---
title: "Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford…"
description: "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)."
url: https://scholar4ai.com/datasets/fine-grained-benchmarks-cub-200-stanford-cars-oxford-flowers
markdown_url: https://scholar4ai.com/datasets/fine-grained-benchmarks-cub-200-stanford-cars-oxford-flowers.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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… (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers)

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/3.md)

Labelled image collections used to train and compare image classifiers.

- data category: Image classification benchmarks
- 0 papers in the last 7 days
- Names searched for: CUB-200, Caltech-UCSD Birds, Stanford Cars, Oxford Flowers, Oxford-IIIT Pet, FGVC-Aircraft

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Image classification benchmarks datasets: https://scholar4ai.com/datasets/image-classification-benchmarks.md



## Key findings

- 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 (2026-10-07 to 2026-11-05), ranking #64 of the 246 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.

## Mentions over 36 months

*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)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 71 | 56 | 51 | 178 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 2 | 3 | 2 | 3 | 4 | 12 | 39 | 64 | 55 | 45 |
| All categories | 2 | 3 | 2 | 3 | 4 | 12 | 39 | 64 | 55 | 45 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 44 |
| 2 | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 41 |
| 3 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 19 |
| 4 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 18 |
| 5 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 15 |
| 6 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 11 |
| 7 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 7 |
| 8 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 7 |
| 9 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 7 |
| 10 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 7 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 17 |
| 2 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 13 |
| 3 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 11 |
| 4 | [Nanjing University](https://scholar4ai.com/institutions/nanjing-university.md) | 10 |
| 5 | [University College London](https://scholar4ai.com/institutions/university-college-london.md) | 8 |
| 6 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 8 |
| 7 | [Institute of Automation](https://scholar4ai.com/institutions/institute-of-automation.md) | 7 |
| 8 | [Tencent (China)](https://scholar4ai.com/companies/tencent-china.md) | 7 |
| 9 | [Beijing University of Posts and Telecommunications](https://scholar4ai.com/institutions/beijing-university-of-posts-and-telecommunications.md) | 6 |
| 10 | Lanzhou University of Technology | 6 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 111 |
| 2 | United States | 26 |
| 3 | United Kingdom | 24 |
| 4 | Australia | 21 |
| 5 | Singapore | 19 |
| 6 | Hong Kong | 17 |
| 7 | South Korea | 14 |
| 8 | United Arab Emirates | 7 |
| 9 | Germany | 7 |
| 10 | Netherlands | 7 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [image classification](https://scholar4ai.com/keywords/image-classification.md) | 24 |
| 2 | [catastrophic forgetting](https://scholar4ai.com/keywords/catastrophic-forgetting.md) | 23 |
| 3 | [vision transformer](https://scholar4ai.com/keywords/vision-transformer.md) | 23 |
| 4 | continual learning | 17 |
| 5 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 17 |
| 6 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 17 |
| 7 | [overfitting](https://scholar4ai.com/keywords/overfitting.md) | 14 |
| 8 | [few-shot learning](https://scholar4ai.com/keywords/few-shot-learning.md) | 13 |
| 9 | fine-grained image classification | 11 |
| 10 | [self-supervised learning](https://scholar4ai.com/keywords/self-supervised-learning.md) | 11 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 998 | 30.9 | [Vision-Language Models for Vision Tasks: A Survey](https://doi.org/10.1109/tpami.2024.3369699) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-02-26 | Jingyi Zhang, Jiaxing Huang, Sheng Jin et al. |
| 2 | 495 | 14.0 | [A comprehensive survey on applications of transformers for deep learning tasks](https://doi.org/10.1016/j.eswa.2023.122666) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2023-11-22 | Saidul Islam, Hanae Elmekki, Ahmed Elsebai et al. |
| 3 | 456 | 12.3 | [A survey of the vision transformers and their CNN-transformer based variants](https://doi.org/10.1007/s10462-023-10595-0) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2023-10-03 | Asifullah Khan, Asifullah Khan, Zunaira Rauf et al. |
| 4 | 377 | 13.6 | [Class-Incremental Learning: A Survey](https://doi.org/10.1109/tpami.2024.3429383) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-07-16 | Da-Wei Zhou, Qiwei Wang, Zhi-Hong Qi et al. |
| 5 | 330 | 2.9 | [Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition](https://doi.org/10.1109/tpami.2017.2723400) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2017-07-04 | Tsung‐Yu Lin, Aruni RoyChowdhury, Subhransu Maji |

## Used together with

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.

*What papers use Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers) together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 162 | 14.4 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 104 | 15.7 |
| [MS COCO](https://scholar4ai.com/datasets/ms-coco.md) | Dataset | 41 | 9.2 |
| [Caltech-101 / 256](https://scholar4ai.com/datasets/caltech-101-256.md) | Dataset | 33 | 46.6 |
| [PASCAL VOC](https://scholar4ai.com/datasets/pascal-voc.md) | Dataset | 30 | 18.8 |
| [MNIST / Fashion-MNIST](https://scholar4ai.com/datasets/mnist-fashion-mnist.md) | Dataset | 29 | 5.5 |
| [SVHN](https://scholar4ai.com/datasets/svhn.md) | Dataset | 19 | 19.0 |
| [Action recognition (Kinetics, UCF101, HMDB51)](https://scholar4ai.com/datasets/action-recognition-kinetics-ucf101-hmdb51.md) | Dataset | 17 | 12.2 |
| [Face recognition (LFW, CelebA, VGGFace2)](https://scholar4ai.com/datasets/face-recognition-lfw-celeba-vggface2.md) | Dataset | 16 | 11.3 |
| [Domain adaptation benchmarks (Office-31, Office-Home, VisDA…](https://scholar4ai.com/datasets/domain-adaptation-benchmarks-office-31-office-home-visda-domainnet.md) | Dataset | 12 | 15.4 |
| [ADE20K](https://scholar4ai.com/datasets/ade20k.md) | Dataset | 11 | 16.3 |
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 9 | 4.0 |

The whole network: https://scholar4ai.com/code/network.md

## Papers

*Papers mentioning Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers) in the last 30 days, newest first.*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-26 | [TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation](https://doi.org/10.1007/s11263-026-03081-x) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Haokun Lin, Teng Wang, Yixiao Ge et al. | City University of Hong Kong; Chinese Academy of Sciences; Tencent (China); … |
| 2026-10-23 | [A Lagrangian-inspired force-based loss for deep metric learning](https://doi.org/10.1016/j.neunet.2026.109798) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Byung Do Cheon, Jeongmin Yu, Jung-Eun Kim et al. | Yonsei University |
| 2026-10-20 | [Weighted UnLearning from Experience for Improved Spurious Correlation Robustness](https://doi.org/10.1007/s10994-026-07164-1) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Jeff Mitchell, Jesus Martinez-del-Rincon, Niall McLaughlin | Queen's University Belfast |
| 2026-10-17 | [CorrCLIPv2: Harnessing Unlabeled Images for Open-Vocabulary Semantic Segmentation](https://doi.org/10.1007/s11263-026-03027-3) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | D H Zhang, Fagui Liu, Quan Tang et al. | South China University of Technology; Peng Cheng Laboratory |
| 2026-10-09 | [Test-time optimization for domain adaptive open vocabulary segmentation](https://doi.org/10.1016/j.neunet.2026.109689) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 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 5, 2026, from https://scholar4ai.com/datasets/fine-grained-benchmarks-cub-200-stanford-cars-oxford-flowers

```bibtex
@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 = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).