{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T02:06:27Z","timestamp":1769565987447,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686448","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,27]]},"abstract":"<jats:p>Aircraft re-identification is a critical task in aviation and surveillance applications. This paper proposes a Fuzzy Attribute Information Embedding (FAIE) module to enhance aircraft re-identification by integrating textual attribute descriptions with visual data. The FAIE module employs a fuzzy self-attention mechanism to improve the model\u2019s ability to focus on and distinguish attribute features. This method is integrated into the Transformer-based TransReID framework, utilizing multimodal data (image patches, auxiliary information, and attribute embeddings) to achieve feature representation. Experiments on Market-1501 and DukeMTMC-reID pedestrian datasets demonstrate state-of-the-art performance (98.50% mAP and 99.60% Rank-1 on Market-1501). For aircraft re-identification, with optimal attribute embedding strength equal to 0.6, the model achieves 99.2% mAP and 99.3% Rank-1. This method demonstrates the effectiveness of a comprehensive utilization of attribute text and image features in re-identification tasks.<\/jats:p>","DOI":"10.3233\/faia251641","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:18:38Z","timestamp":1769519918000},"source":"Crossref","is-referenced-by-count":0,"title":["Fuzzy Attribute Information Embedding to ViT for Aircraft Re-Identification1"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7040-3591","authenticated-orcid":false,"given":"Jianjun","family":"Huang","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518061, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen, China"}]},{"given":"Yusi","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518061, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen, China"}]},{"given":"Li","family":"Kang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518061, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining XI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251641","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T13:18:39Z","timestamp":1769519919000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251641"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,27]]},"ISBN":["9781643686448"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251641","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,27]]}}}