Human parsing is a type of semantic segmentation of different human body parts in an image. It is an emerging task in the field of computer vision. Compared with general objects, human body is much more structured but with wide variations in pose and occlusions caused by wearing. In this paper we present a hierarchical label network (HLNet). Firstly, fine categories are merged into body parts with different granularities to obtain multiple parsing maps for each image. Next, a convolutional neural network with a pyramid feature extraction structure is trained under supervision of these maps. Finally, the hierarchical features are fused together to predict the final parsing results. Experimental results on the LIP dataset show that the proposed algorithm achieves higher parsing accuracy and better segmentation performance, compared with common semantic segmentation algorithms.
HU Lina
,
GAO Shenghua
. Hierarchical label-guided human parsing[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(6)
: 820
-827
.
DOI: 10.7523/j.issn.2095-6134.2020.06.014
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