  The heavy reliance on data is one of the major reasons that currently limit
the development of deep learning. Data quality directly dominates the effect of
deep learning models, and the long-tailed distribution is one of the factors
affecting data quality. The long-tailed phenomenon is prevalent due to the
prevalence of power law in nature. In this case, the performance of deep
learning models is often dominated by the head classes while the learning of
the tail classes is severely underdeveloped. In order to learn adequately for
all classes, many researchers have studied and preliminarily addressed the
long-tailed problem. In this survey, we focus on the problems caused by
long-tailed data distribution, sort out the representative long-tailed visual
recognition datasets and summarize some mainstream long-tailed studies.
Specifically, we summarize these studies into ten categories from the
perspective of representation learning, and outline the highlights and
limitations of each category. Besides, we have studied four quantitative
metrics for evaluating the imbalance, and suggest using the Gini coefficient to
evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we
quantitatively study 20 widely-used and large-scale visual datasets proposed in
the last decade, and find that the long-tailed phenomenon is widespread and has
not been fully studied. Finally, we provide several future directions for the
development of long-tailed learning to provide more ideas for readers.
