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Preprints, Working Papers, ...

Fine-Grained Visual Classification of Aircraft

Abstract : 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.
Document type :
Preprints, Working Papers, ...
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Contributor : Matthew Blaschko Connect in order to contact the contributor
Submitted on : Sunday, July 7, 2013 - 5:54:23 PM
Last modification on : Friday, January 21, 2022 - 3:01:28 AM

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  • HAL Id : hal-00842101, version 1
  • ARXIV : 1306.5151



Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, Andrea Vedaldi. Fine-Grained Visual Classification of Aircraft. 2013. ⟨hal-00842101⟩



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