{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:43:44Z","timestamp":1761745424501,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030712778"},{"type":"electronic","value":"9783030712785"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-71278-5_7","type":"book-chapter","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T08:05:38Z","timestamp":1615881938000},"page":"86-100","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Long-Tailed Recognition Using Class-Balanced Experts"],"prefix":"10.1007","author":[{"given":"Saurabh","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Fritz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernt","family":"Schiele","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"unstructured":"Bengio, S.: The battle against the long tail. In: Workshop on Big Data and Statistical Machine Learning (2015)","key":"7_CR1"},{"key":"7_CR2","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. JAIR 16, 321\u2013357 (2002)","journal-title":"JAIR"},{"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)","key":"7_CR3","DOI":"10.1109\/CVPR.2019.00949"},{"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 (2009)","key":"7_CR4","DOI":"10.1109\/CVPR.2009.5206848"},{"doi-asserted-by":"crossref","unstructured":"Dong, Q., Gong, S., Zhu, X.: Class rectification hard mining for imbalanced deep learning. In: ICCCV (2017)","key":"7_CR5","DOI":"10.1109\/ICCV.2017.205"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/j.0824-7935.2004.t01-1-00228.x","volume":"20","author":"A Estabrooks","year":"2004","unstructured":"Estabrooks, A., Jo, T., Japkowicz, N.: A multiple resampling method for learning from imbalanced data sets. Comput. Intell. 20, 18\u201336 (2004)","journal-title":"Comput. Intell."},{"key":"7_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-030-01231-1_2","volume-title":"Computer Vision \u2013 ECCV 2018","author":"R Felix","year":"2018","unstructured":"Felix, R., Vijay Kumar, B.G., Reid, I., Carneiro, G.: Multi-modal cycle-consistent generalized zero-shot learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 21\u201337. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_2"},{"unstructured":"Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42, 463\u2013484 (2011)","key":"7_CR8"},{"doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: CVPR (2018)","key":"7_CR9","DOI":"10.1109\/CVPR.2018.00459"},{"unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1321\u20131330. JMLR. org (2017)","key":"7_CR10"},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1145\/1007730.1007736","volume":"6","author":"H Guo","year":"2004","unstructured":"Guo, H., Viktor, H.L.: Learning from imbalanced data sets with boosting and data generation: the databoost-im approach. KDD Explor. Newslett. 6, 30\u201339 (2004)","journal-title":"KDD Explor. Newslett."},{"doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W.Y., Mao, B.H.: Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: ICIC (2005)","key":"7_CR12","DOI":"10.1007\/11538059_91"},{"key":"7_CR13","first-page":"1263","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. TKDE 21, 1263\u20131284 (2009)","journal-title":"TKDE"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","key":"7_CR14","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of International Conference on Learning Representations (2017)","key":"7_CR15"},{"unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: Proceedings of the International Conference on Learning Representations (2019)","key":"7_CR16"},{"unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)","key":"7_CR17"},{"key":"7_CR18","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1109\/TPAMI.2019.2914680","volume":"42","author":"C Huang","year":"2019","unstructured":"Huang, C., Li, Y., Chen, C.L., Tang, X.: Deep imbalanced learning for face recognition and attribute prediction. TPAMI 42, 2781\u20132794 (2019)","journal-title":"TPAMI"},{"issue":"1","key":"7_CR19","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacobs","year":"1991","unstructured":"Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79\u201387 (1991)","journal-title":"Neural Comput."},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1109\/TSMCA.2010.2084081","volume":"41","author":"TM Khoshgoftaar","year":"2010","unstructured":"Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Comparing boosting and bagging techniques with noisy and imbalanced data. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41, 552\u2013568 (2010)","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"7_CR21","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.asoc.2013.08.014","volume":"14","author":"B Krawczyk","year":"2014","unstructured":"Krawczyk, B., Wo\u017aniak, M., Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14, 554\u2013562 (2014)","journal-title":"Appl. Soft Comput."},{"unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: 6th International Conference on Learning Representations, ICLR 2018 (2018)","key":"7_CR22"},{"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 (2017)","key":"7_CR23","DOI":"10.1109\/ICCV.2017.324"},{"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)","key":"7_CR24","DOI":"10.1109\/CVPR.2019.00264"},{"unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)","key":"7_CR25"},{"doi-asserted-by":"crossref","unstructured":"Oh Song, H., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: CVPR (2016)","key":"7_CR26","DOI":"10.1109\/CVPR.2016.434"},{"doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717\u20131724 (2014)","key":"7_CR27","DOI":"10.1109\/CVPR.2014.222"},{"doi-asserted-by":"crossref","unstructured":"Qi, H., Brown, M., Lowe, D.G.: Low-shot learning with imprinted weights. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5822\u20135830 (2018)","key":"7_CR28","DOI":"10.1109\/CVPR.2018.00610"},{"doi-asserted-by":"crossref","unstructured":"Qiao, S., Liu, C., Shen, W., Yuille, A.L.: Few-shot image recognition by predicting parameters from activations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7229\u20137238 (2018)","key":"7_CR29","DOI":"10.1109\/CVPR.2018.00755"},{"unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: NeurIPS (2017)","key":"7_CR30"},{"doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, Y., Chua, T.S., Schiele, B.: Meta-transfer learning for few-shot learning. In: CVPR (2019)","key":"7_CR31","DOI":"10.1109\/CVPR.2019.00049"},{"unstructured":"Van Horn, G., Perona, P.: The devil is in the tails: fine-grained classification in the wild. arXiv preprint arXiv:1709.01450 (2017)","key":"7_CR32"},{"unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: NIPS (2016)","key":"7_CR33"},{"key":"7_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-009-0198-y","volume":"25","author":"BX Wang","year":"2010","unstructured":"Wang, B.X., Japkowicz, N.: Boosting support vector machines for imbalanced data sets. Knowl. Inf. Syst. 25, 1\u201320 (2010)","journal-title":"Knowl. Inf. Syst."},{"doi-asserted-by":"crossref","unstructured":"Wang, S., Yao, X.: Diversity analysis on imbalanced data sets by using ensemble models. In: CIDM (2009)","key":"7_CR35","DOI":"10.1109\/CIDM.2009.4938667"},{"doi-asserted-by":"crossref","unstructured":"Wang, Y.X., Girshick, R., Hebert, M., Hariharan, B.: Low-shot learning from imaginary data. In: CVPR (2018)","key":"7_CR36","DOI":"10.1109\/CVPR.2018.00760"},{"key":"7_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1007\/978-3-319-46466-4_37","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y-X Wang","year":"2016","unstructured":"Wang, Y.-X., Hebert, M.: Learning to learn: model regression networks for easy small sample learning. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 616\u2013634. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_37"},{"unstructured":"Wang, Y.X., Ramanan, D., Hebert, M.: Learning to model the tail. In: NeurIPS (2017)","key":"7_CR38"},{"key":"7_CR39","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert, D.H.: Stacked generalization. Neural Netw. 5, 241\u2013259 (1992)","journal-title":"Neural Netw."},{"doi-asserted-by":"crossref","unstructured":"Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: Feature generating networks for zero-shot learning. In: CVPR (2018)","key":"7_CR40","DOI":"10.1109\/CVPR.2018.00581"},{"doi-asserted-by":"crossref","unstructured":"Xian, Y., Sharma, S., Schiele, B., Akata, Z.: f-vaegan-d2: a feature generating framework for any-shot learning. In: CVPR (2019)","key":"7_CR41","DOI":"10.1109\/CVPR.2019.01052"},{"doi-asserted-by":"crossref","unstructured":"Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Feature transfer learning for face recognition with under-represented data. In: CVPR (2019)","key":"7_CR42","DOI":"10.1109\/CVPR.2019.00585"},{"doi-asserted-by":"crossref","unstructured":"Yu, N., Shen, X., Lin, Z., Mech, R., Barnes, C.: Learning to detect multiple photographic defects. In: WACV (2018)","key":"7_CR43","DOI":"10.1109\/WACV.2018.00156"},{"issue":"8","key":"7_CR44","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1109\/TNNLS.2012.2200299","volume":"23","author":"SE Yuksel","year":"2012","unstructured":"Yuksel, S.E., Wilson, J.N., Gader, P.D.: Twenty years of mixture of experts. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1177\u20131193 (2012)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"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)","key":"7_CR45","DOI":"10.1109\/ICCV.2017.578"},{"doi-asserted-by":"crossref","unstructured":"Zhong, Y., et al.: Unequal-training for deep face recognition with long-tailed noisy data. In: CVPR (2019)","key":"7_CR46","DOI":"10.1109\/CVPR.2019.00800"},{"key":"7_CR47","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","volume":"40","author":"B Zhou","year":"2017","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. TPAMI 40, 1452\u20131464 (2017)","journal-title":"TPAMI"},{"doi-asserted-by":"crossref","unstructured":"Zhu, X., Anguelov, D., Ramanan, D.: Capturing long-tail distributions of object subcategories. In: CVPR (2014)","key":"7_CR48","DOI":"10.1109\/CVPR.2014.122"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-71278-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T16:28:50Z","timestamp":1671640130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-71278-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030712778","9783030712785"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-71278-5_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"17 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"T\u00fcbingen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"28 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.gcpr-vmv-vcbm-2020.uni-tuebingen.de\/?page_id=102","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":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"89","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":"35","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":"39% - 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":"4.15","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":"No","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 took place virtually due to the COVID-19 pandemic","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)"}}]}}