  In this work, we tackle the challenging problem of long-tailed image
recognition. Previous long-tailed recognition approaches mainly focus on data
augmentation or re-balancing strategies for the tail classes to give them more
attention during model training. However, these methods are limited by the
small number of training images for the tail classes, which results in poor
feature representations. To address this issue, we propose the Latent
Categories based long-tail Recognition (LCReg) method. Our hypothesis is that
common latent features shared by head and tail classes can be used to improve
feature representation. Specifically, we learn a set of class-agnostic latent
features shared by both head and tail classes, and then use semantic data
augmentation on the latent features to implicitly increase the diversity of the
training sample. We conduct extensive experiments on five long-tailed image
recognition datasets, and the results show that our proposed method
significantly improves the baselines.
