[18] A. Gupta, P. Dollar, and R. Girshick. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5356-5364, 2019.
[19] H. He and E. A. Garcia. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21(9):1263-1284, 2009.
[20] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[21] W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118-22133, 2020.
[22] M. A. Jamal, M. Brown, M.-H. Yang, L. Wang, and B. Gong. Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7610-7619, 2020.
[23] H. Janati, M. Cuturi, and A. Gramfort. Debiased sinkhorn barycenters. In International Conference on Machine Learning, pages 4692-4701. PMLR, 2020.
[24] E. Jang, S. Gu, and B. Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016.
[25] B. Kang, S. Xie, M. Rohrbach, Z. Yan, A. Gordo, J. Feng, and Y. Kalantidis. Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217, 2019.
[26] L. Kantorovich. On the transfer of masses (in russian). In Doklady Akademii Nauk, volume 37, pages 227-229, 1942.
[27] P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan. Supervised contrastive learning. Advances in neural information processing systems, 33:18661-18673, 2020.
[28] J. Kim, J. Jeong, and J. Shin. M2m: Imbalanced classification via major-to-minor translation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13896-13905, 2020.
[29] J. J. Kosowsky and A. L. Yuille. The invisible hand algorithm: Solving the assignment problem with statistical physics. Neural networks, 7(3):477-490, 1994.
[30] A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[31] C. Léonard. From the schrödinger problem to the monge-kantorovich problem. Journal of Functional Analysis, 262(4):1879-1920, 2012.
[32] J. Li, P. Zhou, C. Xiong, and S. C. Hoi. Prototypical contrastive learning of unsupervised representations. arXiv preprint arXiv:2005.04966, 2020.
[33] R. Li, X. Ye, H. Zhou, and H. Zha. Learning to match via inverse optimal transport. Journal of machine learning research, 20, 2019.
[34] Y. Li, Y. Mo, L. Shi, and J. Yan. Improving generative adversarial networks via adversarial learning in latent space. Advances in Neural Information Processing Systems, 35:8868-8881, 2022.
[35] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dólár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980-2988, 2017.
[36] J. Liu, Y. Sun, C. Han, Z. Dou, and W. Li. Deep representation learning on long-tailed data: A learnable embedding augmentation perspective. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2970-2979, 2020.