The DOI matches its Crossref record; 128 of the 134 references it cites were found in a registry or an index.
Deep metric learning for few-shot image classification: A Review of recent developments
Open accessAbstract
Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images. One main solution to few-shot image classification is deep metric learning. These methods, by classifying unseen samples according to their distances to few seen samples in an embedding space learned by powerful deep neural networks, can avoid overfitting to few training images in few-shot image classification and have achieved the state-of-the-art performance. In this paper, we provide an up-to-date review of deep metric learning methods for few-shot image classification from 2018 to 2022 and categorize them into three groups according to three stages of metric learning, namely learning feature embeddings, learning class representations, and learning distance measures. With this taxonomy, we identify the novelties of different methods and problems they face. We conclude this review with a discussion on current challenges and future trends in few-shot image classification.
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“It has found extensive application in FSL [78].”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [78]
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Cited by
Feng, Y., Chen, J., Xie, J., Zhang, T., Lv, H., & Pan, T. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107646 · Cited by 262
Fu, H., Sun, G., Zhang, L., Zhang, A., Ren, J., Jia, X., & Li, F. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.07.013 · Cited by 94
Li, X., Su, H., Xiang, L., Yao, Q., & Hu, A. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110967 · Cited by 90
Self-citationQiu, C., Zhang, X., Tong, X., Guan, N., Yi, X., Yang, K., Zhu, J., & Yu, A. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.02.005 · Cited by 85
Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey
Zhang, J., Liu, L., Silvén, O., Pietikäinen, M., & Hu, D. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3529038 · Cited by 84
Metadata
- Authors
- Li, X., Yang, X., Ma, Z., & Xue, J.-H.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- University of Glasgow (GB)
- University College London (GB)
- Corresponding author
- Ma, Zhanyu
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 138
- Pages
- 109381
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W4287180559
- Cited by
- 243
- References
- 134
- Citations by year
- 2026: 55 · 2025: 90 · 2024: 77 · 2023: 20
- FWCI
- 22.82
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Pattern Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China
- Beijing Natural Science Foundation
- Royal Society
- 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 › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- few-shot image classification, deep metric learning, prototype learning, task-specific features, graph neural networks, convolutional neural networks, similarity learning
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