{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T06:11:51Z","timestamp":1777183911956,"version":"3.51.4"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271034"],"award-info":[{"award-number":["62271034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1109\/tcsvt.2023.3321733","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T17:47:26Z","timestamp":1696441646000},"page":"3806-3818","source":"Crossref","is-referenced-by-count":8,"title":["OHD: An Online Category-Aware Framework for Learning With Noisy Labels Under Long-Tailed Distribution"],"prefix":"10.1109","volume":"34","author":[{"given":"Qihao","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2058-2373","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology and the Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5922-9358","authenticated-orcid":false,"given":"Songhe","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Tampines, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"The Mnist Database of Handwritten Digits","author":"LeCun","year":"1998"},{"key":"ref2","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"ref4","article-title":"WebVision database: Visual learning and understanding from web data","author":"Li","year":"2017","journal-title":"arXiv:1708.02862"},{"key":"ref5","first-page":"1","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. NIPS","author":"Han"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"ref7","first-page":"1","article-title":"Learning to reweight examples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ren"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00342"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/icpr48806.2021.9412550"},{"key":"ref10","first-page":"7365","article-title":"MetaCleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","author":"Yisen"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"ref12","first-page":"872","article-title":"What is the effect of importance weighting in deep learning?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Byrd"},{"key":"ref13","article-title":"Learning imbalanced datasets with label-distribution-aware margin loss","author":"Cao","year":"2019","journal-title":"arXiv:1906.07413"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref15","article-title":"Balanced meta-softmax for long-tailed visual recognition","author":"Ren","year":"2020","journal-title":"arXiv:2007.10740"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3264231"},{"key":"ref17","article-title":"Heteroskedastic and imbalanced deep learning with adaptive regularization","author":"Cao","year":"2020","journal-title":"arXiv:2006.15766"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20654"},{"key":"ref19","article-title":"Learning from long-tailed data with noisy labels","author":"Karthik","year":"2021","journal-title":"arXiv:2108.11096"},{"key":"ref20","article-title":"Intriguing properties of adversarial training at scale","author":"Xie","year":"2019","journal-title":"arXiv:1906.03787"},{"key":"ref21","article-title":"Long-tail learning via logit adjustment","author":"Menon","year":"2020","journal-title":"arXiv:2007.07314"},{"key":"ref22","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","author":"Li","year":"2020","journal-title":"arXiv:2002.07394"},{"key":"ref23","article-title":"Early-learning regularization prevents memorization of noisy labels","author":"Liu","year":"2020","journal-title":"arXiv:2007.00151"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"ref25","first-page":"1196","article-title":"Learning with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Natarajan"},{"key":"ref26","first-page":"3355","article-title":"Dimensionality-driven learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref27","first-page":"1","article-title":"A closer look at memorization in deep networks","volume-title":"Proc. ICML","author":"Arpit"},{"key":"ref28","first-page":"1","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. ICML","author":"Yu"},{"key":"ref29","first-page":"1","article-title":"Understanding and utilizing deep neural networks trained with noisy labels","volume-title":"Proc. ICML","author":"Chen"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.07.024"},{"key":"ref31","article-title":"Identifying mislabeled data using the area under the margin ranking","author":"Pleiss","year":"2020","journal-title":"arXiv:2001.10528"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2983600"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3073021"},{"key":"ref34","first-page":"1","article-title":"Robust early-learning: Hindering the memorization of noisy labels","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xia"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3231887"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2002-6504"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2732482"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3122110"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3161427"},{"key":"ref42","article-title":"Long-tailed recognition by routing diverse distribution-aware experts","author":"Wang","year":"2020","journal-title":"arXiv:2010.01809"},{"key":"ref43","article-title":"Decoupling representation and classifier for long-tailed recognition","author":"Kang","year":"2019","journal-title":"arXiv:1910.09217"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.578"},{"key":"ref46","first-page":"7032","article-title":"Learning to model the tail","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01391"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00918"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2005.10.028"},{"key":"ref50","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"ref52","article-title":"The iNaturalist challenge 2017 dataset","author":"Horn","year":"2017","journal-title":"arxiv:1707.06642"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref54","article-title":"Robust long-tailed learning under label noise","author":"Wei","year":"2021","journal-title":"arXiv:2108.11569"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00075"},{"key":"ref58","first-page":"960","article-title":"Decoupling \u2018when to update\u2019 from \u2018how to update","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Malach"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00041"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00906"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00013"},{"key":"ref62","article-title":"Learning with feature-dependent label noise: A progressive approach","author":"Zhang","year":"2021","journal-title":"arXiv:2103.07756"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.580"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20056-4_30"},{"key":"ref65","first-page":"2309","article-title":"Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jiang"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/76\/10527423\/10271714.pdf?arnumber=10271714","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T20:11:58Z","timestamp":1736453518000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10271714\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":65,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2023.3321733","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5]]}}}