The DOI matches its Crossref record; 216 of the 219 references it cites were found in a registry or an index.
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Open accessAbstract
Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge. In this context, we extensively investigated 200+ FSL papers published in top journals and conferences in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL with a fresh perspective and to provide an impartial comparison of the strengths and weaknesses of existing work. To avoid conceptual confusion, we first elaborate and contrast a set of relevant concepts including few-shot learning, transfer learning, and meta-learning. Then, we inventively extract prior knowledge related to few-shot learning in the form of a pyramid, which summarizes and classifies previous work in detail from the perspective of challenges. Furthermore, to enrich this survey, we present in-depth analysis and insightful discussions of recent advances in each subsection. What is more, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into technology trends and potential future research opportunities to guide FSL follow-up research.
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“This gap can result in overfitting, where the model excels on training data but underperforms on testing data, compromising its generalization ability [27, 39].”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [39]
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Metadata
- Authors
- Song, Y., Wang, T., Cai, P., Mondal, S. K., & Sahoo, J. P.
- Affiliations
- East China Normal University (CN)
- Michigan State University (US)
- Macau University of Science and Technology (MO)
- Siksha O Anusandhan University (IN)
- Venue
- ACM Computing Surveys
- Published
- 2023-07-13
- Volume
- 55
- Issue
- 13s
- Pages
- 1-40
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4319163914
- Cited by
- 721
- References
- 219
- Citations by year
- 2026: 190 · 2025: 309 · 2024: 178 · 2023: 43
- FWCI
- 67.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Computing Surveys (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Key R&D Program of China (2021ZD0114600)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- few-shot learning, transfer learning, meta-learning, few-shot classification, computer vision applications, research trends, future research directions
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