{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:57:32Z","timestamp":1770044252002,"version":"3.49.0"},"reference-count":7,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>Person re-identification with natural language description is a process of retrieving the corresponding person\u2019s image from an image dataset according to a text description of the person. The key challenge in this cross-modal task is to extract visual and text features and construct loss functions to achieve cross-modal matching between text and image. Firstly, we designed a two-branch network framework for person re-identification with natural language description. In this framework we include the following: a Bi-directional Long Short-Term Memory (Bi-LSTM) network is used to extract text features and a truncated attention mechanism is proposed to select the principal component of the text features; a MobileNet is used to extract image features. Secondly, we proposed a Cascade Loss Function (CLF), which includes cross-modal matching loss and single modal classification loss, both with relative entropy function, to fully exploit the identity-level information. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves better results in Top-5 and Top-10 than other current 10 state-of-the-art algorithms.<\/jats:p>","DOI":"10.3233\/jifs-210382","type":"journal-article","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T11:38:02Z","timestamp":1635507482000},"page":"6575-6587","source":"Crossref","is-referenced-by-count":0,"title":["Truncated attention mechanism and cascade loss for cross-modal person re-identification"],"prefix":"10.1177","volume":"41","author":[{"given":"Shuo","family":"Shi","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, China"}]},{"given":"Changwei","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, China"},{"name":"Beijing Branch of China United Network Communication Co., Ltd, Beijing, China"}]},{"given":"Yingchun","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, China"}]},{"given":"Stephen","family":"Lean","sequence":"additional","affiliation":[{"name":"School of Fundamental Sciences, Massey University, Palmerston North, New Zealand"}]},{"given":"Gang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, China"}]},{"given":"Ming","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, China"}]}],"member":"179","reference":[{"issue":"6","key":"10.3233\/JIFS-210382_ref1","first-page":"770","article-title":"Survey on person re-identification research","volume":"12","author":"Song","year":"2017","journal-title":"CAAI Transactions on Intelligent Systems"},{"issue":"9","key":"10.3233\/JIFS-210382_ref2","doi-asserted-by":"crossref","first-page":"3436","DOI":"10.1007\/s10489-019-01459-8","article-title":"A spatial and temporal features mixture model with body parts for video-based person re-identification","volume":"49","author":"Liu","year":"2019","journal-title":"Applied Intelligence"},{"key":"10.3233\/JIFS-210382_ref3","first-page":"6471","article-title":"A neuromorphic person re-identification framework for video surveillance,","volume":"5","author":"Nanda","year":"2017","journal-title":"IEEE Access"},{"issue":"2","key":"10.3233\/JIFS-210382_ref16","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TPAMI.2018.2797921","article-title":"Learning Two-Branch Neural Networks for Image-Text Matching Tasks","volume":"41","author":"Wang","year":"2018","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"10.3233\/JIFS-210382_ref22","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.ipm.2018.12.005","article-title":"Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean","volume":"56","author":"Song","year":"2019","journal-title":"Inform Process Manag"},{"issue":"5","key":"10.3233\/JIFS-210382_ref34","first-page":"1","article-title":"Evaluating appearance models for recognition, reacquisition, and tracking","volume":"3","author":"Gray","year":"2007","journal-title":"In IEEE International Workshop on Performance Evaluation for Tracking and Surveillance"},{"key":"10.3233\/JIFS-210382_ref37","doi-asserted-by":"crossref","first-page":"5542","DOI":"10.1109\/TIP.2020.2984883","article-title":"Improving Description-Based Person Re-Identification by Multi-Granularity Image-Text Alignments","volume":"29","author":"Niu","year":"2020","journal-title":"in IEEE Transactions on Image Processing"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-210382","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T03:22:51Z","timestamp":1770002571000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-210382"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":7,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-210382","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}