The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification
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The problem of class imbalanced data lies in that the generalization performance of the classifier is deteriorated due to the lack of data of the 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 a foreground patch from a minority class to a background image from a majority class having affluent contexts. 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 will be publicly available at link.
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