Cross-Domain Transfer Hashing for Efficient Cross-modal Retrieval

Li, F; Wang, B; Zhu, L; Li, J; Zhang, Z; Chang, X

Cross-Domain Transfer Hashing for Efficient Cross-modal Retrieval

Li, F Wang, B Zhu, L Li, J Zhang, Z Chang, X

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems for Video Technology, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Li, F
dc.contributor.author	Wang, B
dc.contributor.author	Zhu, L
dc.contributor.author	Li, J
dc.contributor.author	Zhang, Z
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.date.accessioned	2024-08-07T05:01:25Z
dc.date.available	2024-08-07T05:01:25Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems for Video Technology, 2024, PP, (99), pp. 1-1
dc.identifier.issn	1051-8215
dc.identifier.issn	1558-2205
dc.identifier.uri	http://hdl.handle.net/10453/180260
dc.description.abstract	Unsupervised cross-modal hashing presents significant advantages in heterogeneous modality retrieval, offering label scalability, high retrieval efficiency, and low storage costs. However, the lack of explicit semantic supervision in this process results in a noticeable semantic deficit, impacting retrieval performance. In this paper, we address this challenge with a dual-pronged approach: Cross-Domain Transfer Hashing (CDTH), a lightweight weakly-supervised cross-modal hashing model. Our method leverages a semantically rich auxiliary domain to augment the target unsupervised cross-modal hash learning process. Simultaneously, we design a lightweight target cross-modal hashing network to reduce semantic requirements, lessening the burden of parameter optimization. Within the auxiliary domain, we perform direct semantic transfer with hashing network parameter transfer and indirect correlation semantic transfer by constructing an auxiliary semantic correlation graph with the identified cross-domain semantic consistent samples. In the target domain, we generate pseudo-labels using CLIP and establish a target weak semantic correlation graph. These two graphs collaborate to bolster the target cross-modal hashing training process. Extensive experiments on three publicly available datasets affirm the superiority of our approach in both retrieval accuracy and training efficiency. The source code for our method is accessible at: https://github.com/WangBowen7/CDTH.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Circuits and Systems for Video Technology
dc.relation.isbasedon	10.1109/TCSVT.2024.3374791
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	Cross-Domain Transfer Hashing for Efficient Cross-modal Retrieval
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
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
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:01:23Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Unsupervised cross-modal hashing presents significant advantages in heterogeneous modality retrieval, offering label scalability, high retrieval efficiency, and low storage costs. However, the lack of explicit semantic supervision in this process results in a noticeable semantic deficit, impacting retrieval performance. In this paper, we address this challenge with a dual-pronged approach: Cross-Domain Transfer Hashing (CDTH), a lightweight weakly-supervised cross-modal hashing model. Our method leverages a semantically rich auxiliary domain to augment the target unsupervised cross-modal hash learning process. Simultaneously, we design a lightweight target cross-modal hashing network to reduce semantic requirements, lessening the burden of parameter optimization. Within the auxiliary domain, we perform direct semantic transfer with hashing network parameter transfer and indirect correlation semantic transfer by constructing an auxiliary semantic correlation graph with the identified cross-domain semantic consistent samples. In the target domain, we generate pseudo-labels using CLIP and establish a target weak semantic correlation graph. These two graphs collaborate to bolster the target cross-modal hashing training process. Extensive experiments on three publicly available datasets affirm the superiority of our approach in both retrieval accuracy and training efficiency. The source code for our method is accessible at: https://github.com/WangBowen7/CDTH.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180260