Fine-Grained Visual Classification of Aircraft
Fine-Grained Visual Classification of Aircraft
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发表时间：
2013-06
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通讯作者：
Subhransu Maji;Esa Rahtu;Juho Kannala;Matthew B. Blaschko;A. Vedaldi
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作者：
Subhransu Maji;Esa Rahtu;Juho Kannala;Matthew B. Blaschko;A. Vedaldi
本文介绍了FGVC-Aircraft，这是一个新的数据集，包含10，000张飞机图像，跨越100个飞机模型，以三级层次结构组织。在更精细的层面上，模型之间的差异往往很细微，但总是可以在视觉上测量，这使得视觉识别具有挑战性，但也是可能的。通过定义相应的分类任务和评估协议，得到了一个基准，并给出了基准结果。这个数据集的构建是通过飞机爱好者的工作实现的，这一策略可以扩展到其他对象类的研究。与细粒度视觉分类（FGVC）中通常考虑的域（例如动物）相比，飞机是刚性的，因此不易变形。然而，它们呈现出其他有趣的变化模式，包括目的、大小、名称、结构、历史风格和品牌。
This paper introduces FGVC-Aircraft, a new dataset containing 10,000 images of aircraft spanning 100 aircraft models, organised in a three-level hierarchy. At the finer level, differences between models are often subtle but always visually measurable, making visual recognition challenging but possible. A benchmark is obtained by defining corresponding classification tasks and evaluation protocols, and baseline results are presented. The construction of this dataset was made possible by the work of aircraft enthusiasts, a strategy that can extend to the study of number of other object classes. Compared to the domains usually considered in fine-grained visual classification (FGVC), for example animals, aircraft are rigid and hence less deformable. They, however, present other interesting modes of variation, including purpose, size, designation, structure, historical style, and branding.