DIE-CDK: A Discriminative Information Enhancement Method with Cross-modal Domain Knowledge for Fine-grained Ship Detection

Guo, Y; Yu, H; Ma, L; Luo, X; Xie, S

DIE-CDK: A Discriminative Information Enhancement Method with Cross-modal Domain Knowledge for Fine-grained Ship Detection

Guo, Y Yu, H

Ma, L Luo, X Xie, S

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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	Guo, Y
dc.contributor.author	Yu, H https://orcid.org/0000-0003-3444-9992
dc.contributor.author	Ma, L
dc.contributor.author	Luo, X
dc.contributor.author	Xie, S
dc.date.accessioned	2024-08-21T05:51:05Z
dc.date.available	2024-08-21T05:51:05Z
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/10453/180521
dc.description.abstract	Due to the overarching similarities of ships, subtle information is imperative for fine-grained ship detection. However, this information is easily lost in adverse weather (e.g., fog, rain, snow, and cloud) or occlusion scenarios. Experts can quickly and accurately recognize fine-grained objects because they have the domain knowledge to help them find the most discriminative information (e.g., edge, structure, texture, and class semantics); thus, they do not need a lot of information to make an identification. Motivated by it, we propose a discriminative information enhancement method with cross-modal domain knowledge (DIE-CDK) for fine-grained ship detection. The core idea behind DIE-CDK is to enhance the discriminative information about fine-grained ships by fusing cross-modal domain knowledge. The introduced cross-modal domain knowledge comprises local and global knowledge: 1) local knowledge is the knowledge of visual shape (e.g., edge contour) which is extracted from the image domain; and 2) global knowledge is the knowledge of the class semantics which is obtained from the common sense domain. In addition, to further study fine-grained ship detection, we introduce a Fine-grained ship dataset (called FgShips). Experiments show that our proposed DIE-CDK method achieves impressive gains in detection performance and outperforms state-of-the-art methods on fine-grained ship and public datasets.
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.2024.3407057
dc.rights	info:eu-repo/semantics/closedAccess
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	DIE-CDK: A Discriminative Information Enhancement Method with Cross-modal Domain Knowledge for Fine-grained Ship Detection
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
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-21T05:50:54Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Due to the overarching similarities of ships, subtle information is imperative for fine-grained ship detection. However, this information is easily lost in adverse weather (e.g., fog, rain, snow, and cloud) or occlusion scenarios. Experts can quickly and accurately recognize fine-grained objects because they have the domain knowledge to help them find the most discriminative information (e.g., edge, structure, texture, and class semantics); thus, they do not need a lot of information to make an identification. Motivated by it, we propose a discriminative information enhancement method with cross-modal domain knowledge (DIE-CDK) for fine-grained ship detection. The core idea behind DIE-CDK is to enhance the discriminative information about fine-grained ships by fusing cross-modal domain knowledge. The introduced cross-modal domain knowledge comprises local and global knowledge: 1) local knowledge is the knowledge of visual shape (e.g., edge contour) which is extracted from the image domain; and 2) global knowledge is the knowledge of the class semantics which is obtained from the common sense domain. In addition, to further study fine-grained ship detection, we introduce a Fine-grained ship dataset (called FgShips). Experiments show that our proposed DIE-CDK method achieves impressive gains in detection performance and outperforms state-of-the-art methods on fine-grained ship and public datasets.

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http://hdl.handle.net/10453/180521