{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T03:11:49Z","timestamp":1758597109401,"version":"3.44.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"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":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04636-0","type":"journal-article","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T10:46:13Z","timestamp":1757414773000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLOSAM: A unified and efficient anomaly detection model based on auto mask prompt"],"prefix":"10.1007","volume":"19","author":[{"given":"Ruizhi","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheming","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"issue":"10","key":"4636_CR1","doi-asserted-by":"publisher","first-page":"7405","DOI":"10.1007\/s11760-024-03403-x","volume":"18","author":"J Si","year":"2024","unstructured":"Si, J., Kim, S.: V-daft: Visual technique for texture image defect recognition with denoising autoencoder and fourier transform. SIViP 18(10), 7405\u20137418 (2024)","journal-title":"SIViP"},{"issue":"1","key":"4636_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11760-024-03608-0","volume":"19","author":"X Liang","year":"2025","unstructured":"Liang, X., Chen, Y.: Masked feature reconstruction distillation for unsupervised anomaly detection. SIViP 19(1), 1\u201312 (2025)","journal-title":"SIViP"},{"issue":"8","key":"4636_CR3","doi-asserted-by":"publisher","first-page":"6339","DOI":"10.1007\/s11760-024-03320-z","volume":"18","author":"J Qin","year":"2024","unstructured":"Qin, J., Gu, C., Yu, J., Zhang, C.: Multilevel saliency-guided self-supervised learning for image anomaly detection. SIViP 18(8), 6339\u20136351 (2024)","journal-title":"SIViP"},{"key":"4636_CR4","unstructured":"Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357 (2020)"},{"key":"4636_CR5","doi-asserted-by":"crossref","unstructured":"Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: International Conference on Pattern Recognition, pp. 475\u2013489 (2021). Springer","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"4636_CR6","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Cao, Y., Shen, W.: Prototypical learning guided context-aware segmentation network for few-shot anomaly detection. IEEE Transactions on Neural Networks and Learning Systems (2024)","DOI":"10.1109\/TNNLS.2024.3463495"},{"key":"4636_CR7","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., : Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763 (2021). PmLR"},{"key":"4636_CR8","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., : Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"4636_CR9","doi-asserted-by":"crossref","unstructured":"Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., Dabeer, O.: Winclip: Zero-\/few-shot anomaly classification and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19606\u201319616 (2023)","DOI":"10.1109\/CVPR52729.2023.01878"},{"key":"4636_CR10","unstructured":"Yan, Z., Fang, Q., Lv, W., Su, Q.: Anomalysd: One-for-all few-shot anomaly detection via pre-trained diffusion models. Available at SSRN 5266814"},{"key":"4636_CR11","doi-asserted-by":"crossref","unstructured":"Damm, S., Laszkiewicz, M., Lederer, J., Fischer, A.: Anomalydino: Boosting patch-based few-shot anomaly detection with dinov2. arXiv preprint arXiv:2405.14529 (2024)","DOI":"10.1109\/WACV61041.2025.00136"},{"key":"4636_CR12","doi-asserted-by":"crossref","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch\u00f6lkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2022)","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"4636_CR13","unstructured":"Chen, X., Han, Y., Zhang, J.: 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 ad. arXiv preprint arXiv:2305.173822(4) (2023)"},{"issue":"3","key":"4636_CR14","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1162\/dint_a_00144","volume":"4","author":"Q Lin","year":"2022","unstructured":"Lin, Q., Liu, Y., Wen, W., Tao, Z., Ouyang, C., Wan, Y.: Ensemble making few-shot learning stronger. Data Intelligence 4(3), 529\u2013551 (2022)","journal-title":"Data Intelligence"},{"key":"4636_CR15","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al.: Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"4636_CR16","unstructured":"Zhou, J., Wei, C., Wang, H., Shen, W., Xie, C., Yuille, A., Kong, T.: ibot: Image bert pre-training with online tokenizer. arXiv preprint arXiv:2111.07832 (2021)"},{"key":"4636_CR17","doi-asserted-by":"crossref","unstructured":"Ji, W., Li, J., Bi, Q., Liu, T., Li, W., Cheng, L.: Segment anything is not always perfect: An investigation of sam on different real-world applications. Springer (2024)","DOI":"10.1007\/s11633-023-1385-0"},{"key":"4636_CR18","doi-asserted-by":"publisher","first-page":"171214","DOI":"10.1109\/ACCESS.2019.2953727","volume":"7","author":"R Zhao","year":"2019","unstructured":"Zhao, R., Chen, W., Cao, G.: Edge-boosted u-net for 2d medical image segmentation. IEEE Access 7, 171214\u2013171222 (2019)","journal-title":"IEEE Access"},{"key":"4636_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, J., Pang, G.: Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17826\u201317836 (2024)","DOI":"10.1109\/CVPR52733.2024.01688"},{"issue":"3","key":"4636_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cja.2024.06.007","volume":"38","author":"Z Hu","year":"2025","unstructured":"Hu, Z., Zeng, X., Li, Y., Yin, Z., Meng, E., Zhu, L., Kong, X.: Few-shot anomaly detection with adaptive feature transformation and descriptor construction. Chin. J. Aeronaut. 38(3), 103098 (2025). https:\/\/doi.org\/10.1016\/j.cja.2024.06.007","journal-title":"Chin. J. Aeronaut."},{"issue":"4","key":"4636_CR21","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.: The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. Int. J. Comput. Vision 129(4), 1038\u20131059 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"4636_CR22","doi-asserted-by":"crossref","unstructured":"Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. In: European Conference on Computer Vision, pp. 392\u2013408 (2022). Springer","DOI":"10.1007\/978-3-031-20056-4_23"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04636-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04636-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04636-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T13:16:22Z","timestamp":1758546982000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04636-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"references-count":22,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4636"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04636-0","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,9,9]]},"assertion":[{"value":"22 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1055"}}