{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T20:13:56Z","timestamp":1774642436845,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"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":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11263-024-02342-x","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T13:31:11Z","timestamp":1736170271000},"page":"3349-3366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Delving Deep into Simplicity Bias for Long-Tailed Image Recognition"],"prefix":"10.1007","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8200-1845","authenticated-orcid":false,"given":"Xiu-Shen","family":"Wei","sequence":"first","affiliation":[]},{"given":"Xuhao","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"2342_CR1","doi-asserted-by":"crossref","unstructured":"Alobaidi MH, Meguid MA, Zayed T (2022) Semi-supervised learning framework for oil and gas pipeline failure detection. Scientific Reports 12(13758)","DOI":"10.1038\/s41598-022-16830-y"},{"key":"2342_CR2","doi-asserted-by":"crossref","unstructured":"Alshammari, S., Wang, Y., Ramanan, D., & Kong, S. (2022). Long-tailed recognition via weight balancing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 6897\u20136907.","DOI":"10.1109\/CVPR52688.2022.00677"},{"key":"2342_CR3","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. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249\u2013259.","journal-title":"Neural Networks"},{"key":"2342_CR4","unstructured":"Byrd, J., & Lipton, Z. (2019). What is the effect of importance weighting in deep learning? In: International conference on machine learning, pp. 872\u2013881."},{"key":"2342_CR5","doi-asserted-by":"crossref","unstructured":"Cai, J., Wang, Y., & Hwang, J.N. (2021). ACE: Ally complementary experts for solving long-tailed recognition in one-shot. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 112\u2013121.","DOI":"10.1109\/ICCV48922.2021.00018"},{"key":"2342_CR6","doi-asserted-by":"crossref","unstructured":"Cao, D., Zhu, X., Huang, X., Guo, J., & Lei, Z. (2020). Domain balancing: Face recognition on long-tailed domains. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5670\u20135678.","DOI":"10.1109\/CVPR42600.2020.00571"},{"key":"2342_CR7","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., & Ma, T. (2019). Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in neural information processing systems, pp. 1567\u20131578."},{"key":"2342_CR8","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020). Unsupervised learning of visual features by contrasting cluster assignments. In: Advances in neural information processing systems, pp. 9912\u20139924."},{"key":"2342_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. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321\u2013357.","journal-title":"Journal of artificial intelligence research"},{"key":"2342_CR10","unstructured":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020a). A simple framework for contrastive learning of visual representations. In: International conference on machine learning, pp. 1597\u20131607."},{"key":"2342_CR11","doi-asserted-by":"crossref","unstructured":"Chen, X., & He, K. (2021). Exploring simple siamese representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 15,750\u201315,758.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"2342_CR12","unstructured":"Chen, X., Fan, H., Girshick, R., & He, K. (2020b). Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 pp. 1\u20133."},{"key":"2342_CR13","doi-asserted-by":"crossref","unstructured":"Chu, P., Bian, X., Liu, S., & Ling, H. (2020). Feature space augmentation for long-tailed data. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part 16, pp. 694\u2013710.","DOI":"10.1007\/978-3-030-58526-6_41"},{"key":"2342_CR14","unstructured":"Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., & Schmidhuber, J. (2011). High-performance neural networks for visual object classification. arXiv preprint arXiv:1102.0183 pp. 1\u201312."},{"key":"2342_CR15","doi-asserted-by":"crossref","unstructured":"Cui, J., Zhong, Z., Liu, S., Yu, B., & Jia, J. (2021). Parametric contrastive learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 715\u2013724.","DOI":"10.1109\/ICCV48922.2021.00075"},{"issue":"3","key":"2342_CR16","first-page":"3695","volume":"45","author":"J Cui","year":"2023","unstructured":"Cui, J., Liu, S., Tian, Z., Zhong, Z., & Jia, J. (2023). ResLT: Residual learning for long-tailed recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3695\u20133706.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2342_CR17","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., & Belongie, S. (2019). Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9268\u20139277.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"2342_CR18","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Bursuc, A., Komodakis, N., P\u00e9rez, P., & Cord, M. (2019). Boosting few-shot visual learning with self-supervision. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 8059\u20138068.","DOI":"10.1109\/ICCV.2019.00815"},{"key":"2342_CR19","unstructured":"Grill, J.B., Strub, F., Altch\u00e9, F., Tallec, C., Richemond, P.H., Buchatskaya, E., Doersch, C., Pires, B.A,. Guo, Z.D., Azar, M.G., Piot, B., Kavukcuoglu, K., Munos, R., & Valko, M. (2020). Bootstrap your own latent: A new approach to self-supervised learning. In: Advances in Neural Information Process System, pp. 21,271\u201321,284."},{"key":"2342_CR20","unstructured":"Guo, C., Pleiss, G., Sun, Y., & Weinberger, K.Q. (2017). On calibration of modern neural networks. In: International conference on machine learning, pp. 1321\u20131330."},{"issue":"9","key":"2342_CR21","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263\u20131284.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2342_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2342_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9729\u20139738.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"2342_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 16,000\u201316,009.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"2342_CR25","doi-asserted-by":"crossref","unstructured":"He, Y.Y., Wu, J., & Wei, X.S. (2021). Distilling virtual examples for long-tailed recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 235\u2013244.","DOI":"10.1109\/ICCV48922.2021.00030"},{"key":"2342_CR26","doi-asserted-by":"crossref","unstructured":"Hou, C., Zhang, J., Wang, H., & Zhou, T. (2023). Subclass-balancing contrastive learning for long-tailed recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5395\u20135407.","DOI":"10.1109\/ICCV51070.2023.00497"},{"key":"2342_CR27","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Change\u00a0Loy, C., & Tang, X. (2016). Learning deep representation for imbalanced classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5375\u20135384.","DOI":"10.1109\/CVPR.2016.580"},{"issue":"11","key":"2342_CR28","doi-asserted-by":"publisher","first-page":"2781","DOI":"10.1109\/TPAMI.2019.2914680","volume":"42","author":"C Huang","year":"2020","unstructured":"Huang, C., Li, Y., Loy, C. C., & Tang, X. (2020). Deep imbalanced learning for face recognition and attribute prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(11), 2781\u20132794.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2342_CR29","unstructured":"Huh, M., Mobahi, H., Zhang, R., Cheung, B., Agrawal, P., & Isola, P. (2021). The low-rank simplicity bias in deep networks. arXiv preprint arXiv:2103.10427."},{"issue":"5","key":"2342_CR30","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N Japkowicz","year":"2002","unstructured":"Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent data analysis, 6(5), 429\u2013449.","journal-title":"Intelligent data analysis"},{"key":"2342_CR31","unstructured":"Kang, B., Xie, S., Rohrbach, M., Yan, Z., Gordo, A., Feng, J., & Kalantidis, Y. (2020). Decoupling representation and classifier for long-tailed recognition. In: Proceedings in International Conference Learning Representations, pp. 1\u201316."},{"key":"2342_CR32","unstructured":"Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images. Citeseer: Technical report"},{"key":"2342_CR33","doi-asserted-by":"crossref","unstructured":"Li, M., ming Cheung, Y., & Lu, Y. (2022). Long-tailed visual recognition via gaussian clouded logit adjustment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6929\u20136938.","DOI":"10.36227\/techrxiv.17031920.v1"},{"key":"2342_CR34","doi-asserted-by":"crossref","unstructured":"Li, T., Wang, L., & Wu, G. (2021). Self supervision to distillation for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 630\u2013639.","DOI":"10.1109\/ICCV48922.2021.00067"},{"key":"2342_CR35","doi-asserted-by":"crossref","unstructured":"Liu, B., Li, H., Kang, H., & Hua, G. (2021). GistNet: A geometric structure transfer network for long-tailed recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 8209\u20138218.","DOI":"10.1109\/ICCV48922.2021.00810"},{"key":"2342_CR36","unstructured":"Liu, H., HaoChen, J.Z., Gaidon, A., & Ma, T. (2022). Self-supervised learning is more robust to dataset imbalance. In: Proceedings of International Conference Learning Representations, pp. 1\u201324."},{"key":"2342_CR37","doi-asserted-by":"crossref","unstructured":"Liu, X.Y., & Zhou, Z.H. (2006). The influence of class imbalance on cost-sensitive learning: An empirical study. In: Proceeding IEEE International Conference Data Mining, pp. 970\u2013974.","DOI":"10.1109\/ICDM.2006.158"},{"key":"2342_CR38","doi-asserted-by":"crossref","unstructured":"Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S.X. (2019). 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.","DOI":"10.1109\/CVPR.2019.00264"},{"key":"2342_CR39","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., & Hinton, G. (2008). Visualizing data using $$t$$-SNE. Journal of Machine Learning Research, 9, 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"2342_CR40","doi-asserted-by":"crossref","unstructured":"Mei, S., Zhao, C., Yuan, S., & Ni, B. (2022). Towards bridging sample complexity and model capacity. In: Proceedings in Conference AAAI, pp. 1972\u20131980.","DOI":"10.1609\/aaai.v36i2.20092"},{"key":"2342_CR41","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 427\u2013436.","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"2342_CR42","doi-asserted-by":"crossref","unstructured":"Park1, S., Hong, Y., Heo, B., Yun, S., & Choi, J.Y. (2022). The majority can help the minority: Context-rich minority oversampling for long-tailed classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 6887\u20136896.","DOI":"10.1109\/CVPR52688.2022.00676"},{"issue":"3","key":"2342_CR43","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"International Journal of Computer Vision"},{"key":"2342_CR44","doi-asserted-by":"crossref","unstructured":"Salman, K., Munawar, H., Waqas, Z.S., Jianbing, S., & Ling, S. (2019). Striking the right balance with uncertainty. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 103\u2013112.","DOI":"10.1109\/CVPR.2019.00019"},{"key":"2342_CR45","doi-asserted-by":"crossref","unstructured":"Samuel, D., & Chechik, G. (2021). Distributional robustness loss for long-tail learning. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 9495\u20139504.","DOI":"10.1109\/ICCV48922.2021.00936"},{"key":"2342_CR46","unstructured":"Saunshi, N., Plevrakis, O., Arora, S., Khodak, M., & Khandeparkar, H. (2019). A theoretical analysis of contrastive unsupervised representation learning. In: International Conference on Machine Learning, pp. 5628\u20135637."},{"key":"2342_CR47","unstructured":"Shah, H., Tamuly, K., Raghunathan, A., Jain, P., & Netrapalli, P. (2020). The pitfalls of simplicity bias in neural networks. In: Advances in Neural Information Processing System, pp. 9573\u20139585."},{"key":"2342_CR48","doi-asserted-by":"crossref","unstructured":"Shen, L., Lin, Z., & Huang, Q. (2016). Relay backpropagation for effective learning of deep convolutional neural networks. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part VII 14, pp. 467\u2013482.","DOI":"10.1007\/978-3-319-46478-7_29"},{"key":"2342_CR49","doi-asserted-by":"crossref","unstructured":"Teney, D., Abbasnejad, E., Lucey, S., & van\u00a0den Hengel, A. (2022). Evading the simplicity bias: Training a diverse set of models discovers solutions with superior OOD generalization. In: Proceedings of the IEEE\/CVF conference on computer vision, pp. 16,761\u201316,772.","DOI":"10.1109\/CVPR52688.2022.01626"},{"key":"2342_CR50","doi-asserted-by":"crossref","unstructured":"Van\u00a0Horn, G., Mac\u00a0Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2018). The iNaturalist species classification and detection dataset. In: Proceedings in IEEE Conference Compter Vison Patterns Recognition, pp. 8769\u20138778.","DOI":"10.1109\/CVPR.2018.00914"},{"key":"2342_CR51","doi-asserted-by":"crossref","unstructured":"Wang P, Han K, Wei XS, Zhang L, Wang L (2021a) 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","DOI":"10.1109\/CVPR46437.2021.00100"},{"key":"2342_CR52","unstructured":"Wang X, Lian L, Miao Z, Liu Z, Yu S (2021b) Long-tailed recognition by routing diverse distribution-aware experts. In: Proceedings in Intetnational Conference Learning Representations, pp 1\u201315"},{"key":"2342_CR53","unstructured":"Wang Y, Zhang Q, Wang Y, Yang J, Lin Z (2022) Chaos is a ladder: A new theoretical understanding of contrastive learning via augmentation overlap. In: Proceedings in Intetnational Conference Learning Representations, pp 1\u201311"},{"key":"2342_CR54","unstructured":"Wang YX, Ramanan D, Hebert M (2017) Learning to model the tail. In: Advances in Neural Information Processing System, pp 7029\u20137039"},{"issue":"12","key":"2342_CR55","doi-asserted-by":"publisher","first-page":"6116","DOI":"10.1109\/TIP.2019.2924811","volume":"28","author":"XS Wei","year":"2019","unstructured":"Wei, X. S., Wang, P., Liu, L., Shen, C., & Wu, J. (2019). Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples. IEEE Transactions on Image Processing, 28(12), 6116\u20136125.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"12","key":"2342_CR56","doi-asserted-by":"publisher","first-page":"8927","DOI":"10.1109\/TPAMI.2021.3126648","volume":"44","author":"XS Wei","year":"2022","unstructured":"Wei, X. S., Song, Y. Z., Aodha, O. M., Wu, J., Peng, Y., Tang, J., Yang, J., & Belongie, S. (2022). Fine-grained image analysis with deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 8927\u20138948.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"6","key":"2342_CR57","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s11432-021-3489-1","volume":"65","author":"XS Wei","year":"2022","unstructured":"Wei, X. S., Xu, S. L., Chen, H., Xiao, L., & Peng, Y. (2022). Prototype-based classifier learning for long-tailed visual recognition. SCIENCE CHINA Information Sciences, 65(6), 160.","journal-title":"SCIENCE CHINA Information Sciences"},{"issue":"11","key":"2342_CR58","doi-asserted-by":"publisher","first-page":"13904","DOI":"10.1109\/TPAMI.2023.3299563","volume":"45","author":"XS Wei","year":"2024","unstructured":"Wei, X. S., Shen, Y., Sun, X., Wang, P., & Peng, Y. (2024). Attribute-aware deep hashing with self-consistency for large-scale fine-grained image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 13904\u201313920.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"2342_CR59","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1109\/TPAMI.2023.3333528","volume":"46","author":"XS Wei","year":"2024","unstructured":"Wei, X. S., Xu, H. Y., Yang, Z., Duan, C. L., & Peng, Y. (2024). Negatives make a positive: An embarrassingly simple approach to semi-supervised few-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(4), 2091\u20132103.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2342_CR60","doi-asserted-by":"crossref","unstructured":"Wu Z, Xiong Y, Yu SX, Lin D (2018) Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3733\u20133742","DOI":"10.1109\/CVPR.2018.00393"},{"key":"2342_CR61","doi-asserted-by":"crossref","unstructured":"Xiang L, Ding G, Han J (2020) Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part V 16, pp 247\u2013263","DOI":"10.1007\/978-3-030-58558-7_15"},{"key":"2342_CR62","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"2342_CR63","unstructured":"Xu Z, Chai Z, Yuan C (2021) Towards calibrated model for long-tailed visual recognition from prior perspective. In: Advances in Neural Information Processing Systems, pp 7139\u20137152"},{"key":"2342_CR64","unstructured":"Zhang Y, Hooi B, Hong L, Feng J (2021) Test-agnostic long-tailed recognition by test-time aggregating diverse experts with self-supervision. arXiv preprint arXiv:2107.09249"},{"key":"2342_CR65","unstructured":"Zhang Y, Hooi B, Hong L, Feng J (2022) Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. In: Advances in Neural Information Processing Systems, pp 34,077\u201334,090"},{"key":"2342_CR66","doi-asserted-by":"crossref","unstructured":"Zhong Z, Cui J, Liu S, Jia J (2021) Improving calibration for long-tailed recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16,489\u201316,498","DOI":"10.1109\/CVPR46437.2021.01622"},{"key":"2342_CR67","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"issue":"6","key":"2342_CR68","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. (2017). Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1452\u20131464.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2342_CR69","doi-asserted-by":"crossref","unstructured":"Zhou B, Cui Q, Wei XS, Chen ZM (2020) 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","DOI":"10.1109\/CVPR42600.2020.00974"},{"issue":"1","key":"2342_CR70","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/TKDE.2006.17","volume":"18","author":"ZH Zhou","year":"2006","unstructured":"Zhou, Z. H., & Liu, X. Y. (2006). Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 18(1), 63\u201377.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2342_CR71","doi-asserted-by":"crossref","unstructured":"Zhu J, Wang Z, Chen J, Chen YPP, Jiang YG (2022) Balanced contrastive learning for long-tailed visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6908\u20136917","DOI":"10.1109\/CVPR52688.2022.00678"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02342-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02342-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02342-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T06:55:08Z","timestamp":1746860108000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02342-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,6]]},"references-count":71,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2342"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02342-x","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,6]]},"assertion":[{"value":"12 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}