{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T13:08:50Z","timestamp":1784293730802,"version":"3.55.0"},"publisher-location":"Cham","reference-count":60,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200434","type":"print"},{"value":"9783031200441","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20044-1_4","type":"book-chapter","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T23:11:54Z","timestamp":1666221114000},"page":"57-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and\u00a0Beyond"],"prefix":"10.1007","author":[{"given":"Yuzhe","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dina","family":"Katabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"4_CR1","unstructured":"Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)"},{"key":"4_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-030-01270-0_28","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Beery","year":"2018","unstructured":"Beery, S., Van Horn, G., Perona, P.: Recognition in terra incognita. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 472\u2013489. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_28"},{"issue":"1","key":"4_CR3","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.W.: A theory of learning from different domains. Mach. Learn. 79(1), 151\u2013175 (2010)","journal-title":"Mach. Learn."},{"issue":"2","key":"4_CR4","first-page":"1","volume":"22","author":"G Blanchard","year":"2021","unstructured":"Blanchard, G., Deshmukh, A.A., Dogan, U., Lee, G., Scott, C.: Domain generalization by marginal transfer learning. J. Mach. Learn. Res. 22(2), 1\u201355 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249\u2013259 (2018)","journal-title":"Neural Netw."},{"key":"4_CR6","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: NeurIPS (2019)"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Carlucci, F.M., D\u2019Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving Jigsaw puzzles. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00233"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Carroll, J.D., Arabie, P.: Multidimensional scaling. In: Measurement, Judgment and Decision Making, pp. 179\u2013250 (1998)","DOI":"10.1016\/B978-012099975-0.50005-1"},{"key":"4_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"issue":"1","key":"4_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0169-7439(99)00047-7","volume":"50","author":"R De Maesschalck","year":"2000","unstructured":"De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The Mahalanobis distance. Chemom. Intell. Lab. Syst. 50(1), 1\u201318 (2000)","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"6","key":"4_CR12","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1109\/TPAMI.2018.2832629","volume":"41","author":"Q Dong","year":"2019","unstructured":"Dong, Q., Gong, S., Zhu, X.: Imbalanced deep learning by minority class incremental rectification. IEEE Trans. Pattern Anal. Mach. Intell. 41(6), 1367\u20131381 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"4_CR13","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s10994-009-5148-0","volume":"79","author":"M Dredze","year":"2010","unstructured":"Dredze, M., Kulesza, A., Crammer, K.: Multi-domain learning by confidence-weighted parameter combination. Mach. Learn. 79(1), 123\u2013149 (2010)","journal-title":"Mach. Learn."},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Fang, C., Xu, Y., Rockmore, D.N.: Unbiased metric learning: on the utilization of multiple datasets and web images for softening bias. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.208"},{"issue":"1","key":"4_CR15","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030\u20132096 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR16","unstructured":"Globerson, A., Chechik, G., Pereira, F., Tishby, N.: Euclidean embedding of co-occurrence data. In: NeurIPS (2004)"},{"key":"4_CR17","unstructured":"Goldberger, J., Hinton, G.E., Roweis, S., Salakhutdinov, R.R.: Neighbourhood components analysis. In: NeurIPS (2004)"},{"issue":"1","key":"4_CR18","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13(1), 723\u2013773 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR19","unstructured":"Gulrajani, I., Lopez-Paz, D.: In search of lost domain generalization. In: ICLR (2021)"},{"key":"4_CR20","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: AdaSyn: adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks, pp. 1322\u20131328 (2008)"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Chen, C.L., Tang, X.: Deep imbalanced learning for face recognition and attribute prediction. IEEE Trans. Pattern Anal. Mach. Intell. (2019)","DOI":"10.1109\/TPAMI.2019.2914680"},{"key":"4_CR23","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: ICLR (2020)"},{"key":"4_CR24","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"4_CR25","unstructured":"Krueger, D., et al.: Out-of-distribution generalization via risk extrapolation (REX). arXiv preprint arXiv:2003.00688 (2020)"},{"issue":"11","key":"4_CR26","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Learning to generalize: meta-learning for domain generalization. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.591"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Li, H., Pan, S.J., Wang, S., Kot, A.C.: Domain generalization with adversarial feature learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00566"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Li, T., et al.: Targeted supervised contrastive learning for long-tailed recognition. arXiv preprint arXiv:2111.13998 (2021)","DOI":"10.1109\/CVPR52688.2022.00679"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Li, Y., Gong, M., Tian, X., Liu, T., Tao, D.: Domain generalization via conditional invariant representations. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11682"},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"4_CR33","doi-asserted-by":"crossref","unstructured":"Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"4_CR34","unstructured":"Muandet, K., Balduzzi, D., Sch\u00f6lkopf, B.: Domain generalization via invariant feature representation. In: ICML (2013)"},{"key":"4_CR35","doi-asserted-by":"crossref","unstructured":"Nam, H., Lee, H., Park, J., Yoon, W., Yoo, D.: Reducing domain gap by reducing style bias. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00858"},{"key":"4_CR36","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011)"},{"issue":"10","key":"4_CR37","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"4_CR38","doi-asserted-by":"crossref","unstructured":"Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00149"},{"key":"4_CR39","unstructured":"Ren, J., Yu, C., Ma, X., Zhao, H., Yi, S., et al.: Balanced meta-softmax for long-tailed visual recognition. In: NeurIPS (2020)"},{"key":"4_CR40","unstructured":"Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization. In: ICLR (2020)"},{"key":"4_CR41","unstructured":"Schoenauer-Sebag, A., Heinrich, L., Schoenauer, M., Sebag, M., Wu, L.F., Altschuler, S.J.: Multi-domain adversarial learning. In: ICLR (2019)"},{"key":"4_CR42","unstructured":"Shi, Y., et al.: Gradient matching for domain generalization. arXiv preprint arXiv:2104.09937 (2021)"},{"key":"4_CR43","unstructured":"Shu, J., et al.: Meta-weight-net: learning an explicit mapping for sample weighting. arXiv preprint arXiv:1902.07379 (2019)"},{"key":"4_CR44","unstructured":"Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: NeurIPS (2016)"},{"key":"4_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"issue":"5","key":"4_CR46","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/72.788640","volume":"10","author":"VN Vapnik","year":"1999","unstructured":"Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988\u2013999 (1999)","journal-title":"IEEE Trans. Neural Netw."},{"key":"4_CR47","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"4_CR48","unstructured":"Wang, X., Lian, L., Miao, Z., Liu, Z., Yu, S.: Long-tailed recognition by routing diverse distribution-aware experts. In: ICLR (2021)"},{"key":"4_CR49","doi-asserted-by":"crossref","unstructured":"Xiao, T., Li, H., Ouyang, W., Wang, X.: Learning deep feature representations with domain guided dropout for person re-identification. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.140"},{"key":"4_CR50","doi-asserted-by":"crossref","unstructured":"Xu, M., et al.: Adversarial domain adaptation with domain mixup. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i04.6123"},{"key":"4_CR51","unstructured":"Yang, Y., Hospedales, T.M.: A unified perspective on multi-domain and multi-task learning. In: ICLR (2015)"},{"key":"4_CR52","unstructured":"Yang, Y., Xu, Z.: Rethinking the value of labels for improving class-imbalanced learning. In: NeurIPS (2020)"},{"key":"4_CR53","unstructured":"Yang, Y., Zha, K., Chen, Y.C., Wang, H., Katabi, D.: Delving into deep imbalanced regression. In: ICML (2021)"},{"key":"4_CR54","unstructured":"Zhang, M., Marklund, H., Gupta, A., Levine, S., Finn, C.: Adaptive risk minimization: a meta-learning approach for tackling group shift. arXiv preprint arXiv:2007.02931 (2020)"},{"key":"4_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, X., Fang, Z., Wen, Y., Li, Z., Qiao, Y.: Range loss for deep face recognition with long-tailed training data. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.578"},{"key":"4_CR56","unstructured":"Zhang, Y., Hooi, B., Hong, L., Feng, J.: Test-agnostic long-tailed recognition by test-time aggregating diverse experts with self-supervision. arXiv preprint arXiv:2107.09249 (2021)"},{"key":"4_CR57","unstructured":"Zhang, Y., Kang, B., Hooi, B., Yan, S., Feng, J.: Deep long-tailed learning: a survey. arXiv preprint arXiv:2110.04596 (2021)"},{"key":"4_CR58","doi-asserted-by":"crossref","unstructured":"Zhou, B., Cui, Q., Wei, X.S., Chen, Z.M.: BBN: bilateral-branch network with cumulative learning for long-tailed visual recognition. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"4_CR59","doi-asserted-by":"crossref","unstructured":"Zhou, K., Liu, Z., Qiao, Y., Xiang, T., Loy, C.C.: Domain generalization in vision: a survey. arXiv preprint arXiv:2103.02503 (2021)","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"4_CR60","unstructured":"Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with mixstyle. In: ICLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20044-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:43:14Z","timestamp":1710358994000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20044-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200434","9783031200441"],"references-count":60,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20044-1_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}