{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:34:43Z","timestamp":1784003683521,"version":"3.55.0"},"reference-count":78,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0102200"],"award-info":[{"award-number":["2018AAA0102200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62122018"],"award-info":[{"award-number":["62122018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2097"],"award-info":[{"award-number":["U22A2097"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62020106008"],"award-info":[{"award-number":["62020106008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872064"],"award-info":[{"award-number":["61872064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004806","name":"Fok Ying Tong Education Foundation","doi-asserted-by":"publisher","award":["171106"],"award-info":[{"award-number":["171106"]}],"id":[{"id":"10.13039\/501100004806","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SongShan Laboratory","award":["YYJC012022019"],"award-info":[{"award-number":["YYJC012022019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tip.2023.3328478","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T18:17:56Z","timestamp":1699035476000},"page":"6115-6128","source":"Crossref","is-referenced-by-count":36,"title":["From Global to Local: Multi-Scale Out-of-Distribution Detection"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6949-3673","authenticated-orcid":false,"given":"Ji","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianli","family":"Gao","sequence":"additional","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingguang","family":"Hao","sequence":"additional","affiliation":[{"name":"Yingcai Honors College, University of Electronic Science and Technology of China (UESTC), Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Huang","sequence":"additional","affiliation":[{"name":"Kuaishou Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2549-8322","authenticated-orcid":false,"given":"Jingkuan","family":"Song","sequence":"additional","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2999-2088","authenticated-orcid":false,"given":"Hengtao","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref57","first-page":"1","article-title":"VOS: Learning what you don’t know by virtual outlier synthesis","author":"du","year":"2022","journal-title":"Proc ICML"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/83.650858"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3093784"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2938758"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587598"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2754942"},{"key":"ref58","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"khosla","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713048"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3135248"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3082765"},{"key":"ref11","first-page":"11839","article-title":"CSI: Novelty detection via contrastive learning on distributionally shifted instances","volume":"33","author":"tack","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3025062"},{"key":"ref10","first-page":"1","article-title":"SSD: A unified framework for self-supervised outlier detection","author":"sehwag","year":"2021","journal-title":"Proc ICLR"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2670780"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3062192"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3129321"},{"key":"ref18","first-page":"4167","article-title":"Adversarially robust one-class novelty detection","volume":"45","author":"lo","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref51","first-page":"1","article-title":"An image is worth 16 × 16 words: Transformers for image recognition at scale","author":"dosovitskiy","year":"2020","journal-title":"Proc ICLR"},{"key":"ref50","article-title":"DETA: Denoised task adaptation for few-shot learning","author":"zhang","year":"2023","journal-title":"arXiv 2303 06315"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3065254"},{"key":"ref45","first-page":"1","article-title":"CDTrans: Cross-domain transformer for unsupervised domain adaptation","author":"xu","year":"2021","journal-title":"Proc ICLR"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01521"},{"key":"ref47","first-page":"201","article-title":"Stacked cross attention for image-text matching","author":"lee","year":"2018","journal-title":"Proc ECCV"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00450"},{"key":"ref41","first-page":"5","article-title":"Towards unknown-aware learning with virtual outlier synthesis","volume":"1","author":"du","year":"2022","journal-title":"Proc ICLR"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00948"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.73"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref8","first-page":"427","article-title":"Deep neural networks are easily fooled: High confidence predictions for unrecognizable images","author":"nguyen","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref7","first-page":"1","article-title":"A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: Solutions and future challenges","author":"salehi","year":"2021","journal-title":"Proc Trans Mach Learn Res"},{"key":"ref9","first-page":"20827","article-title":"Out-of-distribution detection with deep nearest neighbors","author":"sun","year":"2022","journal-title":"Proc ICML"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref3","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"liu","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16829"},{"key":"ref5","first-page":"1","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","author":"liang","year":"2018","journal-title":"Proc ICLR"},{"key":"ref40","first-page":"15650","article-title":"POEM: Out-of-distribution detection with posterior sampling","author":"ming","year":"2022","journal-title":"Proc ICML"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3268069"},{"key":"ref78","first-page":"8491","article-title":"Detecting out-of-distribution examples with Gram matrices","author":"sastry","year":"2020","journal-title":"Proc ICML"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3219237"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.352"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3205212"},{"key":"ref75","first-page":"1","article-title":"In or out? Fixing ImageNet out-of-distribution detection evaluation","author":"bitterwolf","year":"2022","journal-title":"Proc ICML"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01434"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539970"},{"key":"ref30","first-page":"9239","article-title":"Towards maximizing the representation gap between in-domain & out-of-distribution examples","volume":"33","author":"nandy","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref77","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547835"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3164190"},{"key":"ref2","first-page":"1","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume":"31","author":"lee","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref1","first-page":"1","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"hendrycks","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref39","first-page":"7068","article-title":"Exploring the limits of out-of-distribution detection","volume":"34","author":"fort","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref38","first-page":"1","article-title":"Open-set recognition: A good closed-set classifier is all you need?","author":"vaze","year":"2022","journal-title":"Proc ICLR"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref70","article-title":"TurkerGaze: Crowdsourcing saliency with webcam based eye tracking","author":"xu","year":"2015","journal-title":"arXiv 1504 06755"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00914"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00860"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"ref24","article-title":"A simple fix to Mahalanobis distance for improving near-OOD detection","author":"ren","year":"2021","journal-title":"arXiv 2106 09022"},{"key":"ref67","first-page":"1","article-title":"Reading digits in natural images with unsupervised feature learning","author":"netzer","year":"2011","journal-title":"Proc NIPS Workshop Deep Learn Unsupervised Feature Learn"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01901"},{"key":"ref26","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"radford","year":"2021","journal-title":"Proc ICML"},{"key":"ref69","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","author":"yu","year":"2015","journal-title":"arXiv 1506 03365"},{"key":"ref25","first-page":"1","article-title":"How to exploit hyperspherical embeddings for out-of-distribution detection?","author":"ming","year":"2023","journal-title":"Proc ICLR"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298799"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00330"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_40"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3112897"},{"key":"ref21","first-page":"23631","article-title":"Mitigating neural network overconfidence with Logit Normalization","author":"wei","year":"2022","journal-title":"Proc ICML"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00743"},{"key":"ref27","first-page":"35087","article-title":"Delving into out-of-distribution detection with vision-language representations","volume":"35","author":"ming","year":"2022","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00304"},{"key":"ref62","first-page":"1","article-title":"Contrastive training for improved out-of-distribution detection","author":"winkens","year":"2021","journal-title":"Proc EMNLP"},{"key":"ref61","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9991910\/10308720.pdf?arnumber=10308720","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T21:08:42Z","timestamp":1743800922000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10308720\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":78,"URL":"https:\/\/doi.org\/10.1109\/tip.2023.3328478","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}