{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:14Z","timestamp":1761176114102,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Zero-shot anomaly detection (ZSAD) aims to identify anomalies of unseen classes without requiring samples from those classes. Existing methods typically rely on pre-trained visual language models, such as CLIP, to detect anomalies by designing or learning generic text prompts and computing similarities with image features, which often fail to address the complexity and novelty of anomaly patterns, especially when the target domain exhibits significant differences from the source domain. To address the problems, we propose Region-aware Compositional Context Prompting (ReCo-CoP) for ZSAD, which dynamically generates contextual prompts by integrating both global and local visual information. Specifically, we introduce a Compositional Context Prompting (CCP) module that incorporates global visual features into the context through a set of basis vectors shared among images, and a Regional Context Prompting (RCP) module that optimizes the context based on image patch features, thereby enhancing the model\u2019s ability to perceive local abnormal regions. Additionally, we combine dynamically generated prompts with static generic prompts to prevent the model from losing the essential general knowledge. Extensive experiments on 12 datasets from industrial and medical domains demonstrate the superior zero-shot detection performance of our model. The code is available at https:\/\/github.com\/WenDongyp\/ReCoCoP<\/jats:p>","DOI":"10.3233\/faia250792","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:22Z","timestamp":1761126142000},"source":"Crossref","is-referenced-by-count":0,"title":["Region-Aware Compositional Context Prompting for Zero-Shot Anomaly Detection"],"prefix":"10.3233","author":[{"given":"Wen","family":"Dong","sequence":"first","affiliation":[{"name":"Nanjing University"}]},{"given":"Guanglei","family":"Chu","sequence":"additional","affiliation":[{"name":"China Mobile (Suzhou) Software Technology Co., Ltd."}]},{"given":"Zhe","family":"Pan","sequence":"additional","affiliation":[{"name":"China Mobile (Suzhou) Software Technology Co., Ltd."}]},{"given":"Guo-Sen","family":"Xie","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology"}]},{"given":"Caifeng","family":"Shan","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Fang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Nanjing University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250792","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:22Z","timestamp":1761126142000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250792","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}