mlsanityData to 7 Oct 2026

Fine-Grained Visual Classification of Aircraft

arXiv 1306.5151 ↗First posted 21 Jun 2013cs.CVcited by 1,701

Not on the map. Why?

Cited by 546 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 1,701 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2013.

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Citing papers per year: 2021: 135; 2022: 209; 2023: 313; 2024: 392; 2025: 395; 2026: 257.

1,701 papers.

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