{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T00:01:01Z","timestamp":1793404861381,"version":"4.5.0"},"reference-count":173,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"NUS ODPRT","award":["A-0008067-00-00"],"award-info":[{"award-number":["A-0008067-00-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,9,1]]},"DOI":"10.1109\/tpami.2023.3268118","type":"journal-article","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T13:27:28Z","timestamp":1681910848000},"page":"10795-10816","source":"Crossref","is-referenced-by-count":655,"title":["Deep Long-Tailed Learning: A Survey"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2125-1074","authenticated-orcid":false,"given":"Yifan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6014-719X","authenticated-orcid":false,"given":"Bingyi","family":"Kang","sequence":"additional","affiliation":[{"name":"ByteDance AI Lab, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5645-1754","authenticated-orcid":false,"given":"Bryan","family":"Hooi","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8906-3777","authenticated-orcid":false,"given":"Shuicheng","family":"Yan","sequence":"additional","affiliation":[{"name":"SEA AI Lab, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-0064","authenticated-orcid":false,"given":"Jiashi","family":"Feng","sequence":"additional","affiliation":[{"name":"ByteDance AI Lab, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00265"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01100"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413970"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00344"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87240-3_31"},{"key":"ref52","article-title":"Rail-5k: A real-world dataset for rail surface defects detection","author":"zhang","year":"2021"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16246"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_25"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref169","first-page":"8184","article-title":"AdaXpert: Adapting neural architecture for growing data","author":"niu","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref170","article-title":"COVID-DA: Deep domain adaptation from typical pneumonia to COVID-19","author":"zhang","year":"2020"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01036"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.205"},{"key":"ref173","first-page":"11842","article-title":"Delving into deep imbalanced regression","author":"yang","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref172","article-title":"Heteroskedastic and imbalanced deep learning with adaptive regularization","author":"cao","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01484"},{"key":"ref45","first-page":"1513","article-title":"Long-tailed classification by keeping the good and removing the bad momentum causal effect","author":"tang","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00800"},{"key":"ref47","article-title":"Fine-grained visual classification of plant species in the wild: Object detection as a reinforced means of attention","author":"keaton","year":"2021"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref41","first-page":"487","article-title":"Learning deep features for scene recognition using places database","volume":"27","author":"zhou","year":"2014","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"ref43","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"lin","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00304"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3448250"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7068349"},{"key":"ref6","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2982166"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01391"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_41"},{"key":"ref40","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00263"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_43"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58548-8_10"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00550"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00656"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref30","first-page":"34077","article-title":"Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition","volume":"35","author":"zhang","year":"2022","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref149","article-title":"Unsupervised representation learning by predicting image rotations","author":"gidaris","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00340"},{"key":"ref146","first-page":"3833","article-title":"Rethinking pre-training and self-training","volume":"33","author":"zoph","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","article-title":"Decoupling representation and classifier for long-tailed recognition","author":"kang","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref147","first-page":"29848","article-title":"Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning","author":"zhang","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00786"},{"key":"ref155","article-title":"Robust long-tailed learning under label noise","author":"wei","year":"2021"},{"key":"ref156","article-title":"The effectiveness of data augmentation in image classification using deep learning","author":"perez","year":"2017"},{"key":"ref153","first-page":"1530","article-title":"Semi-supervised learning literature survey","author":"zhu","year":"2005"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"ref151","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref150","article-title":"Learning from long-tailed data with noisy labels","author":"karthik","year":"2021"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00393-0"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00914"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00685"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_1"},{"key":"ref20","first-page":"831","article-title":"Principles of risk minimization for learning theory","author":"vapnik","year":"1992","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1007\/11538059_91"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00571"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.578"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00763"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00330"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref167","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-IID data","author":"luo","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref164","article-title":"Identifying and compensating for feature deviation in imbalanced deep learning","author":"ye","year":"2020"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"ref162","article-title":"In defense of the triplet loss for person re-identification","author":"hermans","year":"2017"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1090.0795"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-65414-6_9"},{"key":"ref161","first-page":"12635","article-title":"Implicit semantic data augmentation for deep networks","volume":"32","author":"wang","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref13","article-title":"Exploring balanced feature spaces for representation learning","author":"kang","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/21.229466"},{"key":"ref14","article-title":"Long-tail learning via logit adjustment","author":"menon","year":"2021","journal-title":"Proc Int Conf on Learn Representations"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00076"},{"key":"ref97","first-page":"4175","article-title":"Balanced meta-softmax for long-tailed visual recognition","volume":"33","author":"jiawei","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00585"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1111\/j.0824-7935.2004.t01-1-00228.x"},{"key":"ref11","first-page":"2553","article-title":"Deep neural networks for object detection","author":"szegedy","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref99","first-page":"8101","article-title":"Posterior re-calibration for imbalanced datasets","volume":"33","author":"tian","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00018"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01406"},{"key":"ref125","first-page":"3695","article-title":"ResLT: Residual learning for long-tailed recognition","volume":"45","author":"cui","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref17","article-title":"Long-tailed recognition by routing diverse distribution-aware experts","author":"wang","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"ref18","first-page":"1567","article-title":"Learning imbalanced datasets with label-distribution-aware margin loss","author":"cao","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00512"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.17"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00432"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2826011"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00019"},{"key":"ref131","first-page":"5149","article-title":"Meta-learning in neural networks: A survey","volume":"44","author":"hospedales","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref94","first-page":"1917","article-title":"Meta-weight-net: Learning an explicit mapping for sample weighting","author":"shu","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref132","first-page":"973","article-title":"The foundations of cost-sensitive learning","author":"elkan","year":"2001","journal-title":"Proc Int Joint Conf Artif Intell"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_12"},{"key":"ref91","first-page":"7029","article-title":"Learning to model the tail","author":"wang","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.100"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.580"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1015362183"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2007.04.009"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2018.2822810"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2922175"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219948"},{"key":"ref87","first-page":"613","article-title":"Open set learning with counterfactual images","author":"neal","year":"2018","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2955078"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3006377"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00502"},{"key":"ref81","first-page":"360","article-title":"From whole slide imaging to microscopy: Deep microscopy adaptation network for histopathology cancer image classification","author":"zhang","year":"2019","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Interv"},{"key":"ref145","first-page":"2712","article-title":"Using pre-training can improve model robustness and uncertainty","author":"hendrycks","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3094137"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/402"},{"key":"ref143","first-page":"201","article-title":"Why does unsupervised pre-training help deep learning?","author":"erhan","year":"2010","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref140","first-page":"1","article-title":"Machine learning from imbalanced data sets 101","author":"provost","year":"2000","journal-title":"Proc AAAI Workshop Imbalanced Data Sets"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00173"},{"key":"ref78","article-title":"Towards out-of-distribution generalization: A survey","author":"shen","year":"2021"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00957"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00810"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00440"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_11"},{"key":"ref77","first-page":"5815","article-title":"Out-of-distribution generalization via risk extrapolation","author":"krueger","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref102","first-page":"19290","article-title":"Rethinking the value of labels for improving class-imbalanced learning","author":"yang","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_15"},{"key":"ref2","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/BF00130487"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00077"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"ref112","first-page":"18970","article-title":"Label-imbalanced and group-sensitive classification under overparameterization","author":"kini","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref73","first-page":"4080","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00312"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1214\/aop\/1024404422"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00067"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.415"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00047"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00378"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01464"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00356"},{"key":"ref63","article-title":"RelTransformer: Balancing the visual relationship detection from local context, scene and memory","author":"chen","year":"2021"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00030"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247847"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00855"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539955"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01622"},{"key":"ref60","article-title":"One million scenes for autonomous driving: Once dataset","author":"mao","year":"2021","journal-title":"Proc Neural Inf Process Syst Datasets Benchmarks"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00936"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00244"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00517"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01512"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00075"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10210213\/10105457.pdf?arnumber=10105457","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T13:59:01Z","timestamp":1693231141000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10105457\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":173,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3268118","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,1]]}}}