  In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle
long-tailed recognition. Based on theoretical analysis, we observe supervised
contrastive loss tends to bias on high-frequency classes and thus increases the
difficulty of imbalanced learning. We introduce a set of parametric class-wise
learnable centers to rebalance from an optimization perspective. Further, we
analyze our PaCo loss under a balanced setting. Our analysis demonstrates that
PaCo can adaptively enhance the intensity of pushing samples of the same class
close as more samples are pulled together with their corresponding centers and
benefit hard example learning. Experiments on long-tailed CIFAR, ImageNet,
Places, and iNaturalist 2018 manifest the new state-of-the-art for long-tailed
recognition. On full ImageNet, models trained with PaCo loss surpass supervised
contrastive learning across various ResNet backbones, e.g., our ResNet-200
achieves 81.8% top-1 accuracy. Our code is available at
https://github.com/dvlab-research/Parametric-Contrastive-Learning.
