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The primary difficulties stem from substantial inter\u2010modal discrepancies and intra\u2010modal feature variations, which complicate effective cross\u2010modal matching. While existing approaches generally focus on embedding heterogeneous modal data into a unified feature space to extract shared representations, they often overlook the discriminative identity information embedded within modality\u2010specific features. To overcome this inherent limitation, we propose a novel pinwheel\u2010guided dynamic representation network (PDRNet), designed to mine and enhance the directional structural cues and scale\u2010sensitive discriminative features inherent in modality\u2010specific representations. Specifically, we integrate direction\u2010aware pinwheel convolution (PConv) into a two\u2010stream architecture to strengthen local structural representation and guide the learning of shared semantic features, thereby improving both the discriminability and structural modeling of modality\u2010specific information. Furthermore, to accommodate scale disparities across modalities and individuals, we incorporate a scale\u2010based dynamic loss (SD loss), which adaptively adjusts the loss weights related to scale and positional information. This mechanism mitigates the error amplification often observed in small\u2010scale samples and enhances both the discriminative power and robustness of cross\u2010modal matching across varying scales. We perform extensive experiments on multiple well\u2010established public benchmarks. The results consistently show that the proposed PDRNet achieves superior performance compared to existing methods in both recognition accuracy and cross\u2010modal matching effectiveness.<\/jats:p>","DOI":"10.1002\/cpe.70569","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:46:12Z","timestamp":1769733972000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PDRNet: Pinwheel\u2010Guided Dynamic Representation Learning for Visible\u2010Infrared Person Re\u2010Identification"],"prefix":"10.1002","volume":"38","author":[{"given":"Fengshan","family":"Lai","sequence":"first","affiliation":[{"name":"School of Big Data and Computer Science Guizhou Normal University  Gui Yang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixiang","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Big Data and Computer Science Guizhou Normal University  Gui Yang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongyu","family":"Jia","sequence":"additional","affiliation":[{"name":"Guizhou Key Laboratory of Advanced Computing Guizhou Normal University  Gui Yang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1715-3324","authenticated-orcid":false,"given":"Daoxun","family":"Xia","sequence":"additional","affiliation":[{"name":"Guizhou Key Laboratory of Advanced Computing Guizhou Normal University  Gui Yang China"},{"name":"Leading Talent Workstation for Technological Innovation in Artificial Intelligence of Guizhou Province Guizhou Normal University  Gui Yang China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112737","article-title":"Enhancing Person Re\u2010Identification via Uncertainty Feature Fusion Method and Auto\u2010Weighted Measure Combination","volume":"307","author":"Che Q. H.","year":"2025","journal-title":"Knowledge\u2010Based Systems"},{"key":"e_1_2_10_3_1","first-page":"9637","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Chen G.","year":"2019"},{"issue":"12","key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"8599","DOI":"10.1109\/TCSVT.2022.3194084","article-title":"Global Relation\u2010Aware Contrast Learning for Unsupervised Person Re\u2010Identification","volume":"32","author":"Zhang H.","year":"2022","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105702"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3233716"},{"issue":"5","key":"e_1_2_10_7_1","doi-asserted-by":"crossref","first-page":"4596","DOI":"10.1609\/aaai.v38i5.28259","article-title":"High\u2010Order Structure Based Middle\u2010Feature Learning for Visible\u2010Infrared Person 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