frequency and high-frequency
components of features. The low-frequency component can represent
the smooth structures and the high-frequency components
can describe the fine details. We conduct extensive experiments
to evaluate GWNet on PASCAL-Person-Part, LIP, and PPSS
datasets. GWNet obtains state-of-the-art performance on these
human parsing datasets.