{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:13:34Z","timestamp":1780053214532,"version":"3.54.0"},"reference-count":81,"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tip.2023.3312910","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T18:28:35Z","timestamp":1697740115000},"page":"6195-6209","source":"Crossref","is-referenced-by-count":2,"title":["Evolving Into a Transformer: From a Training-Free Retrieval-Based Method for Anomaly Obstacle Segmentation"],"prefix":"10.1109","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1650-7167","authenticated-orcid":false,"given":"Yongjian","family":"Fu","sequence":"first","affiliation":[{"name":"College of Computer Science, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1024-4124","authenticated-orcid":false,"given":"Dingli","family":"Gao","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dayang","family":"Hao","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00906"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00374"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58586-0_17"},{"key":"ref4","first-page":"1","article-title":"Faster R-CNN: Towards realtime object detection with region proposal networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ren"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.155"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.198"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.699"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00214"},{"key":"ref9","article-title":"Detecting road obstacles by erasing them","author":"Lis","year":"2020","journal-title":"arXiv:2012.13633"},{"key":"ref10","article-title":"SegmentMeIfYouCan: A benchmark for anomaly segmentation","author":"Chan","year":"2021","journal-title":"arXiv:2104.14812"},{"key":"ref11","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"Hendrycks","year":"2016","journal-title":"arXiv:1610.02136"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00224"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01664"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_9"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00508"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00294"},{"key":"ref17","article-title":"Dense outlier detection and open-set recognition based on training with noisy negative images","author":"Bevandi\u0107","year":"2021","journal-title":"arXiv:2101.09193"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01514"},{"key":"ref19","first-page":"1","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Kendall"},{"key":"ref20","first-page":"1","article-title":"A simple baseline for Bayesian uncertainty in deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Maddox"},{"key":"ref21","article-title":"Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning","author":"Papernot","year":"2018","journal-title":"arXiv:1803.04765"},{"key":"ref22","first-page":"1","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Lee"},{"key":"ref23","article-title":"Distance-based confidence score for neural network classifiers","author":"Mandelbaum","year":"2017","journal-title":"arXiv:1709.09844"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206659"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref26","first-page":"1","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lakshminarayanan"},{"key":"ref27","article-title":"Scaling out-of-distribution detection for real-world settings","author":"Hendrycks","year":"2019","journal-title":"arXiv:1911.11132"},{"key":"ref28","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Liu"},{"key":"ref29","first-page":"1","article-title":"Enhancing the reliability of out-ofdistribution image detection in neural networks","volume-title":"Proc. 6th Int. Conf. Learn. Represent.","author":"Liang"},{"key":"ref30","first-page":"1","article-title":"Training confidence-calibrated classifiers for detecting out-of-distribution samples","volume-title":"Proc. ICLR","author":"Lee"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00356"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00218"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01541"},{"key":"ref34","article-title":"Dense open-set recognition with synthetic outliers generated by real NVP","author":"Grci\u0107","year":"2020","journal-title":"arXiv:2011.11094"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.888350"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3138302"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2005.863105"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3142530"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3148814"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3137660"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.906769"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.881946"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00970"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00957"},{"key":"ref46","article-title":"VL-BERT: Pre-training of generic visual-linguistic representations","author":"Su","year":"2019","journal-title":"arXiv:1908.08530"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1514"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7005"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.382970"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995373"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88682-2_24"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383172"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2007.4408891"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248018"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1285"},{"key":"ref56","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref58","first-page":"1","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yang"},{"key":"ref59","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"key":"ref60","first-page":"10347","article-title":"Training data-efficient image transformers&distillation through attention","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Touvron"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3162964"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3148867"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3135477"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref65","article-title":"Deformable DETR: Deformable transformers for end-to-end object detection","author":"Zhu","year":"2020","journal-title":"arXiv:2010.04159"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01075"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.00681"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3161832"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1604.01685"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.534"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2016.7759186"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.02611"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref74","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref75","article-title":"Evaluating Bayesian deep learning methods for semantic segmentation","author":"Mukhoti","year":"2018","journal-title":"arXiv:1811.12709"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01536"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19806-9_29"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19842-7_15"},{"issue":"11","key":"ref79","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9991910\/10288331.pdf?arnumber=10288331","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T10:02:54Z","timestamp":1709373774000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10288331\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":81,"URL":"https:\/\/doi.org\/10.1109\/tip.2023.3312910","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}