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In this paper, we propose a Multi-scale Dynamic Fusion Network (MDFN) to address these challenges in the VI-ReID task. Specifically, the proposed MDFN consists of the Dynamic Feature Fusion (DFF), Dynamic Perception Enhancement (DPE), and Feature Reweighting with Similarity (FRS) modules. The DFF module dynamically extracts local and long-range dependencies among features to obtain finer-grained discriminative features. The DPE module extracts multi-scale features from both visible and infrared modalities to generate diverse embeddings. The FRS module mitigates the impact of information imbalance between modalities, thereby further improving performance. Extensive experiments on the SYSU-MM01 and RegDB datasets show that our MDFN outperforms other state-of-the-art methods, especially in complex dynamic scenes with occlusions, background shifts, and pose changes.<\/jats:p>","DOI":"10.1145\/3715330","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T15:53:42Z","timestamp":1738079622000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-Scale Dynamic Fusion for Visible-Infrared Person Re-Identification"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8979-9774","authenticated-orcid":false,"given":"Shen","family":"Wang","sequence":"first","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2621-2981","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7580-0553","authenticated-orcid":false,"given":"Renjie","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9859-9573","authenticated-orcid":false,"given":"Kejun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9097-2318","authenticated-orcid":false,"given":"Chia-Wen","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3475-6098","authenticated-orcid":false,"given":"Chengtao","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China, Heilongjiang Provincial Key Laboratory of Environment Intelligent Perception, Harbin, China, and Key laboratory of Intelligent Technology and Application of Marine Equipment, Harbin Engineering University, Harbin, China"}]}],"member":"320","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3141868"},{"key":"e_1_3_1_3_2","first-page":"677--683","article-title":"Cross-modality person re-identification with generative adversarial training","author":"Dai Pingyang","year":"2018","unstructured":"Pingyang Dai, Rongrong Ji, Haibin Wang, Qiong Wu, and Yuyu Huang. 2018. 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