{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:26:15Z","timestamp":1772907975068,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585167","type":"print"},{"value":"9783030585174","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58517-4_46","type":"book-chapter","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T19:03:11Z","timestamp":1602270191000},"page":"786-802","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Suppressing Mislabeled Data via Grouping and Self-attention"],"prefix":"10.1007","author":[{"given":"Xiaojiang","family":"Peng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyang","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,10]]},"reference":[{"key":"46_CR1","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N.E., McGuinness, K.: Unsupervised label noise modeling and loss correction (2019)"},{"key":"46_CR2","unstructured":"Arpit, D., et al.: A closer look at memorization in deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 233\u2013242. JMLR. org (2017)"},{"key":"46_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/3-540-44522-6_64","volume-title":"Advances in Pattern Recognition","author":"R Barandela","year":"2000","unstructured":"Barandela, R., Gasca, E.: Decontamination of training samples for supervised pattern recognition methods. In: Ferri, F.J., I\u00f1esta, J.M., Amin, A., Pudil, P. (eds.) SSPR \/SPR 2000. LNCS, vol. 1876, pp. 621\u2013630. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-44522-6_64"},{"key":"46_CR4","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: ICML, pp. 41\u201348. ACM (2009)","DOI":"10.1145\/1553374.1553380"},{"key":"46_CR5","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1613\/jair.606","volume":"11","author":"CE Brodley","year":"1999","unstructured":"Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. J. Artif. Intell. Res. 11, 131\u2013167 (1999)","journal-title":"J. Artif. Intell. Res."},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"5","key":"46_CR7","first-page":"845","volume":"25","author":"B Fr\u00e9nay","year":"2014","unstructured":"Fr\u00e9nay, B., Verleysen, M.: Classification in the presence of label noise: a survey. TNNLS 25(5), 845\u2013869 (2014)","journal-title":"TNNLS"},{"issue":"2","key":"46_CR8","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/s11263-013-0658-4","volume":"106","author":"Y Gong","year":"2014","unstructured":"Gong, Y., Ke, Q., Isard, M., Lazebnik, S.: A multi-view embedding space for modeling internet images, tags, and their semantics. IJCV 106(2), 210\u2013233 (2014). https:\/\/doi.org\/10.1007\/s11263-013-0658-4","journal-title":"IJCV"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Guo, H., Mao, Y., Zhang, R.: Mixup as locally linear out-of-manifold regularization. In: AAAI, vol. 33, pp. 3714\u20133722 (2019)","DOI":"10.1609\/aaai.v33i01.33013714"},{"key":"46_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-3-030-01249-6_9","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Guo","year":"2018","unstructured":"Guo, S., et al.: CurriculumNet: weakly supervised learning from large-scale web images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 139\u2013154. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_9"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Han, J., Luo, P., Wang, X.: Deep self-learning from noisy labels. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00524"},{"key":"46_CR12","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: regularizing very deep neural networks on corrupted labels. arXiv preprint arXiv:1712.05055 (2017)"},{"key":"46_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-3-319-46478-7_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Joulin","year":"2016","unstructured":"Joulin, A., van der Maaten, L., Jabri, A., Vasilache, N.: Learning visual features from large weakly supervised data. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 67\u201384. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_5"},{"key":"46_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/978-3-319-46487-9_19","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Krause","year":"2016","unstructured":"Krause, J., et al.: The unreasonable effectiveness of noisy data for fine-grained recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 301\u2013320. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_19"},{"key":"46_CR15","doi-asserted-by":"crossref","unstructured":"Lee, K.H., He, X., Zhang, L., Yang, L.: CleanNet: transfer learning for scalable image classifier training with label noise. arXiv preprint arXiv:1711.07131 (2017)","DOI":"10.1109\/CVPR.2018.00571"},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"Lee, K.H., He, X., Zhang, L., Yang, L.: CleanNet: transfer learning for scalable image classifier training with label noise. In: CVPR, pp. 5447\u20135456 (2018)","DOI":"10.1109\/CVPR.2018.00571"},{"key":"46_CR17","doi-asserted-by":"crossref","unstructured":"Li, J., Wong, Y., Zhao, Q., Kankanhalli, M.S.: Learning to learn from noisy labeled data. In: CVPR, June 2019","DOI":"10.1109\/CVPR.2019.00519"},{"key":"46_CR18","doi-asserted-by":"publisher","first-page":"102963","DOI":"10.1016\/j.cviu.2020.102963","volume":"196","author":"Q Li","year":"2020","unstructured":"Li, Q., Peng, X., Cao, L., Du, W., Xing, H., Qiao, Y.: Product image recognition with guidance learning and noisy supervision. Comput. Vis. Image Underst. 196, 102963 (2020)","journal-title":"Comput. Vis. Image Underst."},{"key":"46_CR19","unstructured":"Li, W., Wang, L., Li, W., Agustsson, E., Van Gool, L.: Webvision database: visual learning and understanding from web data. arXiv preprint arXiv:1708.02862 (2017)"},{"key":"46_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., Li, L.J.: Learning from noisy labels with distillation. In: ICCV, pp. 1928\u20131936 (2017)","DOI":"10.1109\/ICCV.2017.211"},{"key":"46_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"46_CR22","unstructured":"Mai, Z., Hu, G., Chen, D., Shen, F., Shen, H.T.: MetaMixUp: learning adaptive interpolation policy of MixUp with meta-learning. arXiv preprint arXiv:1908.10059 (2019)"},{"issue":"3","key":"46_CR23","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1109\/TSMCB.2012.2223460","volume":"43","author":"N Manwani","year":"2013","unstructured":"Manwani, N., Sastry, P.: Noise tolerance under risk minimization. IEEE Trans. Cybern. 43(3), 1146\u20131151 (2013)","journal-title":"IEEE Trans. Cybern."},{"key":"46_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/978-3-642-02319-4_50","volume-title":"Hybrid Artificial Intelligence Systems","author":"ALB Miranda","year":"2009","unstructured":"Miranda, A.L.B., Garcia, L.P.F., Carvalho, A.C.P.L.F., Lorena, A.C.: Use of classification algorithms in noise detection and elimination. In: Corchado, E., Wu, X., Oja, E., Herrero, \u00c1., Baruque, B. (eds.) HAIS 2009. LNCS (LNAI), vol. 5572, pp. 417\u2013424. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-02319-4_50"},{"key":"46_CR25","doi-asserted-by":"crossref","unstructured":"Misra, I., Lawrence Zitnick, C., Mitchell, M., Girshick, R.: Seeing through the human reporting bias: visual classifiers from noisy human-centric labels. In: CVPR, pp. 2930\u20132939 (2016)","DOI":"10.1109\/CVPR.2016.320"},{"key":"46_CR26","doi-asserted-by":"crossref","unstructured":"Patrini, G., Rozza, A., Menon, A.K., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach. In: CVPR, pp. 2233\u20132241 (2017)","DOI":"10.1109\/CVPR.2017.240"},{"key":"46_CR27","unstructured":"Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., Rabinovich, A.: Training deep neural networks on noisy labels with bootstrapping. arXiv preprint arXiv:1412.6596 (2014)"},{"key":"46_CR28","unstructured":"Rolnick, D., Veit, A., Belongie, S., Shavit, N.: Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694 (2017)"},{"issue":"4","key":"46_CR29","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1111\/j.1467-9280.1992.tb00029.x","volume":"3","author":"RA Schmidt","year":"1992","unstructured":"Schmidt, R.A., Bjork, R.A.: New conceptualizations of practice: common principles in three paradigms suggest new concepts for training. Psychol. Sci. 3(4), 207\u2013218 (1992)","journal-title":"Psychol. Sci."},{"key":"46_CR30","unstructured":"Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., Fergus, R.: Training convolutional networks with noisy labels. arXiv preprint arXiv:1406.2080 (2014)"},{"key":"46_CR31","doi-asserted-by":"crossref","unstructured":"Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. arXiv preprint arXiv:1803.11364 (2018)","DOI":"10.1109\/CVPR.2018.00582"},{"key":"46_CR32","doi-asserted-by":"crossref","unstructured":"Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., Belongie, S.J.: Learning from noisy large-scale datasets with minimal supervision, In: CVPR. pp. 6575\u20136583 (2017)","DOI":"10.1109\/CVPR.2017.696"},{"key":"46_CR33","unstructured":"Verma, V., et al.: Manifold mixup: better representations by interpolating hidden states, pp. 6438\u20136447 (2019)"},{"key":"46_CR34","doi-asserted-by":"crossref","unstructured":"Wang, K., Peng, X., Yang, J., Lu, S., Qiao, Y.: Suppressing uncertainties for large-scale facial expression recognition. In: CVPR, June 2020","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"46_CR35","unstructured":"Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: CVPR, pp. 2691\u20132699 (2015)"},{"key":"46_CR36","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"46_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wang, Y., Qiao, Y.: MetaCleaner: learning to hallucinate clean representations for noisy-labeled visual recognition. In: CVPR, pp. 7373\u20137382 (2019)","DOI":"10.1109\/CVPR.2019.00755"},{"key":"46_CR38","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Liu, L., Li, Y., Shen, C., Reid, I.: Attend in groups: a weakly-supervised deep learning framework for learning from web data. In: CVPR, pp. 1878\u20131887 (2017)","DOI":"10.1109\/CVPR.2017.311"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58517-4_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:19:27Z","timestamp":1728433167000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58517-4_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585167","9783030585174"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58517-4_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"10 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}