Graph convolutional networks in language and vision: A survey

Ren, H; Lu, W; Xiao, Y; Chang, X; Wang, X; Dong, Z; Fang, D

Graph convolutional networks in language and vision: A survey

Ren, H Lu, W Xiao, Y Chang, X

Wang, X Dong, Z Fang, D

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2022, 251
Issue Date:: 2022-09-05

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Field	Value	Language
dc.contributor.author	Ren, H
dc.contributor.author	Lu, W
dc.contributor.author	Xiao, Y
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Wang, X
dc.contributor.author	Dong, Z
dc.contributor.author	Fang, D
dc.date.accessioned	2023-03-31T09:17:04Z
dc.date.available	2023-03-31T09:17:04Z
dc.date.issued	2022-09-05
dc.identifier.citation	Knowledge-Based Systems, 2022, 251
dc.identifier.issn	0950-7051
dc.identifier.issn	1872-7409
dc.identifier.uri	http://hdl.handle.net/10453/168954
dc.description.abstract	Graph convolutional networks (GCNs) have a strong ability to learn graph representation and have achieved good performance in a range of applications, including social relationship analysis, biological information processing, natural language processing (NLP), computer vision (CV), and so on. In recent years, the application of GCNs in natural language processing and computer vision has attracted substantial interest from researchers, as a result of which many studies based on GCNs have emerged in the fields of natural language processing and computer vision. However, to the best of our knowledge, a comprehensive survey of GCN application in natural language processing and computer vision has not yet been conducted. Accordingly, this survey presents a comprehensive review of the principles of GCNs and its applications in these two fields. First, we summarize the principles of the two types of GCNs, namely spatial methods and spectral methods. Then we divide GCN applications into two categories: natural language processing and computer vision. Subsequently, we present multiple applications from each category in detail. Finally, we outline the limitations of GCNs and discuss possible future research directions.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2022.109250
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Graph convolutional networks in language and vision: A survey
dc.type	Journal Article
utslib.citation.volume	251
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-09-05T00:00:00+1000Z
dc.date.updated	2023-03-31T09:16:55Z
pubs.publication-status	Published
pubs.volume	251

Abstract:

Graph convolutional networks (GCNs) have a strong ability to learn graph representation and have achieved good performance in a range of applications, including social relationship analysis, biological information processing, natural language processing (NLP), computer vision (CV), and so on. In recent years, the application of GCNs in natural language processing and computer vision has attracted substantial interest from researchers, as a result of which many studies based on GCNs have emerged in the fields of natural language processing and computer vision. However, to the best of our knowledge, a comprehensive survey of GCN application in natural language processing and computer vision has not yet been conducted. Accordingly, this survey presents a comprehensive review of the principles of GCNs and its applications in these two fields. First, we summarize the principles of the two types of GCNs, namely spatial methods and spectral methods. Then we divide GCN applications into two categories: natural language processing and computer vision. Subsequently, we present multiple applications from each category in detail. Finally, we outline the limitations of GCNs and discuss possible future research directions.

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http://hdl.handle.net/10453/168954