Real-world data often follow a long-tailed distribution as the frequency of
each class is typically different. For example, a dataset can have a large
number of under-represented classes and a few classes with more than sufficient
data. However, a model to represent the dataset is usually expected to have
reasonably homogeneous performances across classes. Introducing class-balanced
loss and advanced methods on data re-sampling and augmentation are among the
best practices to alleviate the data imbalance problem. However, the other part
of the problem about the under-represented classes will have to rely on
additional knowledge to recover the missing information.
In this work, we present a novel approach to address the long-tailed problem
by augmenting the under-represented classes in the feature space with the
features learned from the classes with ample samples. In particular, we
decompose the features of each class into a class-generic component and a
class-specific component using class activation maps. Novel samples of
under-represented classes are then generated on the fly during training stages
by fusing the class-specific features from the under-represented classes with
the class-generic features from confusing classes. Our results on different
datasets such as iNaturalist, ImageNet-LT, Places-LT and a long-tailed version
of CIFAR have shown the state of the art performances.

本文提出了一种新方法，通过在特征空间中使用来自具有丰富样本的类别所学习的特征来增强特征空间中的欠表示类别，从而解决长尾分布问题。通过使用类别激活映射，将每个类别的特征分解为类别通用和类别特定部分，并将欠表示类别的类别特定特征与混淆类别的类别通用特征融合，实时生成新样本。结果表明，在 iNaturalist、ImageNet-LT、Places-LT 以及 CIFAR 的长尾版本等数据集上，本文所提出的方法展现了最先进的表现水平。