[1] Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. “Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss”. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 2019.
[2] Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, et al. "Balanced meta-softmax for long-tailed visual recognition". Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 2020.
[3] Seulki Park, Jongin Lim, Younghan Jeon, and Jin Young Choi. "Influence-balanced loss for imbalanced visual classification". Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021, pp. 735-744.
[4] Youngkyu Hong, Seungju Han, Kwanghee Choi, Seokjun Seo, et al. "Disentangling label distribution for long-tailed visual recognition". Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pp. 6626-6636.
[5] Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, et al. "Meta-weight-net: Learning an explicit mapping for sample weighting". Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 2019.
[6] Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. "Learning to reweight examples for robust deep learning". International conference on machine learning. 2018, pp. 4334-4343.
[7] Dandan Guo, Zhuo Li, Meixi Zheng, He Zhao, et al. "Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification". Advances in Neural Information Processing Systems. 2022, pp. 25517-25530.
[8] Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, et al. "Exploring the limits of weakly supervised pretraining". Proceedings of the European conference on computer vision (ECCV). 2018, pp. 181-196.
[9] Jialian Wu, Liangchen Song, Qian Zhang, Ming Yang, and Junsong Yuan. "Forestdet: Large-vocabulary long-tailed object detection and instance segmentation". IEEE Transactions on Multimedia 24 (2021), pp. 3693-3705.
[10] Xinting Hu, Yi Jiang, Kaihua Tang, Jingyuan Chen, et al. "Learning to segment the tail". Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.
[11] Junran Peng, Xingyuan Bu, Ming Sun, Zhaoxiang Zhang, et al. "Large-scale object detection in the wild from imbalanced multi-labels". Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020, pp. 9709-9718.
[12] Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. "SMOTE: synthetic minority over-sampling technique". Journal of artificial intelligence research 16 (2002), pp. 321-357.
[13] Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski. "A systematic study of the class imbalance problem in convolutional neural networks". Neural networks (2018), pp. 249-259.
[14] Guo Haixiang, Li Yijing, Jennifer Shang, Gu Mingyun, et al. "Learning from class-imbalanced data: Review of methods and applications". Expert systems with applications (2017), pp. 220-239.
[15] Debashree Devi, Biswajit Purkayastha, et al. "Redundancy-driven modified Tomek-link based undersampling: A solution to class imbalance". Pattern Recognition Letters (2017), pp. 3-12.
[16] Haibo He and Edwardo A Garcia. "Learning from imbalanced data". IEEE Transactions on knowledge and data engineering (2009), pp. 1263-1284.
[17] Jaehyung Kim, Jongheon Jeong, and Jinwoo Shin. "M2m: Imbalanced classification via major-to-minor translation". Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020, pp. 13896-13905.
[18] Hongxin Wei, Lue Tao, Renchunzi Xie, Lei Feng, and Bo An. "Open-sampling: Exploring out-of-distribution data for re-balancing long-tailed datasets". International Conference on Machine Learning. 2022, pp. 23615-23630.
[19] Yan Hong, Jianfu Zhang, Zhongyi Sun, and Ke Yan. "Safa: Sample-adaptive feature augmentation for long-tailed image classification". European Conference on Computer Vision. 2022.
[20] Seulki Park, Youngkyu Hong, Byeongho Heo, Sangdoo Yun, and Jin Young Choi. "The majority can help the minority: Context-rich minority oversampling for long-tailed classification". Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.