Label-Occurrence-Balanced Mixup for Long-tailed Recognition

TL;DR

This paper introduces Label-Occurrence-Balanced Mixup, a data augmentation technique designed to address label imbalance in long-tailed recognition tasks, significantly improving performance on vision and sound benchmarks.

Contribution

It proposes a novel mixup method that maintains balanced label occurrence using class-balanced samplers, enhancing long-tailed data learning.

Findings

Improves mixup effectiveness on imbalanced datasets

Achieves superior results on vision and sound benchmarks

Addresses label suppression in long-tailed recognition

Abstract

Mixup is a popular data augmentation method, with many variants subsequently proposed. These methods mainly create new examples via convex combination of random data pairs and their corresponding one-hot labels. However, most of them adhere to a random sampling and mixing strategy, without considering the frequency of label occurrence in the mixing process. When applying mixup to long-tailed data, a label suppression issue arises, where the frequency of label occurrence for each class is imbalanced and most of the new examples will be completely or partially assigned with head labels. The suppression effect may further aggravate the problem of data imbalance and lead to a poor performance on tail classes. To address this problem, we propose Label-Occurrence-Balanced Mixup to augment data while keeping the label occurrence for each class statistically balanced. In a word, we employ two independent class-balanced samplers to select data pairs and mix them to generate new data. We test our method on several long-tailed vision and sound recognition benchmarks. Experimental results show that our method significantly promotes the adaptability of mixup method to imbalanced data and achieves superior performance compared with state-of-the-art long-tailed learning methods.