  The problem of class imbalanced data is that the generalization performance
of the classifier deteriorates due to the lack of data from minority classes.
In this paper, we propose a novel minority over-sampling method to augment
diversified minority samples by leveraging the rich context of the majority
classes as background images. To diversify the minority samples, our key idea
is to paste an image from a minority class onto rich-context images from a
majority class, using them as background images. Our method is simple and can
be easily combined with the existing long-tailed recognition methods. We
empirically prove the effectiveness of the proposed oversampling method through
extensive experiments and ablation studies. Without any architectural changes
or complex algorithms, our method achieves state-of-the-art performance on
various long-tailed classification benchmarks. Our code is made available at
https://github.com/naver-ai/cmo.
