{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:58:56Z","timestamp":1786118336415,"version":"build-2736575974"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neucom.2026.134457","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:15:06Z","timestamp":1783628106000},"page":"134457","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Diffusion-to-detection (D2D): A training-free framework for zero-shot anomaly detection"],"prefix":"10.1016","volume":"700","author":[{"given":"Jun-Yi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2631-0448","authenticated-orcid":false,"given":"Gee-Sern","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Hsuan","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.134457_bib0005","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"5564","article-title":"Zero-shot versus many-shot: unsupervised texture anomaly detection","author":"Aota","year":"2023"},{"key":"10.1016\/j.neucom.2026.134457_bib0010","series-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"19088","article-title":"Correcting deviations from normality: a reformulated diffusion model for multi-class unsupervised anomaly detection","author":"Beizaee","year":"2025"},{"key":"10.1016\/j.neucom.2026.134457_bib0015","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9592","article-title":"MVTec AD\u2013A comprehensive real-world dataset for unsupervised anomaly detection","author":"Bergmann","year":"2019"},{"key":"10.1016\/j.neucom.2026.134457_bib0020","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"22560","article-title":"MasaCtrl: tuning-free mutual self-attention control for consistent image synthesis and editing","author":"Cao","year":"2023"},{"key":"10.1016\/j.neucom.2026.134457_bib0025","series-title":"European Conference on Computer Vision","first-page":"55","article-title":"Adaclip: adapting clip with hybrid learnable prompts for zero-shot anomaly detection","author":"Cao","year":"2024"},{"key":"10.1016\/j.neucom.2026.134457_bib0030","author":"Chen"},{"key":"10.1016\/j.neucom.2026.134457_bib0035","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134457_bib0040","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8472","article-title":"A diffusion-based framework for multi-class anomaly detection","author":"He","year":"2024"},{"key":"10.1016\/j.neucom.2026.134457_bib0045","author":"Hertz"},{"key":"10.1016\/j.neucom.2026.134457_bib0050","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134457_bib0055","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8526","article-title":"Anomalydiffusion: few-shot anomaly image generation with diffusion model","author":"Hu","year":"2024"},{"key":"10.1016\/j.neucom.2026.134457_bib0060","author":"Ilharco"},{"key":"10.1016\/j.neucom.2026.134457_bib0065","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19606","article-title":"Winclip: zero-\/few-shot anomaly classification and segmentation","author":"Jeong","year":"2023"},{"key":"10.1016\/j.neucom.2026.134457_bib0070","series-title":"2021 13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)","first-page":"66","article-title":"Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions","author":"Jezek","year":"2021"},{"key":"10.1016\/j.neucom.2026.134457_bib0075","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2426","article-title":"DiffusionCLIP: text-guided diffusion models for robust image manipulation","author":"Kim","year":"2022"},{"key":"10.1016\/j.neucom.2026.134457_bib0080","article-title":"Memory bank-guided diffusion model for lightweight anomaly detection","author":"Lee","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2026.134457_bib0085","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"7667","article-title":"Open-vocabulary object segmentation with diffusion models","author":"Li","year":"2023"},{"key":"10.1016\/j.neucom.2026.134457_bib0090","series-title":"Proceedings of the 29th Annual Symposium of the German Association for Pattern Recognition (DAGM 2007)","first-page":"12","article-title":"Weakly supervised learning for industrial optical inspection","author":"Matthias","year":"2007"},{"key":"10.1016\/j.neucom.2026.134457_bib0095","series-title":"International Conference on Learning Representations","article-title":"SDEdit: guided image synthesis and editing with stochastic differential equations","author":"Meng","year":"2022"},{"key":"10.1016\/j.neucom.2026.134457_bib0100","series-title":"2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)","first-page":"01","article-title":"VT-ADL: a vision transformer network for image anomaly detection and localization","author":"Mishra","year":"2021"},{"key":"10.1016\/j.neucom.2026.134457_bib0105","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6038","article-title":"Null-text inversion for editing real images using guided diffusion models","author":"Mokady","year":"2023"},{"key":"10.1016\/j.neucom.2026.134457_bib0110","series-title":"DAGM German Conference on Pattern Recognition","first-page":"181","article-title":"Anomaly detection with conditioned denoising diffusion models","author":"Mousakhan","year":"2024"},{"key":"10.1016\/j.neucom.2026.134457_bib0115","series-title":"ACM SIGGRAPH 2023 Conference Proceedings","first-page":"1","article-title":"Zero-shot image-to-image translation","author":"Parmar","year":"2023"},{"key":"10.1016\/j.neucom.2026.134457_bib0120","series-title":"International Conference on Machine Learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.neucom.2026.134457_bib0125","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10684","article-title":"High-resolution image synthesis with latent diffusion models","author":"Rombach","year":"2022"},{"key":"10.1016\/j.neucom.2026.134457_bib0130","author":"Song"},{"key":"10.1016\/j.neucom.2026.134457_bib0135","series-title":"Proceedings of the Computer Vision and Pattern Recognition Conference","first-page":"25508","article-title":"Unseen visual anomaly generation","author":"Sun","year":"2025"},{"key":"10.1016\/j.neucom.2026.134457_bib0140","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3940","article-title":"Dynamic addition of noise in a diffusion model for anomaly detection","author":"Tebbe","year":"2024"},{"key":"10.1016\/j.neucom.2026.134457_bib0145","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134457_bib0150","series-title":"Proceedings of the Asian Conference on Computer Vision","first-page":"260","article-title":"Exploring cross-attention maps in multi-modal diffusion transformers for training-free semantic segmentation","author":"Yamaguchi","year":"2024"},{"key":"10.1016\/j.neucom.2026.134457_bib0155","series-title":"European Conference on Computer Vision","first-page":"1","article-title":"Glad: towards better reconstruction with global and local adaptive diffusion models for unsupervised anomaly detection","author":"Yao","year":"2024"},{"key":"10.1016\/j.neucom.2026.134457_bib0160","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"8330","article-title":"Draem-a discriminatively trained reconstruction embedding for surface anomaly detection","author":"Zavrtanik","year":"2021"},{"issue":"8","key":"10.1016\/j.neucom.2026.134457_bib0165","doi-asserted-by":"crossref","first-page":"7140","DOI":"10.1109\/TPAMI.2025.3570494","article-title":"DiffusionAD: norm-guided one-step denoising diffusion for anomaly detection","volume":"47","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.134457_bib0170","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16699","article-title":"Realnet: a feature selection network with realistic synthetic anomaly for anomaly detection","author":"Zhang","year":"2024"},{"key":"10.1016\/j.neucom.2026.134457_bib0175","series-title":"The Twelfth International Conference on Learning Representations","article-title":"AnomalyCLIP: object-agnostic prompt learning for zero-shot anomaly detection","author":"Zhou","year":"2023"},{"key":"10.1016\/j.neucom.2026.134457_bib0180","series-title":"European Conference on Computer Vision","first-page":"392","article-title":"Spot-the-difference self-supervised pre-training for anomaly detection and segmentation","author":"Zou","year":"2022"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226018552?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226018552?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T15:35:47Z","timestamp":1786030547000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226018552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":36,"alternative-id":["S0925231226018552"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134457","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Diffusion-to-detection (D2D): A training-free framework for zero-shot anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134457","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"134457"}}