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。

尾部蕴藏玄机：野外细粒度分类