Instance-Specific Semantic Augmentation for Long-Tailed Image Classification

Recent long-tailed classification methods generally adopt the two-stage pipeline and focus on learning the classifier to tackle the imbalanced data in the second stage via re-sampling or re-weighting, but the classifier is easily prone to overconfidence in head classes. Data augmentation is a natural way to tackle this issue. Existing augmentation methods either perform low-level transformations or apply the same semantic transformation for all instances. However, meaningful augmentations for different instances should be different. In this paper, we propose feature-level augmentation (FLA) and pixel-level augmentation (PLA) learning methods for long-tailed image classification. In the first stage, the feature space is learned from the original imbalanced data. In the second stage, we model the semantic within-class transformation range for each instance by a specific Gaussian distribution and design a semantic transformation generator (STG) to predict the distribution from the instance itself. We train STG by constructing ground-truth distributions for instances of head classes in the feature space. In the third stage, for FLA, we generate instance-specific transformations by STG to obtain feature augmentations of tail classes for fine-tuning the classifier. For PLA, we use STG to guide pixel-level augmentations for fine-tuning the backbone. The proposed augmentation strategy can be combined with different existing long-tail classification methods. Extensive experiments on five imbalanced datasets show the effectiveness of our method.