{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T17:51:02Z","timestamp":1781977862550,"version":"3.54.5"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["62102182"],"award-info":[{"award-number":["62102182"]}],"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":["62202227"],"award-info":[{"award-number":["62202227"]}],"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":["62302217"],"award-info":[{"award-number":["62302217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20220938"],"award-info":[{"award-number":["BK20220938"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20220934"],"award-info":[{"award-number":["BK20220934"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20220936"],"award-info":[{"award-number":["BK20220936"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M721626"],"award-info":[{"award-number":["2022M721626"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M711635"],"award-info":[{"award-number":["2022M711635"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["2022ZB267"],"award-info":[{"award-number":["2022ZB267"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30923010303"],"award-info":[{"award-number":["30923010303"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tmm.2024.3368910","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T19:45:39Z","timestamp":1708631139000},"page":"7426-7437","source":"Crossref","is-referenced-by-count":27,"title":["Learning With Imbalanced Noisy Data by Preventing Bias in Sample Selection"],"prefix":"10.1109","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5396-3183","authenticated-orcid":false,"given":"Huafeng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2011-8597","authenticated-orcid":false,"given":"Mengmeng","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6262-5338","authenticated-orcid":false,"given":"Zeren","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0337-9410","authenticated-orcid":false,"given":"Yazhou","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-5049","authenticated-orcid":false,"given":"Xian-Sheng","family":"Hua","sequence":"additional","affiliation":[{"name":"Terminus Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2999-2088","authenticated-orcid":false,"given":"Heng-Tao","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3141886"},{"key":"ref2","first-page":"1106","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Krizhevsky","year":"2012"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3121571"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00164"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3230331"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/175"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3126430"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3275913"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3157481"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/366"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/76"},{"key":"ref14","first-page":"2424","article-title":"The multidimensional wisdom of crowds","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Welinder","year":"2010"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2010.2048990"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref17","first-page":"8536","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han","year":"2018"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00515"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"ref20","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu","year":"2019"},{"key":"ref21","first-page":"1","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00524"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/455"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/356"},{"key":"ref25","first-page":"24392","article-title":"Understanding and improving early stopping for learning with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bai","year":"2021"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3180545"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01613"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00041"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00740"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3178690"},{"key":"ref31","first-page":"312","article-title":"Unsupervised label noise modeling and loss correction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arazo","year":"2019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3158001"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref35","first-page":"1","article-title":"Training deep neural-networks using a noise adaptation layer","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goldberger","year":"2017"},{"key":"ref36","first-page":"6835","article-title":"Are anchor points really indispensable in label-noise learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xia","year":"2019"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01043"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/340"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413978"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475536"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3055024"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413851"},{"key":"ref45","first-page":"8792","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang","year":"2018"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3236459"},{"key":"ref47","first-page":"1","article-title":"mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2018"},{"key":"ref48","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu","year":"2020"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00046"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3181439"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_8"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00392"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00014"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01122"},{"key":"ref56","first-page":"4331","article-title":"Learning to reweight examples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ren","year":"2018"},{"key":"ref57","first-page":"1917","article-title":"Meta-weight-Net: Learning an explicit mapping for sample weighting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shu","year":"2019"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20661"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20654"},{"key":"ref60","first-page":"1","article-title":"Sample selection with uncertainty of losses for learning with noisy labels","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xia","year":"2022"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00176"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00945"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01514"},{"key":"ref64","first-page":"1","article-title":"SoftMatch: Addressing the quantity-quality trade-off in semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen","year":"2023"},{"key":"ref65","first-page":"5050","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Berthelot","year":"2019"},{"key":"ref66","first-page":"960","article-title":"Decoupling when to update from how to update","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Malach","year":"2017"},{"key":"ref67","first-page":"1","article-title":"Robust early-learning: Hindering the memorization of noisy labels","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xia","year":"2020"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3116430"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26725"},{"key":"ref70","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413978"},{"key":"ref72","first-page":"15637","article-title":"Using self-supervised learning can improve model robustness and uncertainty","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hendrycks","year":"2019"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093342"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_46"},{"key":"ref75","first-page":"19365","article-title":"Self-adaptive training: Beyond empirical risk minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Huang","year":"2020"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01043"},{"key":"ref77","first-page":"14153","article-title":"Robust training under label noise by over-parameterization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu","year":"2022"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109080"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00932"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6046\/10384483\/10443531.pdf?arnumber=10443531","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T17:56:53Z","timestamp":1725904613000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10443531\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":80,"URL":"https:\/\/doi.org\/10.1109\/tmm.2024.3368910","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}