Trusted guidance pyramid network for human parsing

Xianghui Luo, Zhuo Su, Jiaming Guo, Gengwei Zhang, Xiangjian He

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

21 Citations (Scopus)

Abstract

Human parsing has a wide range of applications. However, none of the existing methods can productively solve the issue of label parsing fragmentation due to confused and complicated annotations. In this paper, we propose a novel Trusted Guidance Pyramid Network (TGPNet) to address this limitation. Based on a pyramid architecture, we design a Pyramid Residual Pooling (PRP) module setting at the end of a bottom-up approach to capture both global and local level context. In the top-down approach, we propose a Trusted Guidance Multi-scale Supervision (TGMS) that efficiently integrates and supervises multi-scale contextual information. Furthermore, we present a simple yet powerful Trusted Guidance Framework (TGF) which imposes global-level semantics into parsing results directly without extra ground truth labels in model training. Extensive experiments on two public human parsing benchmarks well demonstrate that our TGPNet has a strong ability in solving label parsing fragmentation problem and has an obtained improvement than other methods.

Original languageEnglish
Title of host publicationMM 2018 - Proceedings of the 2018 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages654-662
Number of pages9
ISBN (Electronic)9781450356657
DOIs
Publication statusPublished - 15 Oct 2018
Externally publishedYes
Event26th ACM Multimedia conference, MM 2018 - Seoul, Korea, Republic of
Duration: 22 Oct 201826 Oct 2018

Publication series

NameMM 2018 - Proceedings of the 2018 ACM Multimedia Conference

Conference

Conference26th ACM Multimedia conference, MM 2018
Country/TerritoryKorea, Republic of
CitySeoul
Period22/10/1826/10/18

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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