Caltech-101 / 256

data category: Image classification benchmarks · 0 papers in the last 7 days

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Labelled image collections used to train and compare image classifiers.

Names searched for: Caltech-101, Caltech-256, Caltech101, Caltech256

Key findings#

From Nov 2023 to Oct 2026, 116 archived papers mentioned Caltech-101 / 256 in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Aug 2024 (7).
Caltech-101 / 256 was mentioned in 2 papers in the last 30 days (2026-10-06 to 2026-11-04), ranking #133 of the 255 datasets mentioned.
By journal category: AI (2).
Most of them appeared in International Journal of Computer Vision (1) and Information Processing & Management (1).
From 2017 to 2026 (so far), Caltech-101 / 256 was mentioned in 147 papers in the tracked journals, most of them in AI (145), information systems (1) and operations research (1).
32 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 (7), Chinese University of Hong Kong (6) and Institute of Automation (6).
The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (25), International Journal of Computer Vision (18) and IEEE Transactions on Neural Networks and Learning Systems (14).
The most-cited of them is “A review of convolutional neural networks in computer vision” (Artificial Intelligence Review, 2024), with 1,187 citations.
Papers use Caltech-101 / 256 most often with ImageNet (91), CIFAR-10 / CIFAR-100 (67) and MNIST / Fashion-MNIST (50); it has 25 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
	42	38	36	116

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026 (so far)
AI	2	5	3	3	5	2	15	47	30	33
Information Systems	0	0	0	0	0	0	0	0	1	0
Operations Research	0	0	0	0	0	0	0	0	1	0
All categories	2	5	3	3	5	2	15	47	32	33

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	IEEE Transactions on Pattern Analysis and Machine Intelligence	25
2	International Journal of Computer Vision	18
3	IEEE Transactions on Neural Networks and Learning Systems	14
4	Artificial Intelligence Review	10
5	Machine Learning	8
6	Pattern Recognition	7
7	Knowledge-Based Systems	7
8	Neural Networks	6
9	Applied Intelligence	4
10	Neural Computing and Applications	4

Institutions with the most papers, since 2023
#	Institution	Papers
1	Chinese Academy of Sciences	7
2	Chinese University of Hong Kong	6
3	Institute of Automation	6
4	Tsinghua University	6
5	University of Electronic Science and Technology of China	6
6	Zhejiang University	6
7	Hong Kong University of Science and Technology	5
8	Harbin Institute of Technology	4
9	Nanjing University	4
10	University of Chinese Academy of Sciences	4

Countries with the most papers, since 2023
#	Country	Papers
1	China	72
2	Hong Kong	18
3	United States	18
4	Australia	17
5	United Kingdom	12
6	South Korea	8
7	Singapore	6
8	Germany	5
9	France	5
10	India	5

Keywords with the most papers, since 2023
#	Keyword	Papers
1	image classification	20
2	vision-language models	16
3	CLIP	14
4	transfer learning	12
5	domain generalization	11
6	domain adaptation	9
7	catastrophic forgetting	8
8	domain shift	8
9	spiking neural networks	8
10	convolutional neural networks	7

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	1,187	37.8	A review of convolutional neural networks in computer vision	Artificial Intelligence Review	2024-03-23	Xia Zhao, Limin Wang, Yufei Zhang et al.
2	998	30.9	Vision-Language Models for Vision Tasks: A Survey	IEEE Transactions on Pattern Analysis and Machine Intelligence	2024-02-26	Jingyi Zhang, Jiaxing Huang, Sheng Jin et al.
3	613	18.5	Autoencoders and their applications in machine learning: a survey	Artificial Intelligence Review	2024-02-03	Kamal Berahmand, Fatemeh Daneshfar, Elaheh Sadat Salehi et al.
4	254	3.0	Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification	IEEE Transactions on Pattern Analysis and Machine Intelligence	2019-11-01	Yanting Pei, Yaping Huang, Qi Zou et al.
5	243	8.6	Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark	IEEE Transactions on Pattern Analysis and Machine Intelligence	2024-06-25	Wenke Huang, Mang Ye, Zekun Shi et al.

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.

Used together with#

Papers use Caltech-101 / 256 most often with ImageNet (91), CIFAR-10 / CIFAR-100 (67) and MNIST / Fashion-MNIST (50); it has 25 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 Caltech-101 / 256 together with (at least 3 papers; lift 1 = chance)
Used with	Type	Papers using both	Lift
ImageNet	Dataset	91	12.5
CIFAR-10 / CIFAR-100	Dataset	67	15.6
MNIST / Fashion-MNIST	Dataset	50	14.6
Fine-grained benchmarks (CUB-200, Stanford Cars, Oxford Flowers)	Dataset	33	46.6
PASCAL VOC	Dataset	29	28.1
SVHN	Dataset	27	41.8
Action recognition (Kinetics, UCF101, HMDB51)	Dataset	24	26.8
Domain adaptation benchmarks (Office-31, Office-Home, VisDA…	Dataset	24	47.6
MS COCO	Dataset	22	7.7
Face recognition (LFW, CelebA, VGGFace2)	Dataset	7	7.7
Cityscapes	Dataset	6	6.5
Kaggle	Dataset	6	3.4

Papers mentioning Caltech-101 / 256 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	International Journal of Computer Vision	Haokun Lin, Teng Wang, Yixiao Ge et al.	City University of Hong Kong; Chinese Academy of Sciences; Tencent (China); …
2026-10-15	Frequency–spatial dual-domain imperceptible watermarking for robust image datasets copyright protection	Information Processing & Management	Xiaojie Du, Yedi Chen, Kai Ma et al.	Guizhou Normal University

Cite this page

Scholar4AI. (2026). Caltech-101 / 256. Journal and Conference Research Rankings. Retrieved November 4, 2026, from https://scholar4ai.com/datasets/caltech-101-256

@misc{scholar4ai-datasets-caltech-101-256,
  author = {{Scholar4AI}},
  title = {Caltech-101 / 256},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/caltech-101-256}},
  urldate = {2026-11-04},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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