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Dynamic Tri-Level Relation Mining With Attentive Graph for Visible Infrared Re-Identification
Ye, Mang1; Chen, Cuiqun2; Shen, Jianbing3; Shao, Ling4
2022-02
Source PublicationIEEE Transactions on Information Forensics and Security
ISSN1556-6013
Volume17Pages:386-398
Abstract

Matching the daytime visible and nighttime infrared person images, namely visible infrared person re-identification (VI-ReID), is a challenging cross-modality retrieval problem. Due to the difficulty of data collection and annotation in nighttime surveillance, VI-ReID usually suffers from noise problems, making it challenging to directly learn part discriminative features. In order to improve the discriminability and enhance the robustness against noisy images, this paper proposes a novel dynamic tri-level relation mining (DTRM) framework by simultaneously exploring channel-level, part-level intra-modality, and graph-level cross-modality relation cues. To address the misalignment within the person images, we design an intra-modality weighted-part attention (IWPA) to construct part-aggregated representation. It adaptively integrates the body part relation into the local feature learning with a residual batch normalization (RBN) connection scheme. Besides, a cross-modality graph structured attention (CGSA) is incorporated to improve the global feature learning by utilizing the contextual relation between images from two modalities. This module reduces the negative effects of noisy images. To seamlessly integrate two components, a parameter-free dynamic aggregation strategy is designed in a progressive joint learning manner. To further improve the performance, we additionally design a simple yet effective channel-level learning strategy by exploiting the rich channel information of visible images, which significantly reinforces the performance without modifying the network structure or changing the training process. Extensive experiments on two visible infrared re-identification datasets have verified the effectiveness under various settings. Code is available at: https://github.com/mangye16/DDAG

KeywordFeature Extraction Image Color Analysis Noise Measurement Periodic Structures Representation Learning Robustness Training
DOI10.1109/TIFS.2021.3139224
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000748395300003
Scopus ID2-s2.0-85122303083
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorShen, Jianbing
Affiliation1.Wuhan University, Hubei Key Laboratory of Multimedia and Network Communication Engineering, National Engineering Research Center for Multimedia Software, School of Computer Science, Institute of Artificial Intelligence, Wuhan, 430072, China
2.Hefei University of Technology, School of Computer Science and Information Engineering, Anhui, 230002, China
3.University of Macau, Department of Computer and Information Science, State Key Laboratory of Internet of Things for Smart City, Macau, Macao
4.Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Ye, Mang,Chen, Cuiqun,Shen, Jianbing,et al. Dynamic Tri-Level Relation Mining With Attentive Graph for Visible Infrared Re-Identification[J]. IEEE Transactions on Information Forensics and Security,2022,17:386-398.
APA Ye, Mang,Chen, Cuiqun,Shen, Jianbing,&Shao, Ling.(2022).Dynamic Tri-Level Relation Mining With Attentive Graph for Visible Infrared Re-Identification.IEEE Transactions on Information Forensics and Security,17,386-398.
MLA Ye, Mang,et al."Dynamic Tri-Level Relation Mining With Attentive Graph for Visible Infrared Re-Identification".IEEE Transactions on Information Forensics and Security 17(2022):386-398.
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