  There is growing interest in the challenging visual perception task of
learning from long-tailed class distributions. The extreme class imbalance in
the training dataset biases the model to prefer recognizing majority class data
over minority class data. Furthermore, the lack of diversity in minority class
samples makes it difficult to find a good representation. In this paper, we
propose an effective data augmentation method, referred to as bilateral mixup
augmentation, which can improve the performance of long-tailed visual
recognition. The bilateral mixup augmentation combines two samples generated by
a uniform sampler and a re-balanced sampler and augments the training dataset
to enhance the representation learning for minority classes. We also reduce the
classifier bias using class-wise temperature scaling, which scales the logits
differently per class in the training phase. We apply both ideas to the
dual-branch network (DBN) framework, presenting a new model, named dual-branch
network with bilateral mixup (DBN-Mix). Experiments on popular long-tailed
visual recognition datasets show that DBN-Mix improves performance
significantly over baseline and that the proposed method achieves
state-of-the-art performance in some categories of benchmarks.
