  Deep long-tailed learning, one of the most challenging problems in visual
recognition, aims to train well-performing deep models from a large number of
images that follow a long-tailed class distribution. In the last decade, deep
learning has emerged as a powerful recognition model for learning high-quality
image representations and has led to remarkable breakthroughs in generic visual
recognition. However, long-tailed class imbalance, a common problem in
practical visual recognition tasks, often limits the practicality of deep
network based recognition models in real-world applications, since they can be
easily biased towards dominant classes and perform poorly on tail classes. To
address this problem, a large number of studies have been conducted in recent
years, making promising progress in the field of deep long-tailed learning.
Considering the rapid evolution of this field, this paper aims to provide a
comprehensive survey on recent advances in deep long-tailed learning. To be
specific, we group existing deep long-tailed learning studies into three main
categories (i.e., class re-balancing, information augmentation and module
improvement), and review these methods following this taxonomy in detail.
Afterward, we empirically analyze several state-of-the-art methods by
evaluating to what extent they address the issue of class imbalance via a newly
proposed evaluation metric, i.e., relative accuracy. We conclude the survey by
highlighting important applications of deep long-tailed learning and
identifying several promising directions for future research.
