{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:10:51Z","timestamp":1780675851810,"version":"3.54.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08397-6","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:53:29Z","timestamp":1773716009000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CFDT-CLIP:fabric anomaly detection with collaborative fusion under dual-text prompts"],"prefix":"10.1007","volume":"82","author":[{"given":"Junjie","family":"Zhuang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiuzhen","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"issue":"1","key":"8397_CR1","doi-asserted-by":"publisher","first-page":"114","DOI":"10.7763\/IJCTE.2013.V5.658","volume":"5","author":"E Hoseini","year":"2013","unstructured":"Hoseini E, Farhadi F, Tajeripour F (2013) Fabric defect detection using auto-correlation function. Int J Comput Theory Eng 5(1):114\u2013117. https:\/\/doi.org\/10.7763\/IJCTE.2013.V5.658","journal-title":"Int J Comput Theory Eng"},{"issue":"1","key":"8397_CR2","doi-asserted-by":"publisher","first-page":"9948808","DOI":"10.1155\/2021\/9948808","volume":"2021","author":"C Li","year":"2021","unstructured":"Li C, Li J, Li Y, He L, Fu X, Chen J (2021) Fabric defect detection in textile manufacturing: a survey of the state of the art. Secur Commun Netw 2021(1):9948808\u20131994880813. https:\/\/doi.org\/10.1155\/2021\/9948808","journal-title":"Secur Commun Netw"},{"key":"8397_CR3","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1016\/j.procs.2018.07.058","volume":"133","author":"P Anandan","year":"2018","unstructured":"Anandan P, Sabeenian RS (2018) Fabric defect detection using discrete curvelet transform. Proc Compute Sci 133:1056\u20131065. https:\/\/doi.org\/10.1016\/j.procs.2018.07.058","journal-title":"Proc Compute Sci"},{"issue":"2","key":"8397_CR4","first-page":"45","volume":"31","author":"L Chen","year":"2020","unstructured":"Chen L, Zeng S, Gao Q, Liu B (2020) Adaptive gabor filtering for fabric defect inspection. J Comput 31(2):45\u201355","journal-title":"J Comput"},{"issue":"4","key":"8397_CR5","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.patcog.2004.07.009","volume":"38","author":"HYT Ngan","year":"2005","unstructured":"Ngan HYT, Pang GKH, Yung SP, Ng MK (2005) Wavelet based methods on patterned fabric defect detection. Pattern Recogn 38(4):559\u2013576. https:\/\/doi.org\/10.1016\/j.patcog.2004.07.009","journal-title":"Pattern Recogn"},{"key":"8397_CR6","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.ins.2020.08.100","volume":"546","author":"B Shi","year":"2021","unstructured":"Shi B, Liang J, Di L, Chen C, Hou Z (2021) Fabric defect detection via low-rank decomposition with gradient information and structured graph algorithm. Inf Sci 546:608\u2013626. https:\/\/doi.org\/10.1016\/j.ins.2020.08.100","journal-title":"Inf Sci"},{"key":"8397_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/J.NEUCOM.2018.10.070","author":"Y Li","year":"2019","unstructured":"Li Y, Zhang D, Lee D (2019) Automatic fabric defect detection with a wide-and-compact network. Neurocomputing. https:\/\/doi.org\/10.1016\/J.NEUCOM.2018.10.070","journal-title":"Neurocomputing"},{"key":"8397_CR8","doi-asserted-by":"publisher","DOI":"10.1177\/1558925020908268","author":"J Jing","year":"2020","unstructured":"Jing J, Zhuo D, Zhang H, Liang Y, Zheng M (2020) Fabric defect detection using the improved yolov3 model. J Eng Fibers Fabr. https:\/\/doi.org\/10.1177\/1558925020908268","journal-title":"J Eng Fibers Fabr"},{"issue":"12","key":"8397_CR9","doi-asserted-by":"publisher","first-page":"6655","DOI":"10.1007\/S00371-022-02754-1","volume":"39","author":"C Wei","year":"2023","unstructured":"Wei C, Liang J, Liu H, Hou Z, Huan Z (2023) Multi-stage unsupervised fabric defect detection based on DCGAN. Vis Comput 39(12):6655\u20136671. https:\/\/doi.org\/10.1007\/S00371-022-02754-1","journal-title":"Vis Comput"},{"key":"8397_CR10","doi-asserted-by":"publisher","DOI":"10.1177\/00405175221144777","author":"H Cheng","year":"2023","unstructured":"Cheng H, Liang J, Liu H (2023) Image restoration fabric defect detection based on the dual generative adversarial network patch model. Text Res J. https:\/\/doi.org\/10.1177\/00405175221144777","journal-title":"Text Res J"},{"key":"8397_CR11","doi-asserted-by":"publisher","unstructured":"Karegowda AG, Pooja R, Rani AL, Devika G (2024) Detection of stain defects in textile industry using state-of-art transfer learning models. In: ICSSES, pp 1\u20136 https:\/\/doi.org\/10.1109\/ICSSES62373.2024.10561384. IEEE","DOI":"10.1109\/ICSSES62373.2024.10561384"},{"key":"8397_CR12","doi-asserted-by":"publisher","unstructured":"\u015eeker A (2018) Evaluation of fabric defect detection based on transfer learning with pre-trained alexnet. In: IDAP, pp 1\u20134 https:\/\/doi.org\/10.1109\/IDAP.2018.8620888. IEEE","DOI":"10.1109\/IDAP.2018.8620888"},{"key":"8397_CR13","doi-asserted-by":"publisher","unstructured":"Jeong J, Zou Y, Kim T, Zhang D, Ravichandran A, Dabeer O (2023) Winclip: Zero-\/few-shot anomaly classification and segmentation. In: CVPR, pp 19606\u201319616 https:\/\/doi.org\/10.1109\/CVPR52729.2023.01878","DOI":"10.1109\/CVPR52729.2023.01878"},{"key":"8397_CR14","doi-asserted-by":"publisher","unstructured":"Deng H, Zhang Z, Bao J, Li X (2023) Anovl: adapting vision-language models for unified zero-shot anomaly localization. https:\/\/doi.org\/10.48550\/ARXIV.2308.15939","DOI":"10.48550\/ARXIV.2308.15939"},{"key":"8397_CR15","doi-asserted-by":"publisher","unstructured":"Chen X, Han Y, Zhang J (2023) A zero-\/few-shot anomaly classification and segmentation method for CVPR 2023 VAND workshop challenge tracks 1&2: 1st place on zero-shot AD and 4th place on few-shot https:\/\/doi.org\/10.48550\/ARXIV.2305.17382","DOI":"10.48550\/ARXIV.2305.17382"},{"key":"8397_CR16","doi-asserted-by":"publisher","unstructured":"Zhou Q, Pang G, Tian Y, He S, Chen J (2024) alyclip: object-agnostic prompt learning for zero-shot anomaly detection. In: ICLR https:\/\/doi.org\/10.48550\/arXiv.2310.18961","DOI":"10.48550\/arXiv.2310.18961"},{"key":"8397_CR17","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton GE (2020) A simple framework for contrastive learning of visual representations. In: ICML, pp 1597\u20131607"},{"key":"8397_CR18","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I (2021) Learning transferable visual models from natural language supervision. In: ICML, pp 8748\u20138763 https:\/\/proceedings.mlr.press\/v139\/radford21a.html"},{"key":"8397_CR19","doi-asserted-by":"crossref","unstructured":"Fan L, Krishnan D, Isola P, Katabi D, Tian Y (2023) Improving CLIP training with language rewrites. In: NeurIPS. https:\/\/arxiv.org\/abs\/2305.20088","DOI":"10.52202\/075280-1544"},{"key":"8397_CR20","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: MICCA, pp 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8397_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2020.107404","volume":"126","author":"X Qin","year":"2020","unstructured":"Qin X, Zhang Z, Huang C, Dehghan M, Za\u00efane OR, J\u00e4gersand M (2020) U$$ ^{\\text{2 }}$$-net: going deeper with nested u-structure for salient object detection. Pattern Recogn 126:107404. https:\/\/doi.org\/10.1016\/J.PATCOG.2020.107404","journal-title":"Pattern Recogn"},{"key":"8397_CR22","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1177\/0040517520928604","volume":"92","author":"J Jing","year":"2022","unstructured":"Jing J, Wang Z, R\u00e4tsch M, Zhang H (2022) Mobile-unet: an efficient convolutional neural network for fabric defect detection. Text Res J 92:30\u201342. https:\/\/doi.org\/10.1177\/0040517520928604","journal-title":"Text Res J"},{"key":"8397_CR23","doi-asserted-by":"publisher","unstructured":"\u00dczen H, T\u00fcrkoglu M, Hanbay D (2021) Surface defect detection using deep u-net network architectures. In: SIU, pp 1\u20134. https:\/\/doi.org\/10.1109\/SIU53274.2021.9477790","DOI":"10.1109\/SIU53274.2021.9477790"},{"key":"8397_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENGAPPAI.2023.107094","volume":"126","author":"H Qu","year":"2023","unstructured":"Qu H, Di L, Liang J, Liu H (2023) U-SMR: u-swint & multi-residual network for fabric defect detection. Eng Appl Artif Intell 126:107094. https:\/\/doi.org\/10.1016\/J.ENGAPPAI.2023.107094","journal-title":"Eng Appl Artif Intell"},{"key":"8397_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/J.DISPLA.2021.102008","author":"Z Liu","year":"2021","unstructured":"Liu Z, Huo Z, Li C, Dong Y, Li B (2021) Dlse-net: a robust weakly supervised network for fabric defect detection. Displays. https:\/\/doi.org\/10.1016\/J.DISPLA.2021.102008","journal-title":"Displays"},{"key":"8397_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2959741","author":"J Liu","year":"2020","unstructured":"Liu J, Wang C, Su H, Du B, Tao D (2020) Multistage gan for fabric defect detection. IEEE Trans Image Process. https:\/\/doi.org\/10.1109\/TIP.2019.2959741","journal-title":"IEEE Trans Image Process"},{"key":"8397_CR27","doi-asserted-by":"publisher","unstructured":"Zhang Z, Wan X, Li L, Wang J (2021) An improved dcgan for fabric defect detection. In: ICECE, pp 72\u201376. https:\/\/doi.org\/10.1109\/ICECE54449.2021.9674302","DOI":"10.1109\/ICECE54449.2021.9674302"},{"key":"8397_CR28","doi-asserted-by":"crossref","unstructured":"Li C, Zhou S, Kong J, Qi L, Xue H (2025) Kanoclip: zero-shot anomaly detection through knowledge-driven prompt learning and enhanced cross-modal integration. In: ICASSP, pp 1\u20135. IEEE. https:\/\/arxiv.org\/abs\/2501.03786","DOI":"10.1109\/ICASSP49660.2025.10888834"},{"key":"8397_CR29","doi-asserted-by":"publisher","unstructured":"Zhang T, Gao L, Li X, Gao Y (2025) Dzad: diffusion-based zero-shot anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 39, pp 10131\u201310138. https:\/\/doi.org\/10.1609\/aaai.v39i10.33099","DOI":"10.1609\/aaai.v39i10.33099"},{"key":"8397_CR30","doi-asserted-by":"publisher","unstructured":"Shah M, Bhalgat Y (2024) Reproducibility study of CDUL: clip-driven unsupervised learning for multi-label image classification. https:\/\/doi.org\/10.48550\/ARXIV.2405.11574","DOI":"10.48550\/ARXIV.2405.11574"},{"key":"8397_CR31","doi-asserted-by":"publisher","unstructured":"Zhou Z, Lei Y, Zhang B, Liu L, Liu Y (2023) Zegclip: Towards adapting CLIP for zero-shot semantic segmentation. In: CVPR, pp. 11175\u201311185. https:\/\/doi.org\/10.1109\/CVPR52729.2023.01075","DOI":"10.1109\/CVPR52729.2023.01075"},{"key":"8397_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/biom14050590","author":"L Hua","year":"2024","unstructured":"Hua L, Luo Y, Qi Q, Long J (2024) Medicalclip: anomaly-detection domain generalization with asymmetric constraints. Biomolecules. https:\/\/doi.org\/10.3390\/biom14050590","journal-title":"Biomolecules"},{"key":"8397_CR33","doi-asserted-by":"publisher","unstructured":"L\u00fcddecke T, Ecker AS (2022) Image segmentation using text and image prompts. In: CVPR, pp 7076\u20137086. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00695","DOI":"10.1109\/CVPR52688.2022.00695"},{"key":"8397_CR34","doi-asserted-by":"publisher","unstructured":"Liang F, Wu B, Dai X, Li K, Zhao Y, Zhang H, Zhang P, Vajda P, Marculescu D (2023) Open-vocabulary semantic segmentation with mask-adapted CLIP. In: CVPR, pp 7061\u20137070. https:\/\/doi.org\/10.1109\/CVPR52729.2023.00682","DOI":"10.1109\/CVPR52729.2023.00682"},{"key":"8397_CR35","doi-asserted-by":"publisher","unstructured":"Zhang Z, Deng H, Bao J, Li X (2024) Dual-image enhanced CLIP for zero-shot anomaly detection. https:\/\/doi.org\/10.48550\/ARXIV.2405.04782","DOI":"10.48550\/ARXIV.2405.04782"},{"key":"8397_CR36","unstructured":"Tamura M (2023) Random word data augmentation with CLIP for zero-shot anomaly detection. In: BMVC, pp 18\u201321"},{"key":"8397_CR37","doi-asserted-by":"publisher","unstructured":"Zhang B, Zhang P, Dong X, Zang Y, Wang J (2024) Long-clip: unlocking the long-text capability of CLIP. CoRR https:\/\/doi.org\/10.48550\/ARXIV.2403.15378","DOI":"10.48550\/ARXIV.2403.15378"},{"issue":"3","key":"8397_CR38","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1109\/TAI.2021.3057027","volume":"1","author":"C Zhang","year":"2020","unstructured":"Zhang C, Feng S, Wang X, Wang Y (2020) Zju-leaper: a benchmark dataset for fabric defect detection and a comparative study. IEEE Trans Artif Intell 1(3):219\u2013232. https:\/\/doi.org\/10.1109\/TAI.2021.3057027","journal-title":"IEEE Trans Artif Intell"},{"issue":"4","key":"8397_CR39","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1007\/s11263-020-01400-4","volume":"129","author":"P Bergmann","year":"2021","unstructured":"Bergmann P, Batzner K, Fauser M, Sattlegger D, Steger C (2021) The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. Int J Comput Vision 129(4):1038\u20131059. https:\/\/doi.org\/10.1007\/s11263-020-01400-4","journal-title":"Int J Comput Vision"},{"key":"8397_CR40","doi-asserted-by":"publisher","unstructured":"Bergmann P, Fauser M, Sattlegger D, Steger C (2020) Uninformed students: student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4183\u20134192 https:\/\/doi.org\/10.1109\/CVPR42600.2020.00424","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"8397_CR41","unstructured":"Ngan HYT Databases sharing: patterned textures. https:\/\/ytngan.wordpress.com\/codes\/"},{"key":"8397_CR42","doi-asserted-by":"crossref","unstructured":"Liu Z, Zhou Y, Xu Y, Wang Z (2023) Simplenet: a simple network for image anomaly detection and localization. In: CVPR","DOI":"10.1109\/CVPR52729.2023.01954"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08397-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08397-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08397-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:53:35Z","timestamp":1773716015000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08397-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,17]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["8397"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08397-6","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,17]]},"assertion":[{"value":"26 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"263"}}