{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:21:51Z","timestamp":1743074511061,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031263507"},{"type":"electronic","value":"9783031263514"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26351-4_16","type":"book-chapter","created":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T09:03:18Z","timestamp":1677315798000},"page":"252-268","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Meta-prototype Decoupled Training for\u00a0Long-Tailed Learning"],"prefix":"10.1007","author":[{"given":"Siming","family":"Fu","sequence":"first","affiliation":[]},{"given":"Huanpeng","family":"Chu","sequence":"additional","affiliation":[]},{"given":"Xiaoxuan","family":"He","sequence":"additional","affiliation":[]},{"given":"Hualiang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhenyu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Haoji","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,26]]},"reference":[{"key":"16_CR1","unstructured":"Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"16_CR2","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"16_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1007\/978-3-642-33712-3_41","volume-title":"Computer Vision \u2013 ECCV 2012","author":"D Chen","year":"2012","unstructured":"Chen, D., Cao, X., Wang, L., Wen, F., Sun, J.: Bayesian face revisited: a joint formulation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 566\u2013579. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33712-3_41"},{"key":"16_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Cui, J., Zhong, Z., Liu, S., Yu, B., Jia, J.: Parametric contrastive learning (2021)","DOI":"10.1109\/ICCV48922.2021.00075"},{"key":"16_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: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16_CR7","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning. pp. 1126\u20131135. PMLR (2017)"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Hong, Y., Han, S., Choi, K., Seo, S., Kim, B., Chang, B.: Disentangling label distribution for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6626\u20136636 (2021)","DOI":"10.1109\/CVPR46437.2021.00656"},{"key":"16_CR9","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition (2019)"},{"key":"16_CR10","unstructured":"Kang, B., Li, Y., Xie, S., Yuan, Z., Feng, J.: Exploring balanced feature spaces for representation learning. In: International Conference on Learning Representations (2021)"},{"key":"16_CR11","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla, P., et al.: Supervised contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 18661\u201318673 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR12","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"16_CR13","unstructured":"Li, J., Xiong, C., Hoi, S.C.: Mopro: webly supervised learning with momentum prototypes. arXiv preprint arXiv:2009.07995 (2020)"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Li, S., Gong, K., Liu, C.H., Wang, Y., Qiao, F., Cheng, X.: Metasaug: meta semantic augmentation for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5212\u20135221 (2021)","DOI":"10.1109\/CVPR46437.2021.00517"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Li, T., et al.: Targeted supervised contrastive learning for long-tailed recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6918\u20136928 (2022)","DOI":"10.1109\/CVPR52688.2022.00679"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"16_CR17","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2537\u20132546 (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"16_CR18","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: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"16_CR19","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"16_CR20","first-page":"4175","volume":"33","author":"J Ren","year":"2020","unstructured":"Ren, J., Yu, C., Ma, X., Zhao, H., Yi, S., et al.: Balanced meta-softmax for long-tailed visual recognition. Adv. Neural. Inf. Process. Syst. 33, 4175\u20134186 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Samuel, D., Chechik, G.: Distributional robustness loss for long-tail learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9495\u20139504 (2021)","DOI":"10.1109\/ICCV48922.2021.00936"},{"key":"16_CR22","unstructured":"Shu, J., et al.: Meta-weight-net: learning an explicit mapping for sample weighting. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"16_CR23","first-page":"1513","volume":"33","author":"K Tang","year":"2020","unstructured":"Tang, K., Huang, J., Zhang, H.: Long-tailed classification by keeping the good and removing the bad momentum causal effect. Adv. Neural. Inf. Process. Syst. 33, 1513\u20131524 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR24","unstructured":"Tukey, J.W., et al.: Exploratory data analysis, vol. 2. Reading, MA (1977)"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Van Horn, G., et al.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8769\u20138778 (2018)","DOI":"10.1109\/CVPR.2018.00914"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Vigneswaran, R., Law, M.T., Balasubramanian, V.N., Tapaswi, M.: Feature generation for long-tail classification. In: Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 1\u20139 (2021)","DOI":"10.1145\/3490035.3490300"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Wang, J., Lukasiewicz, T., Hu, X., Cai, J., Xu, Z.: RSG: a simple but effective module for learning imbalanced datasets. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3784\u20133793 (2021)","DOI":"10.1109\/CVPR46437.2021.00378"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Wang, P., Han, K., Wei, X.S., Zhang, L., Wang, L.: Contrastive learning based hybrid networks for long-tailed image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 943\u2013952 (2021)","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"16_CR29","unstructured":"Wang, X., Lian, L., Miao, Z., Liu, Z., Yu, S.: Long-tailed recognition by routing diverse distribution-aware experts. In: International Conference on Learning Representations (2021)"},{"key":"16_CR30","unstructured":"Wang, Y., Pan, X., Song, S., Zhang, H., Huang, G., Wu, C.: Implicit semantic data augmentation for deep networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. arXiv preprint arXiv:1611.05431 (2016)","DOI":"10.1109\/CVPR.2017.634"},{"key":"16_CR32","unstructured":"Yang, Y., Xu, Z.: Rethinking the value of labels for improving class-imbalanced learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 19290\u201319301 (2020)"},{"key":"16_CR33","unstructured":"Yuan, Y., Wang, J.: OCNET: object context network for scene parsing (2018)"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Zang, Y., Huang, C., Loy, C.C.: FASA: feature augmentation and sampling adaptation for long-tailed instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3457\u20133466 (2021)","DOI":"10.1109\/ICCV48922.2021.00344"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Z., Yan, S., He, X., Sun, J.: Distribution alignment: a unified framework for long-tail visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2361\u20132370 (2021)","DOI":"10.1109\/CVPR46437.2021.00239"},{"key":"16_CR36","unstructured":"Zhang, Y., Kang, B., Hooi, B., Yan, S., Feng, J.: Deep long-tailed learning: a survey. arXiv preprint arXiv:2110.04596 (2021)"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wei, X.S., Zhou, B., Wu, J.: Bag of tricks for long-tailed visual recognition with deep convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3447\u20133455 (2021)","DOI":"10.1609\/aaai.v35i4.16458"},{"key":"16_CR38","unstructured":"Zhang, Z., Xiang, X.: Long-tailed classification with gradual balanced loss and adaptive feature generation (2022)"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Cui, J., Liu, S., Jia, J.: Improving calibration for long-tailed recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16489\u201316498 (2021)","DOI":"10.1109\/CVPR46437.2021.01622"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. , pp. 633\u2013641 (2017)","DOI":"10.1109\/CVPR.2017.544"},{"key":"16_CR41","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9719\u20139728 (2020)","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Zhu, B., Niu, Y., Hua, X.S., Zhang, H.: Cross-domain empirical risk minimization for unbiased long-tailed classification. In: AAAI Conference on Artificial Intelligence (2022)","DOI":"10.1609\/aaai.v36i3.20271"},{"key":"16_CR43","doi-asserted-by":"crossref","unstructured":"Zhu, L., Yang, Y.: Inflated episodic memory with region self-attention for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4344\u20134353 (2020)","DOI":"10.1109\/CVPR42600.2020.00440"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26351-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T09:10:38Z","timestamp":1677316238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26351-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263507","9783031263514"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26351-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","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":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","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 Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","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":"277","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":"33% - 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":"3.3","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":"2.6","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":"For the ACCV 2022 workshops 25 papers have been accepted from 40 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)"}}]}}