{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:47:20Z","timestamp":1775890040044,"version":"3.50.1"},"reference-count":314,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62276256, U2441251"],"award-info":[{"award-number":["62276256, U2441251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Young Elite Scientists Sponsorship Program by CAST","award":["2023QNRC001"],"award-info":[{"award-number":["2023QNRC001"]}]},{"name":"Young Scientists Fund of the State Key Laboratory of Multimodal Artificial Intelligence Systems","award":["ES2P100117"],"award-info":[{"award-number":["ES2P100117"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n            Out-of-distribution (OOD) detection aims at detecting test samples outside the training category space, which is an essential component in building reliable machine learning systems. Existing reviews on OOD detection primarily focus on method taxonomy, surveying the field by categorizing various approaches. However, many recent works concentrate on non-traditional OOD detection scenarios, such as test-time adaptation, multi-modal data sources and other novel contexts. In this survey, we uniquely review recent advances in OOD detection from the task-oriented perspective for the first time. According to the user\u2019s access to the model, that is, whether the OOD detection method is allowed to modify or retrain the model, we classify the methods as training-driven or training-agnostic. Besides, considering the rapid development of pre-trained models, large pre-trained model-based OOD detection is also regarded as an important category and discussed separately. Furthermore, we provide a discussion of the evaluation scenarios, a variety of applications, and several future research directions. We believe this survey with new taxonomy will benefit the proposal of new methods and the expansion of more practical scenarios. A curated list of related articles is provided in the Github repository:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/shuolucs\/Awesome-Out-Of-Distribution-Detection\">https:\/\/github.com\/shuolucs\/Awesome-Out-Of-Distribution-Detection<\/jats:ext-link>\n          <\/jats:p>","DOI":"10.1145\/3760390","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T11:26:16Z","timestamp":1754911576000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7547-3169","authenticated-orcid":false,"given":"Shuo","family":"Lu","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences Institute of Automation","place":["Beijing, China"]},{"name":"University of the Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6580-1302","authenticated-orcid":false,"given":"Yingsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui University","place":["Hefei, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8240-9736","authenticated-orcid":false,"given":"Lijun","family":"Sheng","sequence":"additional","affiliation":[{"name":"Department of Automation, University of Science and Technology of China","place":["Hefei, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0861-8177","authenticated-orcid":false,"given":"Lingxiao","family":"He","sequence":"additional","affiliation":[{"name":"Meituan","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9820-4743","authenticated-orcid":false,"given":"Aihua","family":"Zheng","sequence":"additional","affiliation":[{"name":"Anhui University","place":["Hefei, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3890-1894","authenticated-orcid":false,"given":"Jian","family":"Liang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences Institute of Automation","place":["Beijing, China"]},{"name":"University of the Chinese Academy of Sciences","place":["Beijing, China"]}]}],"member":"320","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hendrycks Dan","year":"2017","unstructured":"Dan Hendrycks and Kevin Gimpel. 2017. 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