From Nov 2023 to Oct 2026, 178 archived papers mentioned Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers) in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in May 2025 (12).
Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers) was mentioned in 5 papers in the last 30 days (2026-10-06 to 2026-11-04), ranking #68 of the 255 datasets mentioned.
By journal category: AI (5).
Most of them appeared in International Journal of Computer Vision (2), Neural Networks (2) and Machine Learning (1).
From 2017 to 2026 (so far), Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers) was mentioned in 229 papers in the tracked journals, most of them in AI (229).
55 of them were published in 2025, against 2 in 2017.
Since 2023, the institutions with the most of these papers were Chinese Academy of Sciences (17), Tsinghua University (13) and Nanyang Technological University (11).
The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (44), International Journal of Computer Vision (41) and Pattern Recognition (19).
The most-cited of them is “Vision-Language Models for Vision Tasks: A Survey” (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024), with 998 citations.
Papers use Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers) most often with ImageNet (162), CIFAR-10 / CIFAR-100 (104) and MS COCO (41); it has 28 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.
Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)
Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.
Papers use Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers) most often with ImageNet (162), CIFAR-10 / CIFAR-100 (104) and MS COCO (41); it has 28 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST. The whole network →
What papers use Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers) together with (at least 3 papers; lift 1 = chance)
Ulindu De Silva, Didula Samaraweera, Sasini Wanigathunga et al.
University of Moratuwa; Stony Brook University; Khalifa University of Science and Technology
Cite this page
Scholar4AI. (2026). Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers). Journal and Conference Research Rankings. Retrieved November 4, 2026, from https://scholar4ai.com/datasets/fine-grained-benchmarks-cub-200-stanford-cars-oxford-flowers
@misc{scholar4ai-datasets-fine-grained-benchmarks-cub-200-stanford-cars-oxford-flowers,
author = {{Scholar4AI}},
title = {Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers)},
year = {2026},
howpublished = {\url{https://scholar4ai.com/datasets/fine-grained-benchmarks-cub-200-stanford-cars-oxford-flowers}},
urldate = {2026-11-04},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).