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While existing methods increasingly leverage visual context, they are fundamentally limited by static fusion strategies for multi\u2010view features and their neglect of semantic context from other modalities. To overcome these limitations, we propose the dynamic cross\u2010modal context fusion (DCMCF) framework, which establishes a core multi\u2010modal fusion architecture by explicitly leveraging self\u2010generated textual descriptions to provide semantics\u2010driven matching guidance. DCMCF consists of three core components: (1) multi\u2010modal feature extraction, which employs an enhanced multi\u2010view visual encoder with dynamic gating and a zero\u2010shot pipeline using pre\u2010trained VLMs to generate multi\u2010level textual descriptions from images; (2) a dynamic cross\u2010modal fusion (DCMF) module, which hierarchically and adaptively fuses the visual and textual features through cross\u2010modal attention and input\u2010dependent gating; and (3) a text\u2010guided inter\u2010image group context ranking (T\u2010IGCR) algorithm, which refines retrieval results by measuring holistic image consistency in both visual and textual spaces. Experiments on CUHK\u2010SYSU and PRW datasets demonstrate state\u2010of\u2010the\u2010art performance, achieving\n                    <jats:bold>96.9%\/97.3%<\/jats:bold>\n                    (mAP\/top\u20101) and\n                    <jats:bold>56.1%\/91.2%<\/jats:bold>\n                    , respectively. This work demonstrates the significant potential of dynamic cross\u2010modal context fusion for advancing image\u2010based person\u00a0search.\n                  <\/jats:p>","DOI":"10.1049\/ipr2.70263","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T13:04:21Z","timestamp":1766149461000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DCMCF: Dynamic Cross\u2010Modal Context Fusion for Image\u2010Based Person Search"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9604-7564","authenticated-orcid":false,"given":"Fudong","family":"Nian","sequence":"first","affiliation":[{"name":"The School of Electronic Information and Automation Hefei University Hefei China"}]},{"given":"Yingfang","family":"Wang","sequence":"additional","affiliation":[{"name":"The School of Electronic Information and Automation Hefei University Hefei China"}]},{"given":"Aoyu","family":"Liu","sequence":"additional","affiliation":[{"name":"The Quectel Wireless Solutions Co., Ltd. Shanghai China"}]},{"given":"Yun","family":"Fu","sequence":"additional","affiliation":[{"name":"The National Science Library Chinese Academy of Sciences Beijing China"}]},{"given":"Yanhong","family":"Gu","sequence":"additional","affiliation":[{"name":"The School of Electronic Information and Automation Hefei University Hefei China"}]}],"member":"265","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","unstructured":"X.Lin P.Ren Y.Xiao X.Chang andA.Hauptmann \u201cPerson Search Challenges and Solutions: A Survey \u201d inInternational Joint Conference on Artificial Intelligence 2021(Association for the Advancement of Artificial Intelligence 2021) 4500\u20134507.","DOI":"10.24963\/ijcai.2021\/613"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110434"},{"key":"e_1_2_12_4_1","doi-asserted-by":"crossref","unstructured":"D.Chen S.Zhang W.Ouyang J.Yang andB.Schiele \u201cHierarchical Online 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