[21] Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, and Da-Cheng Juan. "Remix: rebalanced mixup". Proceedings of the European conference on computer vision (ECCV). 2020, pp. 95-110.
[22] Zhengzhuo Xu, Zenghao Chai, and Chun Yuan. "Towards calibrated model for long-tailed visual recognition from prior perspective". Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 2021, pp. 7139-7152.
[23] Peng Chu, Xiao Bian, Shaopeng Liu, and Haibin Ling. “Feature space augmentation for long-tailed data”. European Conference on Computer Vision. 2020, pp. 694–710.
[24] Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, et al. "Metasaug: Meta semantic augmentation for long-tailed visual recognition". Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pp. 5212-5221.
[25] Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen. "Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition". Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020, pp. 9719-9728.
[26] Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, et al. "Decoupling representation and classifier for long-tailed recognition". Eighth International Conference on Learning Representations (ICLR). 2020.
[27] Zhisheng Zhong, Jiequan Cui, Shu Liu, and Jiaya Jia. "Improving calibration for long-tailed recognition". Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pp. 16489-16498.
[28] Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella Yu. "Long-tailed Recognition by Routing Diverse Distribution-Aware Experts". International Conference on Learning Representations. 2021.
[29] Kaihua Tang, Jianqiang Huang, and Hanwang Zhang. "Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect". Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 2020.
[30] AFM Shahab Uddin, Sirazam Monira, Wheemyung Shin, TaeChoong Chung, Sung-Ho Bae, et al. "SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization". International Conference on Learning Representations. 2021.
[31] Jie-Neng Chen, Shuyang Sun, Ju He, Philip HS Torr, et al. "Transmix: Attend to mix for vision transformers". Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pp. 12135-12144.
[32] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. "mixup: Beyond Empirical Risk Minimization". International Conference on Learning Representations. 2018.
[33] Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, et al. "Umix: Improving importance weighting for subpopulation shift via uncertainty-aware mixup". Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 2022.
[34] Adrian Galdran, Gustavo Carneiro, and Miguel A González Ballester. "Balanced-mixup for highly imbalanced medical image classification". Medical Image Computing and Computer Assisted Intervention. 2021, pp. 323-333.
[35] Gabriel Peyré and Marco Cuturi. "Computational optimal transport". Foundations and Trends in Machine Learning (2019), pp. 355-607.
[36] Lu Yang, He Jiang, Qing Song, and Jun Guo. “A survey on long-tailed visual recognition”. International Journal of Computer Vision (2022), pp. 1837–1872.
[37] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, et al. "Cutmix: Regularization strategy to train strong classifiers with localizable features". Proceedings of the IEEE/CVF international conference on computer vision. 2019, pp. 6023-6032.
[38] Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, and Yannis Avrithis. "Alignmixup: Improving representations by interpolating aligned features". Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pp. 19174-19183.
[39] Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, and Yu Liu. "Tokenmix: Rethinking image mixing for data augmentation in vision transformers". European Conference on Computer Vision. 2022, pp. 455-471.
[40] Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, and Jian Yang. "Recursiveemix: Mixed learning with history". Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 2022.