{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:56:36Z","timestamp":1784300196196,"version":"3.55.0"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001348","name":"Agency for Science, Technology and Research (A*STAR) through the Advanced Manufacturing and Engineering (AME) Programmatic Funds","doi-asserted-by":"publisher","award":["A20H6b0151"],"award-info":[{"award-number":["A20H6b0151"]}],"id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tip.2024.3374048","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:56:04Z","timestamp":1710269764000},"page":"2090-2103","source":"Crossref","is-referenced-by-count":26,"title":["COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3975-3675","authenticated-orcid":false,"given":"Jingyi","family":"Liao","sequence":"first","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A&#x002A;STAR), Fusionopolis Way, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5220-2240","authenticated-orcid":false,"given":"Xun","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A&#x002A;STAR), Fusionopolis Way, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6342-1393","authenticated-orcid":false,"given":"Manh Cuong","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A&#x002A;STAR), Fusionopolis Way, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-7881","authenticated-orcid":false,"given":"Adam","family":"Goodge","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A&#x002A;STAR), Fusionopolis Way, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4748-5792","authenticated-orcid":false,"given":"Chuan Sheng","family":"Foo","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R) and the Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A&#x002A;STAR), Fusionopolis Way, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.70731"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00224"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00838"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_18"},{"key":"ref6","article-title":"Pushing the limits of fewshot anomaly detection in industry vision: Graphcore","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xie"},{"key":"ref7","article-title":"On convergence and stability of GANs","author":"Kodali","year":"2017","journal-title":"arXiv:1705.07215"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00277"},{"key":"ref11","article-title":"Revisiting pretraining for semi-supervised learning in the low-label regime","author":"Xu","year":"2022","journal-title":"arXiv:2205.03001"},{"key":"ref12","article-title":"Unsupervised finetuning","author":"Li","year":"2021","journal-title":"arXiv:2110.09510"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"ref16","first-page":"172","article-title":"A novel anomaly detection scheme based on principal component classifier","volume-title":"Proc. IEEE Found. New Directions Data Mining Workshop","author":"Shyu"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/icassp.2008.4518376"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5220\/0007364500002108"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00301"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref22","first-page":"4393","article-title":"Deep one-class classification","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ruff"},{"key":"ref23","first-page":"31","article-title":"Deep anomaly detection using geometric transformations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Golan"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-69544-6_23"},{"key":"ref25","article-title":"Learning and evaluating representations for deep one-class classification","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Sohn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2917862"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00599"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"ref29","article-title":"Deep semi-supervised anomaly detection","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Ruff"},{"key":"ref30","article-title":"Explainable deep few-shot anomaly detection with deviation networks","author":"Pang","year":"2021","journal-title":"arXiv:2108.00462"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-26387-3_17"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref34","first-page":"29","article-title":"Improved deep metric learning with multi-class N-pair loss objective","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sohn"},{"key":"ref35","first-page":"21808","article-title":"TTT++: When does self-supervised test-time training fail or thrive?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00039"},{"key":"ref37","first-page":"11839","article-title":"CSI: Novelty detection via contrastive learning on distributionally shifted instances","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tack"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3211476"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3231532"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref41","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"Hendrycks","year":"2019","journal-title":"arXiv:1903.12261"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.2478\/aut-2019-0035"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-018-1588-5"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ECTC32696.2021.00345"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"issue":"1","key":"ref46","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"An","year":"2015","journal-title":"Special Lect. IE"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00195"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref50","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref51","article-title":"No free lunch: The hazards of over-expressive representations in anomaly detection","author":"Reiss","year":"2023","journal-title":"arXiv:2306.07284"},{"issue":"11","key":"ref52","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220042"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2023.3270293"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/10346232\/10471293.pdf?arnumber=10471293","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T04:15:07Z","timestamp":1712981707000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10471293\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":54,"URL":"https:\/\/doi.org\/10.1109\/tip.2024.3374048","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}