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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            The rapid development of computer vision technology for detecting anomalies in industrial products has received unprecedented attention. In this article, we propose a dual teacher\u2013student-based discrimination (DTSD) model for anomaly detection, which combines the advantages of both embedding-based and reconstruction-based methods. First, the DTSD builds a dual teacher\u2012student architecture consisting of a pretrained teacher encoder with frozen parameters, a student encoder, and a student decoder. By distillation of knowledge from the teacher encoder, the two teacher\u2012student modules acquire the ability to capture both local and global anomaly patterns. Second, to address the issue of poor reconstruction quality faced by previous reconstruction-based approaches in some challenging cases, the model employs a feature bank that stores encoded features of normal samples. By incorporating template features from the feature bank, the student decoder receives explicit guidance to enhance the quality of reconstruction. Finally, a segmentation network is utilized to adaptively integrate multiscale anomaly information from the two teacher\u2013student modules, thereby improving segmentation accuracy. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches. The code of DTSD is publicly available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Math-Computer\/DTSD\">https:\/\/github.com\/Math-Computer\/DTSD<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3736725","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T17:19:21Z","timestamp":1747934361000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DTSD: A Dual Teacher\u2013Student-Based Discrimination Model for Anomaly Detection"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5137-3542","authenticated-orcid":false,"given":"Weizhi","family":"Xian","sequence":"first","affiliation":[{"name":"Chongqing Research Institute of Harbin Institute of Technology, Harbin Institute of Technology, Chongqing, China and School of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7019-0822","authenticated-orcid":false,"given":"Junyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China and Chongqing Research Institute of Harbin Institute of Technology, Harbin Institute of Technology, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-1759","authenticated-orcid":false,"given":"Xuekai","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8342-7453","authenticated-orcid":false,"given":"Jielu","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8207-6845","authenticated-orcid":false,"given":"Yueting","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1902-2384","authenticated-orcid":false,"given":"Kunyin","family":"Guo","sequence":"additional","affiliation":[{"name":"National Elite Institute of Engineering, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1000-3937","authenticated-orcid":false,"given":"Weijia","family":"Jia","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Beijing, China and Guangdong Key Lab of AI and Multi-Modal Data Processing, BNU-HKBU United International College, Zhuhai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1874-3641","authenticated-orcid":false,"given":"Mingliang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University, Chongqing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","article-title":"Unsupervised adversarial example detection of vision transformers for trustworthy edge computing","author":"Li Jiaxing","year":"2024","unstructured":"Jiaxing Li, Yu\u2019an Tan, Jie Yang, Zhengdao Li, Heng Ye, Chenxiao Xia, and Yuanzhang Li. 2024. 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