Cited by 271Domain Adaptation and Few-Shot LearningAdvanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

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Deep metric learning for few-shot image classification: A Review of recent developments

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Li, Xiaoxu, Yang, Xiaochen, Ma, Zhanyu, Xue, Jing-Hao·2023·Pattern Recognition

DOI 10.1016/j.patcog.2023.109381

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Abstract

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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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
A survey on transfer learning
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 8,890

Show all 134 references

Research works printed but not linked: 3

Santoro, 2016, One-shot learning with memory-augmented neural networks. arxiv preprint, arXiv preprint arXiv:1605.06065 · arXiv
Lu, 2020, Learning from very few samples: a survey, arXiv preprint arXiv:2009.02653 · arXiv
Goldblum, 2020, Adversarially robust few-shot learning: ameta-learning approach, Adv. Neural Inf. Process. Syst., 33, 17886

Items 201–271 of 271

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Li, T., & Cao, W. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8435-0_12
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Hu, C., Song, J.-T., Chen, J., Wang, R., & Wu, X.-J. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.04.003
Surface Electromyography Relation Network
Liu, Y., & Lai, K. W. C. (2024) · IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) · doi:10.1109/arso60199.2024.10557822
Heterogeneous Image Steganalysis Based on Forensics Assistance
Huang, S., Zhang, M., Zhang, X., Kong, Y., Di, F., & Ke, Y. (2024) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce62493.2024.10653356
Adversarial Robustness for Deep Metric Learning
Paket, E., & Baytaş, İ. M. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647877
An Incremental Entity Resolution Approach based on Deep Metric LearningPreprint
Xu, Y., Li, C., Li, P., Duan, X., Wang, Y., Qi, W., Guo, Y., Li, Y., Shi, X., & Zhang, S. (2024) · Research Square · doi:10.21203/rs.3.rs-4982747/v1
Task-Aware Local Descriptors Reconstruction Network for Few-Shot Find-Grained Image Classification
Tan, J., Ding, X., & Yu, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78122-3_22
Metric Learning Assisted Aging Optical Transmitter Identification Based on the Constellation Diagram
Lv, Q., Wang, H., Zhu, P., & Ji, Y. (2024) · Asia Communications and Photonics Conference · doi:10.1109/acp/ipoc63121.2024.10809700
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Kim, H., Choi, C., Kampffmeyer, M., Berge, T., Parviainen, P., & Malde, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91585-7_26
WITHDRAWN: A few-shot classification framework based on multimodal representation fusion of hybrid pre-trained models
Liu, W., Zou, J., Zhang, S., Zhou, L. M., & Liu, H. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103382
Retracted
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Tsai, Y., Wu, Z.-R., & Liu, J. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.066509
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Li, C., Huang, W., Chen, Y., Chen, T., Yang, Z., & Long, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131155
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Jamili, L. K. (2025) · International Journal of Environmental Sciences · doi:10.64252/a24f9536
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Hafner, M., Radovic, A., Langer, M., Findenig, S., & Uhl, A. (2025) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf63717.2025.11113426
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Zhao, J., Cao, Z., Wang, H., Wang, X., & Chen, Y. (2025) · IET Image Processing · doi:10.1049/ipr2.70189
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Zhang, Z., Pan, Z., Yu, Y., & Liu, L. (2025) · Kalpa publications in computing · doi:10.29007/5xfx
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Jerusha, Y. A., Ibrahim, S., & Varadharajan, V. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.110518
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Fajri, R. M., Antony, F., & Rachmansyah (2025) · Journal of Embedded Systems Security and Intelligent Systems · doi:10.59562/jessi.v6i2.8319
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Wang, J., Zhou, F., Ren, J., Tan, L., Wang, J., Zhang, P., & Liao, S. (2025) · Computers · doi:10.3390/computers14110480
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Zhang, P., Tang, S., Luo, H., Wu, Y., Yang, H., & Wang, B. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3631099
SEmotion: Knowledge-Guided Sigmoid-Constrained Network for EEG-Based Emotion Recognition
Rao, W., Zhong, S.-H., Zhang, Z., & Liu, Y. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3625747
Embedded Operation Effort Estimation: A Machine Learning Performance-Efficiency Trade-off Analysis
Spotorno, E. N., Horstmann, L. P., Hoffmann, J. L. C., & Fröhlich, A. A. (2025) · Brazilian Symposium on Computing System Engineering (SBESC) · doi:10.1109/sbesc68008.2025.11288845
Research on Image Classification Method Under Ultra-Low Data Conditions Integrating Generative Sample Augmentation
Tao, Z. (2025) · International Conference on Algorithm, Image Processing and Machine Vision (AIPMV) · doi:10.1109/aipmv67185.2025.11290521
Tripletmaml: A Metric-Based Model-Agnostic Meta-Learning Algorithm for Few-Shot ClassificationPreprint
Gülcü, A., Kuş, Z., Özkan, İ. T. S., & Karakuş, O. F. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4576776
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Hou, H., & Wang, L. (2026) · Applied Sciences · doi:10.3390/app16062887
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Rysbayeva, K. (2023) · theses.fr (ABES) · doi:10.70675/b4cc60bcze30az4325z82d3zb1a11ed44601
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Chen, J., Xu, F., Lyu, X., Li, X., Chen, S., Huang, Y., & Tang, H. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104680
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Sun, S., Fan, P., Yu, X., Chen, P., Zhao, G., & Yang, C. (2026) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2026.102221
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Li, H., Shi, J., Chen, X., Jia, Q., Wang, R., Peng, Y., Gao, L., Chen, Q., An, H., Ouyang, R., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38470-6_41
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Chen, P., Qu, X., Guan, T., Pang, B., Wang, D., Guo, Z., & Xu, Y. (2026) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2026.e06643
Particleboard surface defect recognition using shared-weight feature learning
Chen, J., Zhou, H., Xia, H., Huang, Z., Fan, C., Liu, Y., & Yang, Y. (2026) · Wood Material Science and Engineering · doi:10.1080/17480272.2026.2746428
Visual-Semantic Interactive Alignment for Cross-Domain Few-Shot Learning
Lyu, X., Guo, W., Chen, S., Chen, J., & Li, X. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728765
Self-citation
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Li, Y., Liu, C., & Chong, W. O. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105417
Lightweight Continual Learning for Resource-Constrained Edge AI under Post-Deployment Drift
Kargar, A., Zorbas, D., O’Flynn, B., & Tedesco, S. A. (2026) · IEEE Smart World Congress (SWC) · doi:10.1109/swc69865.2026.11763902
Siamese Neural Network-Based Similarity Assessment of Peruvian Solar Farms: A Deep Learning Approach
Rojas-Arellanos, R., Arellanos-Tafur, E., Dipas, J. J. R., & Burga-Meza, M. (2026) · IEEE International Conference on Electronics, Electrical Engineering and Computing (INTERCON) · doi:10.1109/intercon71992.2026.11768207

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
ORCID: Li, Xiaoxu
Yang, Xiaochen
Ma, Zhanyu
Xue, Jing-Hao
Venue: Pattern Recognition
Publisher: Elsevier BV
Published: 2023-06
Volume: 138
Pages: 109381
ISSN: 0031-3203 (ISSN-L)
DOI: 10.1016/j.patcog.2023.109381
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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Deep metric learning for few-shot image classification: A Review of recent developments

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