Trusted guidance pyramid network for human parsing

Luo, X; Su, Z; Guo, J; Zhang, G; He, X

Trusted guidance pyramid network for human parsing

Luo, X Su, Z Guo, J Zhang, G He, X

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Publication Type:: Conference Proceeding
Citation:: MM 2018 - Proceedings of the 2018 ACM Multimedia Conference, 2018, pp. 654 - 662
Issue Date:: 2018-10-15

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Field	Value	Language
dc.contributor.author	Luo, X	en_US
dc.contributor.author	Su, Z	en_US
dc.contributor.author	Guo, J	en_US
dc.contributor.author	Zhang, G	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2018-10-15	en_US
dc.identifier.citation	MM 2018 - Proceedings of the 2018 ACM Multimedia Conference, 2018, pp. 654 - 662	en_US
dc.identifier.isbn	9781450356657	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130737
dc.description.abstract	© 2018 Association for Computing Machinery. 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.	en_US
dc.relation.ispartof	MM 2018 - Proceedings of the 2018 ACM Multimedia Conference	en_US
dc.relation.isbasedon	10.1145/3240508.3240634	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Trusted guidance pyramid network for human parsing	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 Association for Computing Machinery. 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.

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http://hdl.handle.net/10453/130737