The world is long-tailed. What does this mean for computer vision and visual
recognition? The main two implications are (1) the number of categories we need
to consider in applications can be very large, and (2) the number of training
examples for most categories can be very small. Current visual recognition
algorithms have achieved excellent classification accuracy. However, they
require many training examples to reach peak performance, which suggests that
long-tailed distributions will not be dealt with well. We analyze this question
in the context of eBird, a large fine-grained classification dataset, and a
state-of-the-art deep network classification algorithm. We find that (a) peak
classification performance on well-represented categories is excellent, (b)
given enough data, classification performance suffers only minimally from an
increase in the number of classes, (c) classification performance decays
precipitously as the number of training examples decreases, (d) surprisingly,
transfer learning is virtually absent in current methods. Our findings suggest
that our community should come to grips with the question of long tails.

这篇论文研究了长尾分布对计算机视觉和视觉识别的影响，通过对大规模细粒度分类数据集和深度学习分类算法的分析，发现训练样本数量对分类性能的影响非常大，迁移学习在当前方法中表现欠佳，因此研究人员建议应对长尾分布的问题进行全面思考。