Global-Local Complementary Representation Network for Vehicle Re-Identification

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dc.contributor.authorDeng, Mingchenen_US
dc.contributor.authorTang, Ziyaoen_US
dc.contributor.authorXiao, Guoqiangen_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:03:22Z
dc.date.available2025-10-07T06:03:22Z
dc.date.issued2025
dc.description.abstractVehicle Re-Identification (ReID) aims to retrieve images of the same vehicle across multiple non-overlapping cameras. Despite recent advances driven by deep learning, this problem continues to pose challenging due to inter-class similarity and intraclass variation. To address these challenges, we propose a Global-Local Complementary Representation Network (GLCR-Net), which combines global and local features to enhance vehicle ReID accuracy. The global branch employs group convolutions to mitigate overfitting, reduce parameters, and extract comprehensive global features. Meanwhile, the local branch uses a keypoint prediction model to generate keypoint feature maps that are integrated with global features, emphasizing critical regions. Additionally, a Class Activation Mapping (CAM)-based complementary feature learning module is employed to captures features from non-keypoint regions, enriching the feature representation. Experimental results on the VeRi-776 and VehicleID datasets demonstrate that GLCR-Net surpasses state-of-the-art methods in accuracy and generalization. Ablation studies further confirm the effectiveness of group convolutions, keypoint feature integration, and complementary feature learning.en_US
dc.description.sectionheadersDetecting & Estimating from images
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251272
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages7 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251272
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251272
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Supervised learning by classification
dc.subjectComputing methodologies → Supervised learning by classification
dc.titleGlobal-Local Complementary Representation Network for Vehicle Re-Identificationen_US
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