  In this work, we address the challenging task of long-tailed image
recognition. Previous long-tailed recognition methods commonly focus on the
data augmentation or re-balancing strategy of the tail classes to give more
attention to tail classes during the model training. However, due to the
limited training images for tail classes, the diversity of tail class images is
still restricted, which results in poor feature representations. In this work,
we hypothesize that common latent features among the head and tail classes can
be used to give better feature representation. Motivated by this, we introduce
a Latent Categories based long-tail Recognition (LCReg) method. Specifically,
we propose to learn a set of class-agnostic latent features shared among the
head and tail classes. Then, we implicitly enrich the training sample diversity
via applying semantic data augmentation to the latent features. Extensive
experiments on five long-tailed image recognition datasets demonstrate that our
proposed LCReg is able to significantly outperform previous methods and achieve
state-of-the-art results.
