WCSE

A Novel Human Parsing Method Enhanced by Cross-Refinement

Abstract— The existing human parsing methods lead to some semantic errors and incomplete semantic recognition. To solve this two problems, we design a novel approach, called Cross Refinement Network (CRN). It is built based on the Part Grouping Network (PGN). The CRN remains the same as the PGN in the backbone network and the final refinement block, and uses the ResNet-101 and the Refinement branch separately, just like the PGN. Compared to the PGN, CRN innovatively utilizes the atrous convolution to map the last three feature modules of the backbone network into the feature space, and proposes the cross-refinement module, which consists of the semantic segmentation refinement branch and the edge detection refinement branch, to cross-merge the output feature maps. Experiments are performed based on Crowd Instance-level Human Parsing(CIHP), Pascal-Person-Part (PPP), and LIP datasets separately. The results show that our method outperforms the typical methods on the task of human parsing.

Index Terms—Semantic Segmentation, Human Parsing, Part Grouping Network, Cross Refinement Network

Benzhu Xu, Qing Han, Liping Zheng, Gaofeng Zhang
School of Software, Hefei University of Technology, CHINA
School of Computer and Information, Hefei University of Technology, CHINA
Anhui Province Key Laboratory of Industry Safety and Emergency Technology, CHINA

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Cite:Benzhu Xu, Qing Han, Liping Zheng, Gaofeng Zhang, " A Novel Human Parsing Method Enhanced by Cross-Refinement " Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp. 314-322, Shanghai, China, 19-21 June, 2020.