{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T23:20:53Z","timestamp":1773357653260,"version":"3.50.1"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tim.2025.3598383","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T17:34:09Z","timestamp":1755106449000},"page":"1-15","source":"Crossref","is-referenced-by-count":5,"title":["Few-Shot Steel Strip Surface Defect Classification via Self-Correlation Enhancement and Feature Refinement"],"prefix":"10.1109","volume":"74","author":[{"given":"Ke","family":"Gong","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, North University of China, Taiyuan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4259-8950","authenticated-orcid":false,"given":"Ding","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Harbin Institute of Technology, Harbin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9142-697X","authenticated-orcid":false,"given":"Fusen","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Business, University of New South Wales, Canberra, ACT, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7506-3770","authenticated-orcid":false,"given":"Zihan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Business, University of New South Wales, Canberra, ACT, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1796-7256","authenticated-orcid":false,"given":"Fangrui","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Southwest University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8361-8938","authenticated-orcid":false,"given":"Congqing","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2021.1987551"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2019.105986"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/coatings13010017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEA.2012.6360951"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2023.109766"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.111082"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120284"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TENSYMP50017.2020.9230863"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/AIAM48774.2019.00136"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2022.107294"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1115\/1.4049535"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-020-01710-x"},{"key":"ref13","first-page":"8093","article-title":"Overfitting in adversarially robust deep learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Rice"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/RTEICT42901.2018.9012507"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCT.2019.8869364"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2024.3488000"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3485403"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112446"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111265"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref22","first-page":"6105","article-title":"EfficientNet: Rethinking method scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/coatings13122015"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11081200"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3390\/s24144630"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2958826"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3002277"},{"issue":"12","key":"ref28","first-page":"128","article-title":"Surface defect detection algorithm for steel strip based on STCS-YOLO","volume":"33","author":"Zhou","year":"2023","journal-title":"China Metall."},{"key":"ref29","first-page":"8152","article-title":"Data augmentation for meta-learning","volume-title":"Proc. 38th Int. Conf. Mach. Learn. (ICML)","author":"Ni"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/541"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01287"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISNCC62547.2024.10759010"},{"key":"ref33","first-page":"1126","article-title":"Method-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01091"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ad30b6"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jestch.2025.102075"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1807.06521"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3589335.3651920"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120814"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102611"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102366"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3241919"},{"key":"ref44","first-page":"23103","article-title":"A closer look at few-shot classification again","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Luo"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.3390\/sym13040706"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3390\/s20061562"},{"key":"ref47","volume-title":"Recognition of Surface Defects of Aluminum Profiles","year":"2018"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01285"},{"key":"ref50","first-page":"2445","article-title":"Transductive information maximization for few-shot learning","volume-title":"Proc. NeurIPS","author":"Boudiaf"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3169547"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00870"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00599"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26148"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01733"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105835"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3326843"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2022.101566"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3128208"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3246519"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2023.1208781"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-018-1588-5"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/19\/10764799\/11124316.pdf?arnumber=11124316","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T18:21:59Z","timestamp":1757096519000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11124316\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/tim.2025.3598383","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}