Real-world data often follow a long-tailed distribution with a high imbalance
in the number of samples between classes. The problem with training from
imbalanced data is that some background features, common to all classes, can be
unobserved in classes with scarce samples. As a result, this background
correlates to biased predictions into ``major" classes. In this paper, we
propose saliency masked contrastive learning, a new method that uses saliency
masking and contrastive learning to mitigate the problem and improve the
generalizability of a model. Our key idea is to mask the important part of an
image using saliency detection and use contrastive learning to move the masked
image towards minor classes in the feature space, so that background features
present in the masked image are no longer correlated with the original class.
Experiment results show that our method achieves state-of-the-art level
performance on benchmark long-tailed datasets.

本文提出了一种新的方法，利用显著性遮罩和对比学习来减轻训练不平衡数据的问题并提高模型的泛化能力，在特征空间中将遮罩图像移向次要类别，以减少与原始类别相关的背景特征。实验证明，我们的方法在基准长尾数据集上获得了最先进的性能水平。