BSDA in Visual Recognition: Balanced Semantic Data Augmentation for Long-Tailed Data

Deep convolutional neural networks have shown remarkable success in many visual recognition tasks, but they struggle with long-tailed datasets commonly encountered in natural world datasets. In such datasets, the greatest amount of data belongs to majority classes (head classes) with significantly less data for the minority (tail) classes. The class imbalance greatly reduces the performance of traditional networks. Existing methods use re-sampling and re-weighting strategies as the primary methods to deal with these long-tailed datasets. We address the problem using a data augmentation approach inspired by a recently proposed implicit semantic data augmentation (ISDA) algorithm. In this paper, we propose an approach using dual branches with balanced semantic data augmentation (BSDA) to perform effective semantic data augmentation for long-tail recognition by learning good representations. The deep feature branch learns good representations of inputs, and the classifier branch searches for meaningful semantic transformations, with different sampling strategies in each branch. Both branches share the same weights, with focus shifting from one to the other during training. We also propose an augment coefficient to apply different augmentation intensities for different categories. We test our methods on the CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018 datasets. The results demonstrate the effectiveness of our methods.