{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T17:51:24Z","timestamp":1780509084709,"version":"3.54.1"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62273296"],"award-info":[{"award-number":["62273296"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hebei Innovation Capability Improvement Plan Project","award":["22567619H"],"award-info":[{"award-number":["22567619H"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tase.2025.3552986","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T16:07:11Z","timestamp":1742400431000},"page":"3433-3444","source":"Crossref","is-referenced-by-count":7,"title":["G-Anomaly: A Pyramid Graph Transformer-Based Vision-Language Model for General Industrial Anomaly Detection"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9013-2695","authenticated-orcid":false,"given":"Jiaqi","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7646-4958","authenticated-orcid":false,"given":"Shuhuan","family":"Wen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9149-7336","authenticated-orcid":false,"given":"Bin","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2023.3322156"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2023.3288111"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3280337"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2020.2964289"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2945403"},{"key":"ref6","first-page":"1","article-title":"U-GAT-IT: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kim"},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107646","article-title":"Tackling mode collapse in multi-generator GANs with orthogonal vectors","volume":"110","author":"Li","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref8","article-title":"FastFlow: Unsupervised anomaly detection and localization via 2D normalizing flows","author":"Yu","year":"2021","journal-title":"arXiv:2111.07677"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01359"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3512548"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01878"},{"key":"ref12","article-title":"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","volume-title":"arXiv:2305.17382","author":"Chen"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01594"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2021.3129247"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3441638"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01081"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3152247"},{"key":"ref18","first-page":"1","article-title":"AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Zhou"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2024.3376427"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3350545"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204236"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3074057"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3271623"},{"key":"ref24","first-page":"9266","article-title":"DeepGCNs: Can GCNs go as deep as CNNs?","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV)","author":"Li"},{"key":"ref25","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref28","first-page":"392","article-title":"SPot-the-difference self-supervised pre-training for anomaly detection and segmentation","volume-title":"Proc. Eur. Conf. Comput. Vis.","author":"Zou"},{"key":"ref29","first-page":"1287","article-title":"A benchmark for visual identification of defective solar cells in electroluminescence imagery","volume-title":"Proc. Eur. PV Solar Energy Conf. Exhib. (EU PVSEC)","author":"Buerhop-Lutz","year":"2018"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_18"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3004397"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8856\/11323516\/10933971.pdf?arnumber=10933971","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T06:16:58Z","timestamp":1770877018000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10933971\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/tase.2025.3552986","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"value":"1545-5955","type":"print"},{"value":"1558-3783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}