  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.
