{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:58:31Z","timestamp":1767923911236,"version":"3.49.0"},"reference-count":24,"publisher":"World Scientific Pub Co Pte Lt","issue":"07","funder":[{"name":"Innovation of Common Technology in Key Industries of Chongqing","award":["cstc2017zdcy-zdzxX0013"],"award-info":[{"award-number":["cstc2017zdcy-zdzxX0013"]}]},{"name":"Technology Innovation and Application Development Project of CSTC","award":["cstc2020jscx-fyzxX0026"],"award-info":[{"award-number":["cstc2020jscx-fyzxX0026"]}]},{"name":"Basic and Advanced Research Project of CSTC","award":["cstc2019jcyj-zdxmX0008"],"award-info":[{"award-number":["cstc2019jcyj-zdxmX0008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p> Cross-modal hashing has attracted considerable attention as it can implement rapid cross-modal retrieval through mapping data of different modalities into a common Hamming space. With the development of deep learning, more and more cross-modal hashing methods based on deep learning are proposed. However, most of these methods use a small batch to train a model. The large batch training can get better gradients and can improve training efficiency. In this paper, we propose the DHLBT method, which uses the large batch training and introduces orthogonal regularization to improve the generalization ability of the DHLBT model. Moreover, we consider the discreteness of hash codes and add the distance between hash codes and features to the objective function. Extensive experiments on three benchmarks show that our method achieves better performance than several existing hashing methods. <\/jats:p>","DOI":"10.1142\/s0218194021500297","type":"journal-article","created":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T08:58:10Z","timestamp":1627289890000},"page":"949-971","source":"Crossref","is-referenced-by-count":2,"title":["DHLBT: Efficient Cross-Modal Hashing Retrieval Method Based on Deep Learning Using Large Batch Training"],"prefix":"10.1142","volume":"31","author":[{"given":"Xuewang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, No. 174, Shazheng Street, Shapingba District, Chongqing 400004, P. R. China"},{"name":"School of Software Engineering, Chongqing University Posts and Telecommunications, No. 2, Chongwen Road, Nan\u2019an District, Chongqing 400065, P. R. China"}]},{"given":"Jinzhao","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, No. 174, Shazheng Street, Shapingba District, Chongqing 400004, P. R. China"},{"name":"School of Software Engineering, Chongqing University Posts and Telecommunications, No. 2, Chongwen Road, Nan\u2019an District, Chongqing 400065, P. R. China"}]},{"given":"Yin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chongqing University Posts and Telecommunications, No. 2, Chongwen Road, Nan\u2019an District, Chongqing 400065, P. R. 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