Long-tail learning has received significant attention in recent years due to the challenge it poses with extremely imbalanced datasets. In these datasets, only a few classes (known as the head classes) have an adequate number of training samples, while the rest of the classes (known as the tail classes) are infrequent in the training data. Re-sampling is a classical and widely used approach for addressing class imbalance issues. Unfortunately, recent studies claim that re-sampling brings negligible performance improvements in modern long-tail learning tasks. This paper aims to investigate this phenomenon systematically. Our research shows that re-sampling can considerably improve generalization when the training images do not contain semantically irrelevant contexts. In other scenarios, however, it can learn unexpected spurious correlations between irrelevant contexts and target labels. We design experiments on two homogeneous datasets, one containing irrelevant context and the other not, to confirm our findings. To prevent the learning of spurious correlations, we propose a new context shift augmentation module that generates diverse training images for the tail class by maintaining a context bank extracted from the head-class images. Experiments demonstrate that our proposed module can boost the generalization and outperform other approaches, including class-balanced re-sampling, decoupled classifier re-training, and data augmentation methods. The source code is available at https://www.lamda.nju.edu.cn/code_CSA.ashx.

研究表明，在现代长尾学习任务中，重新取样可以极大地提高泛化性能，但也可能学习到与目标标签无关的错误关联。为了防止学习到错误关联，本文提出了一种新的上下文转移增强模块，通过维护从头类别图像中提取的上下文库为尾类别生成多样化的训练图像。实验证明，我们提出的模块能够提升泛化性能，并优于其他方法，包括类别平衡重新取样、解耦分类器重新训练和数据增强方法。

如何通过重新采样改善长尾学习？