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.

本文系统总结了深度长尾学习的最新进展，围绕着类别再平衡、信息增强和模块改进三个主要类别对相关方法进行详细探讨，并通过提出的相对准确度评估指标对最先进的方法进行了实证分析，为深度长尾学习的应用和未来研究方向提供了重要的参考。

深度长尾学习综述

Deep Long-Tailed Learning: A Survey

Our work focuses on tackling the challenging but natural visual recognition
task of long-tailed data distribution (i.e., a few classes occupy most of the
data, while most classes have rarely few samples). In the literature, class
re-balancing strategies (e.g., re-weighting and re-sampling) are the prominent
and effective methods proposed to alleviate the extreme imbalance for dealing
with long-tailed problems. In this paper, we firstly discover that these
re-balancing methods achieving satisfactory recognition accuracy owe to that
they could significantly promote the classifier learning of deep networks.
However, at the same time, they will unexpectedly damage the representative
ability of the learned deep features to some extent. Therefore, we propose a
unified Bilateral-Branch Network (BBN) to take care of both representation
learning and classifier learning simultaneously, where each branch does perform
its own duty separately. In particular, our BBN model is further equipped with
a novel cumulative learning strategy, which is designed to first learn the
universal patterns and then pay attention to the tail data gradually. Extensive
experiments on four benchmark datasets, including the large-scale iNaturalist
ones, justify that the proposed BBN can significantly outperform
state-of-the-art methods. Furthermore, validation experiments can demonstrate
both our preliminary discovery and effectiveness of tailored designs in BBN for
long-tailed problems. Our method won the first place in the iNaturalist 2019
large scale species classification competition, and our code is open-source and
available at this https URL

提出了针对长尾数据分布的双边分支网络方法，不仅可以同时关注表示学习和分类器学习，还配备了新颖的渐进积累学习策略，并在四种基准数据集上进行了实验，获得了比现有技术更好的性能。