  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.
