{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T19:29:48Z","timestamp":1773170988449,"version":"3.50.1"},"reference-count":104,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100017607","name":"Shenzhen Municipal Fundamental Research Program","doi-asserted-by":"publisher","award":["JCYJ20250604145514018"],"award-info":[{"award-number":["JCYJ20250604145514018"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSFC Young Scientists Fund","award":["62506096"],"award-info":[{"award-number":["62506096"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62236003"],"award-info":[{"award-number":["62236003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1109\/tpami.2025.3646184","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T18:58:30Z","timestamp":1766170710000},"page":"4657-4672","source":"Crossref","is-referenced-by-count":0,"title":["Noisy Correspondence Rectification in Multimodal Clustering Space for Cross-Modal Matching"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6145-0150","authenticated-orcid":false,"given":"Shuo","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6924-7406","authenticated-orcid":false,"given":"Yancheng","family":"Long","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}]},{"given":"Yujie","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}]},{"given":"Zeke","family":"Xie","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3298-2574","authenticated-orcid":false,"given":"Hongxun","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9581-8849","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1476-0273","authenticated-orcid":false,"given":"Liqiang","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00636"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00475"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2019.2892755"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11236"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.348"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_13"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/492"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2017.06.005"},{"key":"ref9","first-page":"1","article-title":"Free lunch for few-shot learning: Distribution calibration","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yang","year":"2021"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3132021"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.110"},{"key":"ref12","first-page":"31629","article-title":"Revisiting context aggregation for image matting","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Liu","year":"2024"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3347633"},{"key":"ref14","first-page":"55948","article-title":"Mind the boundary: Coreset selection via reconstructing the decision boundary","volume-title":"Proc. Int. Conf. Mach. Learn","volume":"235","author":"Yang","year":"2024"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3301876"},{"key":"ref16","article-title":"Dataset pruning: Reducing training data by examining generalization influence","author":"Yang","year":"2022"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20074-8_8"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3263078"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3093590"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3290544"},{"issue":"325","key":"ref22","first-page":"1","article-title":"Mentored learning: Improving generalization and convergence of student learner","volume":"25","author":"Cao","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"ref23","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Int. Conf. Mach. Learn.","author":"Radford","year":"2021"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1238"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref27","first-page":"8527","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Han"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02585"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01904"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2903661"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2929068"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093614"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00306"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3182426"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3088863"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.142"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01455"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0573"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3178899"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01847"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19836-6_20"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01067"},{"key":"ref43","first-page":"9226","article-title":"Modality competition: What makes joint training of multi-modal network fail in deep learning","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","volume":"162","author":"Huang","year":"2022"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW60793.2023.00395"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2797921"},{"key":"ref46","article-title":"VSE++: Improving visual-semantic embeddings with hard negatives","author":"Faghri","year":"2017"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00591"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00359"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01520"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58577-8_7"},{"key":"ref51","first-page":"5583","article-title":"Vilt: Vision-and-language transformer without convolution or region supervision","volume-title":"Proc. Intern. Conf. Mach. Learn.","author":"Kim","year":"2021"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16209"},{"key":"ref53","first-page":"5836","article-title":"Masking: A new perspective of noisy supervision","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han","year":"2018"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_9"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.696"},{"key":"ref56","first-page":"5596","article-title":"Toward Robustness against label noise in training deep discriminative neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vahdat","year":"2017"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.211"},{"key":"ref58","first-page":"431","article-title":"Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Li","year":"2020"},{"key":"ref59","article-title":"Class2Simi: A new perspective on learning with label noise","author":"Wu","year":"2020"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01676"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"ref62","first-page":"6222","article-title":"L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xu","year":"2019"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref64","first-page":"6543","article-title":"Normalized loss functions for deep learning with noisy labels","volume-title":"Proc. Intern. Conf. Mach. Learn.","author":"Ma","year":"2020"},{"key":"ref65","first-page":"1","article-title":"Robust early-learning: Hindering the memorization of noisy labels","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xia","year":"2021"},{"key":"ref66","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":"ref67","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i7.28564"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02568"},{"key":"ref69","article-title":"Robust noisy correspondence learning via self-drop and dual-weight","author":"Liu","year":"2024"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3247939"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i2.27911"},{"key":"ref72","first-page":"24829","article-title":"Cross-modal active complementary learning with self-refining correspondence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Qin","year":"2023"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00726"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1145\/3662732"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3680860"},{"key":"ref76","first-page":"7017","article-title":"Searching to exploit memorization effect in learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yao","year":"2020"},{"key":"ref77","first-page":"7164","article-title":"How does disagreement benefit co-teaching","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu","year":"2019"},{"key":"ref78","first-page":"960","article-title":"Decoupling \u201cwhen to update\u201d from \u201chow to update\u201d","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Malach","year":"2017"},{"key":"ref79","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":"ref80","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","year":"2018"},{"key":"ref81","article-title":"Sample selection with uncertainty of losses for learning with noisy labels","author":"Xia","year":"2021"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3318002"},{"key":"ref83","first-page":"3361","article-title":"Dimensionality-driven learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma","year":"2018"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref85","article-title":"Reliable label correction is a good booster when learning with extremely noisy labels","author":"Wang","year":"2022"},{"key":"ref86","first-page":"25302","article-title":"Estimating instance-dependent Bayes-label transition matrix using a deep neural network","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yang","year":"2022"},{"issue":"2","key":"ref87","first-page":"7","article-title":"Estimating instance-dependent label-noise transition matrix using DNNs","volume":"1","author":"Yang","year":"2021"},{"key":"ref88","first-page":"1","article-title":"Training deep neural networks on noisy labels with bootstrapping","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Reed","year":"2015"},{"key":"ref89","first-page":"312","article-title":"Unsupervised label noise modeling and loss correction","volume-title":"Proc. Intern. Conf. Mach. Learn.","author":"Arazo","year":"2019"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref91","first-page":"5907","article-title":"SELFIE: Refurbishing unclean samples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Song","year":"2019"},{"key":"ref92","first-page":"11447","article-title":"Error-bounded correction of noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zheng","year":"2020"},{"key":"ref93","first-page":"29406","article-title":"Learning with noisy correspondence for cross-modal matching","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Huang"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547922"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26725"},{"key":"ref96","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Liu","year":"2020"},{"key":"ref97","first-page":"1","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2015"},{"key":"ref98","first-page":"1542","article-title":"Graph optimal transport for cross-domain alignment","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen","year":"2020"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00166"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16209"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_13"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00475"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01267"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11424231\/11304594.pdf?arnumber=11304594","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T01:34:54Z","timestamp":1773106494000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11304594\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":104,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3646184","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":[[2026,4]]}}}